Databases And Sql For Data Science With Python

  databases and sql for data science with python: SQL for Data Science Antonio Badia, 2020-11-09 This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
  databases and sql for data science with python: SQL for Data Scientists Renee M. P. Teate, 2021-08-17 Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on how to think about constructing your dataset. Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!
  databases and sql for data science with python: Using SQLite Jay Kreibich, 2010-08-17 Explains how to build database-backed applications for the Web, desktop, embedded systems, and operating systems using SQLite.
  databases and sql for data science with python: Python Data Persistence Malhar Lathkar, 2019-09-19 Designed to provide an insight into the SQL and MySQL database concepts using python DESCRIPTION Python is becoming increasingly popular among data scientists. However, analysis and visualization tools need to interact with the data stored in various formats such as relational and NOSQL databases. This book aims to make the reader proficient in interacting with databases such as MySQL, SQLite, MongoDB, and Cassandra. This book assumes that the reader has no prior knowledge of programming. Hence, basic programming concepts, key concepts of OOP, serialization and data persistence have been explained in such a way that it is easy to understand. NOSQL is an emerging technology. Using MongoDB and Cassandra, the two widely used NOSQL databases are explained in detail. The knowhow of handling databases using Python will certainly be helpful for readers pursuing a career in Data Science. KEY FEATURES A practical approach Ample code examples A Quick Start Guide to Python for beginners WHAT WILL YOU LEARN Python basics and programming fundamentals Serialization libraries pickle, CSV, JSON, and XML DB-AP and, SQLAlchemy Python with Excel documents Python with MongoDB and Cassandra WHO THIS BOOK IS FOR Students and professionals who want to become proficient at database tools for a successful career in data science. Table of Contents 1. Ê Ê Getting Started 2. Ê Ê Program Flow Control 3. Ê Ê Structured Python 4. Ê Ê Python Ð OOP 5. Ê Ê File IO 6. Ê Ê Object Serialization 7. Ê Ê RDBMS Concepts 8. Ê Ê Python DB-API 9. Ê Ê Python Ð SQLAlchemy 10. Ê Python and Excel 11. Ê Python Ð PyMongo 12. Ê Python Ð Cassandra
  databases and sql for data science with python: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
  databases and sql for data science with python: Sams Teach Yourself SQL in 10 Minutes Ben Forta, 2004 With this updated text, readers can learn the fundamentals of SQL quickly through the use of numerous examples depicting all the major components of SQL.
  databases and sql for data science with python: Practical SQL, 2nd Edition Anthony DeBarros, 2022-01-25 Analyze data like a pro, even if you’re a beginner. Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. Anthony DeBarros, a journalist and data analyst, focuses on using SQL to find the story within your data. The examples and code use the open-source database PostgreSQL and its companion pgAdmin interface, and the concepts you learn will apply to most database management systems, including MySQL, Oracle, SQLite, and others.* You’ll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from real-world datasets such as US Census demographics, New York City taxi rides, and earthquakes from US Geological Survey. Each chapter includes exercises and examples that teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently. You’ll learn how to: Create databases and related tables using your own data Aggregate, sort, and filter data to find patterns Use functions for basic math and advanced statistical operations Identify errors in data and clean them up Analyze spatial data with a geographic information system (PostGIS) Create advanced queries and automate tasks This updated second edition has been thoroughly revised to reflect the latest in SQL features, including additional advanced query techniques for wrangling data. This edition also has two new chapters: an expanded set of instructions on for setting up your system plus a chapter on using PostgreSQL with the popular JSON data interchange format. Learning SQL doesn’t have to be dry and complicated. Practical SQL delivers clear examples with an easy-to-follow approach to teach you the tools you need to build and manage your own databases. * Microsoft SQL Server employs a variant of the language called T-SQL, which is not covered by Practical SQL.
  databases and sql for data science with python: SQL for Data Analysis Cathy Tanimura, 2021-09-09 With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference. Learn the key steps for preparing your data for analysis Perform time series analysis using SQL's date and time manipulations Use cohort analysis to investigate how groups change over time Use SQL's powerful functions and operators for text analysis Detect outliers in your data and replace them with alternate values Establish causality using experiment analysis, also known as A/B testing
  databases and sql for data science with python: Data Modeling Essentials Graeme Simsion, Graham Witt, 2004-12-03 Data Modeling Essentials, Third Edition, covers the basics of data modeling while focusing on developing a facility in techniques, rather than a simple familiarization with the rules. In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice. This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises. This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective. - Thorough coverage of the fundamentals and relevant theory - Recognition and support for the creative side of the process - Expanded coverage of applied data modeling includes new chapters on logical and physical database design - New material describing a powerful technique for model verification - Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict
  databases and sql for data science with python: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course.
