Advertisement
| dataquest vs datacamp vs 365 data science: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| dataquest vs datacamp vs 365 data science: The Art of Statistics David Spiegelhalter, 2019-09-03 In this important and comprehensive guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs. |
| dataquest vs datacamp vs 365 data science: Python for R Users Ajay Ohri, 2017-11-13 The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing. |
| dataquest vs datacamp vs 365 data science: Machine Learning Stephen Marsland, 2011-03-23 Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but |
| dataquest vs datacamp vs 365 data science: 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 |
| dataquest vs datacamp vs 365 data science: MOOCs Now Susan W. Alman, Jennifer Jumba, 2017-02-14 This book explores MOOCs (Massive Open Online Courses) from the perspective of veteran librarians, offering guidance to those considering offering MOOCs. It identifies pitfalls to avoid and outcomes to pursue with this trend in online learning, presents the pros and cons in considering a MOOC, and addresses various costs of offering a MOOC (personnel, licensing, software), the challenges of copyright and the administration required, and what the librarian's role should be. |
| dataquest vs datacamp vs 365 data science: Approaching (Almost) Any Machine Learning Problem Abhishek Thakur, 2020-07-04 This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub |
| dataquest vs datacamp vs 365 data science: Beyond the Basic Stuff with Python Al Sweigart, 2020-12-16 BRIDGE THE GAP BETWEEN NOVICE AND PROFESSIONAL You've completed a basic Python programming tutorial or finished Al Sweigart's bestseller, Automate the Boring Stuff with Python. What's the next step toward becoming a capable, confident software developer? Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language. You'll learn: Coding style, and how to use Python's Black auto-formatting tool for cleaner code Common sources of bugs, and how to detect them with static analyzers How to structure the files in your code projects with the Cookiecutter template tool Functional programming techniques like lambda and higher-order functions How to profile the speed of your code with Python's built-in timeit and cProfile modules The computer science behind Big-O algorithm analysis How to make your comments and docstrings informative, and how often to write them How to create classes in object-oriented programming, and why they're used to organize code Toward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself. Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly Pythonic Requirements: Covers Python 3.6 and higher |
| dataquest vs datacamp vs 365 data science: 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. |
| dataquest vs datacamp vs 365 data science: Learn Python 3 the Hard Way Zed A. Shaw, 2017-06-26 You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3 |
| dataquest vs datacamp vs 365 data science: Cracking Codes with Python Al Sweigart, 2018-01-23 Learn how to program in Python while making and breaking ciphers—algorithms used to create and send secret messages! After a crash course in Python programming basics, you’ll learn to make, test, and hack programs that encrypt text with classical ciphers like the transposition cipher and Vigenère cipher. You’ll begin with simple programs for the reverse and Caesar ciphers and then work your way up to public key cryptography, the type of encryption used to secure today’s online transactions, including digital signatures, email, and Bitcoin. Each program includes the full code and a line-by-line explanation of how things work. By the end of the book, you’ll have learned how to code in Python and you’ll have the clever programs to prove it! You’ll also learn how to: - Combine loops, variables, and flow control statements into real working programs - Use dictionary files to instantly detect whether decrypted messages are valid English or gibberish - Create test programs to make sure that your code encrypts and decrypts correctly - Code (and hack!) a working example of the affine cipher, which uses modular arithmetic to encrypt a message - Break ciphers with techniques such as brute-force and frequency analysis There’s no better way to learn to code than to play with real programs. Cracking Codes with Python makes the learning fun! |
| dataquest vs datacamp vs 365 data science: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| dataquest vs datacamp vs 365 data science: Graduate Admissions Essays, Fifth Edition Donald Asher, 2024-07-16 The fully updated fifth edition of the go-to guide for crafting winning essays for any type of graduate program or scholarship, including PhD, master's, MD, JD, Rhodes, and postdocs, with brand-new essays and the latest hot tips and secret techniques. Based on thousands of interviews with successful grad students and admissions officers, Graduate Admissions Essays deconstructs and demystifies the ever-challenging application process for getting into graduate and scholarship programs. The book presents: Sample essays in a comprehensive range of subjects, including some available from no other source: medical residencies, postdocs, elite fellowships, academic autobiographies, and more! The latest on AI, the GRE, and diversity and adversity essays. Detailed strategies that have proven successful for some of the most competitive graduate programs in the country (learn how to beat 1% admissions rates!). How to get strong letters of recommendation, how to get funding when they say they have no funding, and how to appeal for more financial aid. Brand-new sample supplemental application letters, letters to faculty mentors, and letters of continuing interest. Full of Dr. Donald Asher's expert advice, this is the perfect graduate application resource whether you're fresh out of college and eager to get directly into graduate school or decades into your career and looking for a change. |