  databases and sql for data science with python: Sams Teach Yourself SQL in 24 Hours Ryan Stephens, Ron Plew, Arie D. Jones, 2008-05-30 In just 24 lessons of one hour or less, you will learn professional techniques to design and build efficient databases and query them to extract useful information. Using a straightforward, step-by-step approach, each lesson builds on the previous one, allowing you to learn the essentials of ANSI SQL from the ground up. Example code demonstrates the authors’ professional techniques, while exercises written for MySQL offer the reader hands-on learning with an open-source database. Included are advanced techniques for using views, managing transactions, database administration, and extending SQL. Step-by-step instructions carefully walk you through the most common SQL tasks. Q&As, Quizzes, and Exercises at the end of each chapter help you test your knowledge. Notes and Tips point out shortcuts and solutions. New terms are clearly defined and explained. Learn how to... Use SQL-2003, the latest standard for the Structured Query Language Design and deploy efficient, secure databases Build advanced queries for information retrieval Sort, group, and summarize information for best presentation Tune databases and queries for maximum performance Understand database administration and security techniques For more than ten years the authors have studied, applied, and documented the SQL standard and its application to critical database systems. Ryan Stephens and Ron Plew are entrepreneurs, speakers, and cofounders of Perpetual Technologies, Inc. (PTI), a fast-growing IT management and consulting firm which specializes in database technologies. They taught database courses for Indiana University–Purdue University in Indianapolis for five years and have authored more than a dozen books on Oracle, SQL, database design, and the high availability of critical systems. Arie D. Jones is Senior SQL Server database administrator and analyst for PTI. He is a regular speaker at technical events and has authored several books and articles. Category: Database Covers: ANSI SQL User Level: Beginning–Intermediate Register your book at informit.com/title/9780672330186 for convenient access to updates and corrections as they become available.
  databases and sql for data science with python: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.
  databases and sql for data science with python: Learning SQL Alan Beaulieu, 2009-04-11 Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work.
  databases and sql for data science with python: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
  databases and sql for data science with python: Introducing Data Science Davy Cielen, Arno Meysman, 2016-05-02 Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user
  databases and sql for data science with python: Python for Data Science Yuli Vasiliev, 2022-08-02 A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples. Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
  databases and sql for data science with python: Essential SQLAlchemy Rick Copeland, 2008-06-05 Essential SQLAlchemy introduces a high-level open-source code library that makes it easier for Python programmers to access relational databases such as Oracle, DB2, MySQL, PostgreSQL, and SQLite. SQLAlchemy has become increasingly popular since its release, but it still lacks good offline documentation. This practical book fills the gap, and because a developer wrote it, you get an objective look at SQLAlchemy's tools rather than an advocate's description of all the cool features. SQLAlchemy includes both a database server-independent SQL expression language and an object-relational mapper (ORM) that lets you map plain old Python objects (POPOs) to database tables without substantially changing your existing Python code. Essential SQLAlchemy demonstrates how to use the library to create a simple database application, walks you through simple queries, and explains how to use SQLAlchemy to connect to multiple databases simultaneously with the same Metadata. You also learn how to: Create custom types to be used in your schema, and when it's useful to use custom rather than built-in types Run queries, updates, and deletes with SQLAlchemy's SQL expression language Build an object mapper with SQLAlchemy, and understand the differences between this and active record patterns used in other ORMs Create objects, save them to a session, and flush them to the database Use SQLAlchemy to model object oriented inheritance Provide a declarative, active record pattern for use with SQLAlchemy using the Elixir extension Use the SQLSoup extension to provide an automatic metadata and object model based on database reflection In addition, you'll learn how and when to use other extensions to SQLAlchemy, including AssociationProxy, OrderingList, and more. Essential SQLAlchemy is the much-needed guide for every Python developer using this code library. Instead of a feature-by-feature documentation, this book takes an essentials approach that gives you exactly what you need to become productive with SQLAlchemy right away.
  databases and sql for data science with python: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
  databases and sql for data science with python: Python for Data Science For Dummies John Paul Mueller, Luca Massaron, 2015-06-23 Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
  databases and sql for data science with python: Hands-On Data Science and Python Machine Learning Frank Kane, 2017-07-31 This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
  databases and sql for data science with python: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
  databases and sql for data science with python: Data Science with SQL Server Quick Start Guide Dejan Sarka, 2018-08-31 Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.
  databases and sql for data science with python: SQL for Data Analytics Upom Malik, Matt Goldwasser, Benjamin Johnston, 2019-08-22 Take your first steps to become a fully qualified data analyst by learning how to explore large relational datasets. Key Features Explore a variety of statistical techniques to analyze your data Integrate your SQL pipelines with other analytics technologies Perform advanced analytics such as geospatial and text analysis Book Description Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain business insights from data, this book is for you. SQL for Data Analytics covers everything you need progress from simply knowing basic SQL to telling stories and identifying trends in data. You'll be able to start exploring your data by identifying patterns and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll understand how to become productive with SQL with the help of profiling and automation to gain insights faster. By the end of the book, you'll able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of analytics professional. What you will learn Use SQL to summarize and identify patterns in data Apply special SQL clauses and functions to generate descriptive statistics Use SQL queries and subqueries to prepare data for analysis Perform advanced statistical calculations using the window function Analyze special data types in SQL, including geospatial data and time data Import and export data using a text file and PostgreSQL Debug queries that won't run Optimize queries to improve their performance for faster results Who this book is for If you're a database engineer looking to transition into analytics, or a backend engineer who wants to develop a deeper understanding of production data, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL. Knowledge of basic SQL and database concepts will aid in understanding the concepts covered in this book.
  databases and sql for data science with python: SQL and Python Programming Bryan Johnson, 2019-09-21 ★★★Buy the paperback version of this book and get the kindle version FREE★★★ Within this book, you will find 2 Books IN 1.... SQL Programming: The Ultimate Step By Step Guide to Learning SQL for Total Beginners, as well as Python Programming: A Pragmatic Approach To Programming Python for Total Beginners. Between both books, you will gain an incredible insight into the world of both the SQL and Python programming languages, and you will really be set up for success with learning to code! Below are the specifics of what each book contains, starting with Python, and then SQL: Are you interested in learning how to write your own codes? Have you always been interested in seeing how coding works, and learning more about how certain programs work? Do technology and computers interest you but you just don't know where to start? If this sounds like something that interests you, then the Python coding language may be the right option for you. The Python language is one of the preferred coding languages for you to learn how to use. It has a lot of power, an extensive library, the capabilities to be expanded to work with other programs and more, and a great community to help answer your questions and guide you along your journey to learning coding. As a coding language designed for everyone, even beginners, there is just so much that you are able to do when working with the Python language. As a business, it is likely that you will need to hold onto a lot of data. Some of this data is going to be about your customers, like their name, address, credit card information, and more. And some of that information is going to be about your products and services. You want to make sure that any and all information that your business has will stay organized, secure, and easy to sort through when it is needed. This is where the SQL language is going to come into play. It can bring out the queries that you need in no time and can help you to keep the information organized so that you can find it when it is needed. Some of the different topics that we are going to explore when it comes to using the SQL database includes: ★The basics of SQL. ★Some of the commands that you should use with this language. ★Understanding some of the different data types that can show up. ★How to manage the object in SQL. ★Doing your own searches and seeing how the results come up.; ★Relational database concepts. ★How to define some of the data that you need in SQL. ★Working with queries, views, and indexing. ★Database security ★How to use all of this in real-world situations. There is so much that you are able to enjoy when it comes to working with the SQL database. You will be able to finally keep all of your customer and product information stored properly, and you and the customer can pull it up as soon as you need. When you are ready to get started with the SQL database, make sure to read this book to help you get started. Between both books, you have everything you need to get started with programming SQL and Python at a very high level. Scroll up to the top of this page and click the Buy Now Button and begin writing your own codes in SQL and Python today!
  databases and sql for data science with python: The The SQL Workshop Frank Solomon, Prashanth Jayaram, Awni Al Saqqa, 2019-12-30 Get to grips with SQL fundamentals and learn how to efficiently create, read and update information stored in databases Key FeaturesUnderstand the features and syntax of SQL and use them to query databasesLearn how to create databases and tables and manipulate the data within themCreate advanced queries and apply them on realistic databases with hands-on activitiesBook Description Many software applications are backed by powerful relational database systems, meaning that the skills to be able to maintain a SQL database and reliably retrieve data are in high demand. With its simple syntax and effective data manipulation capabilities, SQL enables you to manage relational databases with ease. The SQL Workshop will help you progress from basic to advanced-level SQL queries in order to create and manage databases successfully. This Workshop begins with an introduction to basic CRUD commands and gives you an overview of the different data types in SQL. You'll use commands for narrowing down the search results within a database and learn about data retrieval from single and multiple tables in a single query. As you advance, you'll use aggregate functions to perform calculations on a set of values, and implement process automation using stored procedures, functions, and triggers. Finally, you'll secure your database against potential threats and use access control to keep your data safe. Throughout this Workshop, you'll use your skills on a realistic database for an online shop, preparing you for solving data problems in the real world. By the end of this book, you'll have built the knowledge, skills and confidence to creatively solve real-world data problems with SQL. What you will learnCreate databases and insert data into themUse SQL queries to create, read, update, and delete dataMaintain data integrity and consistency through normalizationCustomize your basic SQL queries to get the desired outputRefine your database search using the WHERE and HAVING clausesUse joins to fetch data from multiple tables and create custom reportsImprove web application performance by automating processesSecure a database with GRANT and REVOKE privilegesWho this book is for This Workshop is suitable for anyone who wants to learn how to use SQL to work with databases. No prior SQL or database experience is necessary. Whether you're an aspiring software developer, database engineer, data scientist, or systems administrator, this Workshop will quickly get you up and running.
  databases and sql for data science with python: Practical Data Science with Python Nathan George, 2021-09-30 Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A getting started with Python section has been included to get complete novices up to speed.
  databases and sql for data science with python: Database Development For Dummies Allen G. Taylor, 2011-05-09 Powerful, low-cost database development tools make it possible for virtually anybody to create their own database—and this hands-on guide makes it fun and easy Databases permeate every nook and cranny of our highly connected, information-intensive world, from ATMs to personal finance, online shopping, and networked information management. Databases have become so integral to the business environment that, nowadays, it's next to impossible to stay competitive without the assistance of some sort of database technology—no matter what type or size of business you run. But developing your own database can be tricky. Whether you want to keep records for a small business or run a large e-commerce website, developing the right database system can be a major challenge. Which is where Database Development For Dummies comes in. From data modeling methods and development tools to Internet accessibility and security, this comprehensive guide shows you, step-by-step and with illustrations, everything you need to know about building a custom system from the ground up. You'll learn to: Model data accurately Design a reliable functional database Deliver robust relational databases on time and on budget Build a user-friendly database application Put your database on the Web The book acquaints you with the most popular data modeling methods and shows you how to systematically design and develop a system incorporating a database and one or more applications that operate on it. Here's more of what you'll discover in the book: Database architecture and how it has evolved How database technology affects everyday life A structured approach to database development How to create an appropriate data model How to develop a reliable relational design The complexities that you're likely to encounter in designing a database and how to simplify them Implementing your design using Microsoft Access 2000, SQL Server, and other powerful database development tools Database security Ten rules to know when creating a database Another ten rules to know when creating a database application If you need a database tailored to you and your company's current and future data storage and management needs, this is the book for you. Get Database Development For Dummies and discover what it takes to design, develop, and implement a sophisticated database system.
  databases and sql for data science with python: Murach's Python for Data Analysis Scott McCoy, 2021-08 Data is collected everywhere these days, in massive quantities. But data alone does not do you much good. That is why data analysis -- making sense of the data -- has become a must-have skill in the fields of business, science, and social science. But it is a tough skill to acquire. The concepts are challenging, and too many books and online tutorials treat only parts of the total skillset needed. Now, though, this book draws all the essential skills together and presents them in a clear and example-packed way. So you will soon be applying your programming skills to complex data analysis problems, more easily than you ever thought possible. In terms of content, this book gets you started the right way by using Pandas for data analysis and Seaborn for data visualisation, with JupyterLab as your IDE. Then, it helps you master descriptive analysis by teaching you how to get, clean, prepare, and analyse data, including time-series data. Next, it gets you started with predictive analysis by showing you how to use linear regression models to predict unknown and future values. And to tie everything together, it gives you 4 real-world case studies that show you how to apply your new skills to political, environmental, social, and sports analysis. At the end, you will have a solid set of the professional skills that can lead to all sorts of new career opportunities. Sound too good to be true? Download a sample chapter for free from the Murach website and see for yourself how this book can turn you into the data analyst that companies are looking for.
  databases and sql for data science with python: Advanced Data Analytics Using Python Sayan Mukhopadhyay, 2018-03-29 Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.
  databases and sql for data science with python: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data
  databases and sql for data science with python: A Tour of Data Science Nailong Zhang, 2020-11-11 A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
  databases and sql for data science with python: The Art of SQL Stephane Faroult, Peter Robson, 2006-03-10 For all the buzz about trendy IT techniques, data processing is still at the core of our systems, especially now that enterprises all over the world are confronted with exploding volumes of data. Database performance has become a major headache, and most IT departments believe that developers should provide simple SQL code to solve immediate problems and let DBAs tune any bad SQL later. In The Art of SQL, author and SQL expert Stephane Faroult argues that this safe approach only leads to disaster. His insightful book, named after Art of War by Sun Tzu, contends that writing quick inefficient code is sweeping the dirt under the rug. SQL code may run for 5 to 10 years, surviving several major releases of the database management system and on several generations of hardware. The code must be fast and sound from the start, and that requires a firm understanding of SQL and relational theory. The Art of SQL offers best practices that teach experienced SQL users to focus on strategy rather than specifics. Faroult's approach takes a page from Sun Tzu's classic treatise by viewing database design as a military campaign. You need knowledge, skills, and talent. Talent can't be taught, but every strategist from Sun Tzu to modern-day generals believed that it can be nurtured through the experience of others. They passed on their experience acquired in the field through basic principles that served as guiding stars amid the sound and fury of battle. This is what Faroult does with SQL. Like a successful battle plan, good architectural choices are based on contingencies. What if the volume of this or that table increases unexpectedly? What if, following a merger, the number of users doubles? What if you want to keep several years of data online? Faroult's way of looking at SQL performance may be unconventional and unique, but he's deadly serious about writing good SQL and using SQL well. The Art of SQL is not a cookbook, listing problems and giving recipes. The aim is to get you-and your manager-to raise good questions.
  databases and sql for data science with python: Python Essential Reference David M Beazley, 2009-06-29 Python Essential Reference is the definitive reference guide to the Python programming language — the one authoritative handbook that reliably untangles and explains both the core Python language and the most essential parts of the Python library. Designed for the professional programmer, the book is concise, to the point, and highly accessible. It also includes detailed information on the Python library and many advanced subjects that is not available in either the official Python documentation or any other single reference source. Thoroughly updated to reflect the significant new programming language features and library modules that have been introduced in Python 2.6 and Python 3, the fourth edition of Python Essential Reference is the definitive guide for programmers who need to modernize existing Python code or who are planning an eventual migration to Python 3. Programmers starting a new Python project will find detailed coverage of contemporary Python programming idioms. This fourth edition of Python Essential Reference features numerous improvements, additions, and updates: Coverage of new language features, libraries, and modules Practical coverage of Python's more advanced features including generators, coroutines, closures, metaclasses, and decorators Expanded coverage of library modules related to concurrent programming including threads, subprocesses, and the new multiprocessing module Up-to-the-minute coverage of how to use Python 2.6’s forward compatibility mode to evaluate code for Python 3 compatibility Improved organization for even faster answers and better usability Updates to reflect modern Python programming style and idioms Updated and improved example code Deep coverage of low-level system and networking library modules — including options not covered in the standard documentation
  databases and sql for data science with python: SQL Queries for Mere Mortals John L. Viescas, Michael James Hernandez, 2014 The #1 Easy, Common-Sense Guide to SQL Queries--Updated for Today's Databases, Standards, and Challenges SQL Queries for Mere Mortals ® has earned worldwide praise as the clearest, simplest tutorial on writing effective SQL queries. The authors have updated this hands-on classic to reflect new SQL standards and database applications and teach valuable new techniques. Step by step, John L. Viescas and Michael J. Hernandez guide you through creating reliable queries for virtually any modern SQL-based database. They demystify all aspects of SQL query writing, from simple data selection and filtering to joining multiple tables and modifying sets of data. Three brand-new chapters teach you how to solve a wide range of challenging SQL problems. You'll learn how to write queries that apply multiple complex conditions on one table, perform sophisticated logical evaluations, and think outside the box using unlinked tables. Coverage includes -- Getting started: understanding what relational databases are, and ensuring that your database structures are sound -- SQL basics: using SELECT statements, creating expressions, sorting information with ORDER BY, and filtering data using WHERE -- Summarizing and grouping data with GROUP BY and HAVING clauses -- Drawing data from multiple tables: using INNER JOIN, OUTER JOIN, and UNION operators, and working with subqueries -- Modifying data sets with UPDATE, INSERT, and DELETE statements Advanced queries: complex NOT and AND, conditions, if-then-else using CASE, unlinked tables, driver tables, and more Practice all you want with downloadable sample databases for today's versions of Microsoft Office Access, Microsoft SQL Server, and the open source MySQL database. Whether you're a DBA, developer, user, or student, there's no better way to master SQL. informit.com/aw forMereMortals.com
  databases and sql for data science with python: Data Science with SQL Server Quick Start Guide Dejan Sarka, 2018-08-31 Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.
  databases and sql for data science with python: Pandas Cookbook Theodore Petrou, 2017-10-23 Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data.
  databases and sql for data science with python: Learn SQL Database Programming Josephine Bush, 2020-05-29 Learn everything you need to know to build efficient SQL queries using this easy-to-follow beginner’s guide Key FeaturesExplore all SQL statements in depth using a variety of examplesGet to grips with database querying, data aggregate, manipulation, and much moreUnderstand how to explore and process data of varying complexity to tell a storyBook Description SQL is a powerful querying language that's used to store, manipulate, and retrieve data, and it is one of the most popular languages used by developers to query and analyze data efficiently. If you're looking for a comprehensive introduction to SQL, Learn SQL Database Programming will help you to get up to speed with using SQL to streamline your work in no time. Starting with an overview of relational database management systems, this book will show you how to set up and use MySQL Workbench and design a database using practical examples. You'll also discover how to query and manipulate data with SQL programming using MySQL Workbench. As you advance, you’ll create a database, query single and multiple tables, and modify data using SQL querying. This SQL book covers advanced SQL techniques, including aggregate functions, flow control statements, error handling, and subqueries, and helps you process your data to present your findings. Finally, you’ll implement best practices for writing SQL and designing indexes and tables. By the end of this SQL programming book, you’ll have gained the confidence to use SQL queries to retrieve and manipulate data. What you will learnInstall, configure, and use MySQL Workbench to restore a databaseExplore different data types such as string, numeric, and date and timeQuery a single table using the basic SQL SELECT statement and the FROM, WHERE, and ORDER BY clausesQuery multiple tables by understanding various types of table relationshipsModify data in tables using the INSERT, UPDATE, and DELETE statementsUse aggregate functions to group and summarize dataDetect bad data, duplicates, and irrelevant values while processing dataWho this book is for This book is for business analysts, SQL developers, database administrators, and students learning SQL. If you want to learn how to query and manipulate SQL data for database administration tasks or simply extract and organize relevant data for analysis, you’ll find this book useful. No prior SQL experience is required.
  databases and sql for data science with python: Introduction to Machine Learning with Python Andreas C. Müller, Sarah Guido, 2016-09-26 Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
  databases and sql for data science with python: Introduction to Computing & Problem Solving With PYTHON Jeeva Jose, P.Sojan Lal, 2016-08-01 This book 'Introduction to Computing and Problem Solving with Python' will help every student,teacher and researcher to understand the computing basics and advanced PythonProgramming language. The Python programming topics include the reserved keywords,identifiers, variables, operators, data types and their operations, flowcontrol techniques which include decision making and looping, modules, filesand exception handling techniques. Advanced topics like Python regularexpressions, Database Programming and Object Oriented Programming concepts arealso covered in detail. All chapters have worked out programs, illustrations,review and frequently asked interview questions. The simple style of presentationmakes this a friend for self-learners. More than 300 solved lab exercisesavailable in this book is tested in Python 3.4.3 version for Windows. The book covers syllabus for more than 35 International Universities and45 Indian universities like Dr. APJ Abdul Kalam Technological University,Christ University, Savitribai Phule Pune University, University of Delhi, University of Calicut, Mahatma Gandhi University, University of Mumbai, AICTE, CBSE, MIT, University of Virginia, University of Chicago, University of Toronto, Technical University of Denmark etc.
  databases and sql for data science with python: Step By Step Database Programming using Python GUI & MySQL Hamzan Wadi, This book provides a practical explanation of database programming using Python GUI & MySQL. The discussion in this book is presented in step by step so that it will help readers understand each material and also will make it easier for the readers to follow all of the instructions. This book is very suitable for students, programmers, and anyone who want to learn database programming using Python GUI & MySQL from scratch. This book is divided into two parts: The first part of this book will discuss about the fundamentals of database programming using Python GUI & MySQL. This part will discuss in detail about how to setup your working environment and how to understand GUI programming using Python. This part will also discuss in detail about how to start your database programming using Python GUI & MySQL. This part will discuss in detail about the basic of database programming using Python GUI & MySQL. The second part of this book will discuss about how to build database application using Python GUI & MySQL. This part will discuss in detail about how to build Multiple Document Interface (MDI) database application through real project-based example. This part will discuss in detail about how to design and create database for Library Management System application, and how to create all forms for the application. The final objective of this book is that the readers are able to create real database application using Python GUI & MySQL. Here are the materials that you will learn in this book. PART I: THE FUNDAMENTAL OF DATABASE PROGRAMMING USING PYTHON GUI & MySQL CHAPTER 1: The discussion in this chapter will guide you in preparing what software are needed to start your database programming using Python GUI. This chapter will guide you to install all software including Python, MySQL, and Qt Designer. In addition, this chapter also will discuss about how to understand and use Qt Designer for user interface design, and how to create a GUI application using Python and Qt Designer. CHAPTER 2: The discussion in this chapter will guide you to start your database programming using Python GUI & MySQL. This chapter will discuss in detail about the basic of database programming using Python GUI & MySQL. The discussion in this chapter will talk about how to create and drop database, how to create and drop table, how to insert data into table, how to display data from table, how to update data in table, and how to delete data in table. All discussions in this chapter will give you deep understanding of database programming using Python GUI & MySQL. PART II: BUILDING DATABASE APPLICATION USING PYTHON GUI & MySQL, CASE STUDY: LIBRARY MANAGEMENT SYSTEM APPLICATION CHAPTER 3: The discussion in this chapter will guide you to design and create database for library management system application. This is the first step that must be taken to create database application using Python GUI & MySQL. This chapter will discuss in detail about how to design the Entity Relationship Diagram (ERD) for library management system application. The discussion in this chapter will also talk about how to create database and its tables based on the ERD design using MySQL server. CHAPTER 4: The discussion in this chapter will guide you to create main form and login form for the application. This chapter will discuss in detail about how to create these two forms. These forms are the first two forms that we will create in building library management system application. This chapter will also discuss about how to run the application. CHAPTER 5: The discussion in this chapter will guide you to create user accounts form and members form for Library Management System application. This chapter will discuss in detail about how to create these two forms. This chapter will also discuss about how to add these two forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use the forms to manage user accounts and members data of Library Management System application. CHAPTER 6: The discussion in this chapter will guide you to create authors form, genres form, and books form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add books form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage authors, genres, and books data in Library Management System application. CHAPTER 7: The discussion in this chapter will guide you to create member search form, book search form, and loan transaction form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add loan transaction form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage loan transactions in Library Management System application. CHAPTER 8: The discussion in this chapter will guide you to create members statistic form, books statistic form, and loan statistic form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add all of the forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use all of the forms to display the statistics in the library.
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Share practical suggestions for making effective use of the Python API for interfacing with SQL databases across several use cases •Agenda: oReading data oWriting data oExecuting SQL 3

UNIT 16 DATA ACCESS USING PYTHON Using Python Data …
You will learn to connect data through SQL as well as CSV files with the help of real dataset (PIMA Indian Dataset), used most commonly by researchers and academia in data analysis.

Python programming | databasing - DTU
Python can work with relational database management systems, such as MySQL, PostgreSQL (both client-server-based) and SQLite (lightweight) The databases can be accessed by:

Interface python with SQL Database - mykvs.in
The DB API provides a minimal standard for working with databases using Python structures and syntax wherever possible. This API includes the following − Importing the API module. …

PyTond: Efficient Python Data Science on the Shoulders of …
generate better SQL code that improves the query processing by the underlying database engine. Our evaluation results show promising performance improvement compared to Python and …

INTRODUCTION TO DATA SCIENCE LECTURE NOTES UNIT - 1 …
Using Data Science techniques/tools to improve processes To become a data scientist, you have to be an expert in R, MatLab, SQL, Python, and other complementary technologies. 6. Data …

Database Programming in python - Study Glance
Python Database Application Programmer’s Interface (DB-API): • Python DB-API is independent of any database engine, which enables you to write Python scripts to access any database …

SQL for Data Science - Emergetech
SQL is one of the most requested skills in Data Science. Let’s find out how it can be used in Data Processing and Machine Learning. Today I’d like to talk about why SQL plays such an …

Computational Structures in Data Science Lecturer Databases …
sqlite3 is a Python module which connects to a very popular database! This is the first time you write code that really interacts with data on your computer! There's some "boilerplate" setup …

An Introduction to Python for Data Science - Wharton Global …
At the end of the course, students will have a solid grasp of Python programming basics, and have been exposed to the entire data science workflow, starting from interacting with SQL …

Python Database Programming For Introduction to …
Python provides an API for accessing SQL database. You can write Python programs to access a relational database system such as MySQL, Oracle, DB2, Sybase, or SQLite. Since SQLite …

Introduction to databases in practice with PostgreSQL and …
In this lab session, we will practice manipulating real-world datasets with PostgreSQL, a popular and powerful open-source relational database management system. You will interact with this …

SQL for Data Scientists: Designing SQL Tutorials for Scalable …
In this demo, we show how we extended the Grok platform to teach SQL for Data Scientists with comprehensive online learning. Grok supports a rich user interface with interactive exam-ples …

New syllabus 2022-23 Interface python with SQL Database
python with SQL Database can be understood with the help of this diagram. Using SQL in any of the dbms ,databases and table can be created and data can be accessed, updated and …

Python in Field of Data Science: A Review - ijcaonline.org
SQL can be used by data scientist for basic filtering like generate query from a query, handle dates, text mining, find the medians, load data into your database and generate

Python - Using Database and SQL - IJSR
In this paper we reviews available resources and basic information about database modules that are known to be used with Python and also how to make the connection between python and …

CS 488/588 Applied Data Science with Python - University of …
Lecture 11: Databases and SQL − Intro to SQLite, creating databases, querying, sorting, filtering, joining tables, conditional expressions, grouping data, subqueries

Python Programming and SQL - 103.203.175.90:81
A solid grasp of SQL is crucial in today's data-driven world. Data-driven decision-making necessitates database proficiency and the capacity to manage massive datasets efficiently. …

A Beginner’s Guide to SQL, Python, and Machine Learning
In this paper, we break down three of the most prevalent technologies that are transforming how we understand and use data: SQL, Python, and machine learning. The first two are …

SQL Cheat Sheet: Accessing Databases using Python - Nelvin …
library in Python for executing SQL queries and retrieving the results into a DataFrame from an SQL database. It's a convenient way to integrate SQL database interactions into your data …

Using the Dataiku DSS Python API for Interfacing with SQL …
Share practical suggestions for making effective use of the Python API for interfacing with SQL databases across several use cases •Agenda: oReading data oWriting data oExecuting SQL 3

UNIT 16 DATA ACCESS USING PYTHON Using Python Data …
You will learn to connect data through SQL as well as CSV files with the help of real dataset (PIMA Indian Dataset), used most commonly by researchers and academia in data analysis.

Python programming | databasing - DTU
Python can work with relational database management systems, such as MySQL, PostgreSQL (both client-server-based) and SQLite (lightweight) The databases can be accessed by:

Interface python with SQL Database - mykvs.in
The DB API provides a minimal standard for working with databases using Python structures and syntax wherever possible. This API includes the following − Importing the API module. …

PyTond: Efficient Python Data Science on the Shoulders of …
generate better SQL code that improves the query processing by the underlying database engine. Our evaluation results show promising performance improvement compared to Python and …

INTRODUCTION TO DATA SCIENCE LECTURE NOTES UNIT
Using Data Science techniques/tools to improve processes To become a data scientist, you have to be an expert in R, MatLab, SQL, Python, and other complementary technologies. 6. Data …

Database Programming in python - Study Glance
Python Database Application Programmer’s Interface (DB-API): • Python DB-API is independent of any database engine, which enables you to write Python scripts to access any database …

SQL for Data Science - Emergetech
SQL is one of the most requested skills in Data Science. Let’s find out how it can be used in Data Processing and Machine Learning. Today I’d like to talk about why SQL plays such an …

Computational Structures in Data Science Lecturer …
sqlite3 is a Python module which connects to a very popular database! This is the first time you write code that really interacts with data on your computer! There's some "boilerplate" setup …

An Introduction to Python for Data Science - Wharton …
At the end of the course, students will have a solid grasp of Python programming basics, and have been exposed to the entire data science workflow, starting from interacting with SQL …

Python Database Programming For Introduction to …
Python provides an API for accessing SQL database. You can write Python programs to access a relational database system such as MySQL, Oracle, DB2, Sybase, or SQLite. Since SQLite …

Introduction to databases in practice with PostgreSQL and …
In this lab session, we will practice manipulating real-world datasets with PostgreSQL, a popular and powerful open-source relational database management system. You will interact with this …

SQL for Data Scientists: Designing SQL Tutorials for Scalable …
In this demo, we show how we extended the Grok platform to teach SQL for Data Scientists with comprehensive online learning. Grok supports a rich user interface with interactive exam-ples …

New syllabus 2022-23 Interface python with SQL Database
python with SQL Database can be understood with the help of this diagram. Using SQL in any of the dbms ,databases and table can be created and data can be accessed, updated and …

Python in Field of Data Science: A Review - ijcaonline.org
SQL can be used by data scientist for basic filtering like generate query from a query, handle dates, text mining, find the medians, load data into your database and generate

Python - Using Database and SQL - IJSR
In this paper we reviews available resources and basic information about database modules that are known to be used with Python and also how to make the connection between python and …

CS 488/588 Applied Data Science with Python - University of …
Lecture 11: Databases and SQL − Intro to SQLite, creating databases, querying, sorting, filtering, joining tables, conditional expressions, grouping data, subqueries