| dataquest vs datacamp vs 365 data science: 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 |
| dataquest vs datacamp vs 365 data science: Mitochondrial Dysfunction and Nanotherapeutics Marcos Roberto de Oliveira, 2021-07-15 Mitochondrial Dysfunction and Nanotherapeutics: Aging, Diseases, and Nanotechnology-Related Strategies in Mitochondrial Medicine provides a comprehensive overview of mitochondrial dysfunction and current strategies for targeting the organelle. Based on the most current research, the editor lined up a team of worldwide experts to cover the most exciting research in the area, considering the impact through the human life span. This book is structured in two parts that provide a good balance of foundational and applied content. Part I deals with an overview of mitochondrial dysfunction and its role in the aging process, including metabolic diseases, neuro-affective and neurodegenerative disorders, sepsis, and toxicological aspects. Part II covers therapeutic substance delivery to mitochondria with a focus on cancer, neurodegenerative diseases, and increasing the bioavailability of natural compounds of interest. Several nanoscale strategies are described. Mitochondrial Dysfunction and Nanotherapeutics: Aging, Diseases, and Nanotechnology-Related Strategies in Mitochondrial Medicine is a complete resource for researchers in this exciting field. Its comprehensive coverage makes this book particularly interesting to bioscience researchers looking to understand the foundations of mitochondrial health throughout the human life span. Additionally, clinician researchers, medical doctors, nutritionists, pharmacologists, and sports scientists may be attracted to the detailed information on the organelle-targeted delivery strategies. - Contains detailed information on mitochondrial dysfunction - Reviews our current understanding of the role of mitochondria in aging - Includes coverage of specific conditions, including sports and affective disorders, among others - Discusses mitochondria targeted delivery of therapeutic compounds |
| dataquest vs datacamp vs 365 data science: Python Crash Course Eric Matthes, 2015-11-01 Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You’ll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you’ll put your new knowledge into practice with three substantial projects: a Space Invaders–inspired arcade game, data visualizations with Python’s super-handy libraries, and a simple web app you can deploy online. As you work through Python Crash Course you’ll learn how to: –Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal –Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses –Work with data to generate interactive visualizations –Create and customize Web apps and deploy them safely online –Deal with mistakes and errors so you can solve your own programming problems If you’ve been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code! Uses Python 2 and 3 |
| dataquest vs datacamp vs 365 data science: Data Science Workflow for Beginners Alejandro Garcia, This book brings to you a simple yet effective 40 to 60 mins introduction that will clear all your doubts about Data Sience and will answer some important questions like: What is data Science ? The book explores all the initial concepts a person might want to know about the data science workflow. There’s not coding, math or statistics required to successfully understand the goals and end results of this process. This book takes you on an exclusive tour of datasets and sites to download your first datasets. Then jumps into a comprehensive and easy-to-follow data science process letting you go through 3 data visualization projects. (Python Code Understanding is Recommended for the Data Visualization projects) - 40 to 60 mins reading time. - 3 Data Visualization projects. - 10 Datasets sources. - 26 Quality datasets for your first visualizations. - Get the code and reuse in your own projects. The ebook covers: - Intro to Data Science. - The Workflow of Data Science. - Data Science and Machine Learning. - Datasets to start right away. - Data Visualization Projects. (Python Code Understanding Recommended) |
| dataquest vs datacamp vs 365 data science: SQL Server Interview Questions and Answers Vinod Kumar, Pinal Dave, 2021-03 As representatives from the IT community, all of us have had our own experiences of attending interviews - clearing or close to clearing and sometimes with tons of questions and doubts failing miserably. These stories are in the most pleasant or not so pleasant memories of our mind and we will assure you this book will kindle those memories for sure. We have taken tons of interviews and most of the interviews are not revolving around how deep technical and internals you know about the subject - but it revolves around how good you are with the basics.To clear an interview, one doesn't need to know inside-out of a subject, and subjects like SQL Server so vast that every single day we learn something new with this product, and even a complete lifetime will fly off if we keep doing this. Again, the various roles one can get into for products like SQL Server are from Database Developer, Database Modelers, Database Architect, Database Administrator and many more. Hence, this book is geared towards demystifying and a refresher for memories on the fundamentals which sometimes are the most important things to clear any type of interview for any role. Some of the concepts discussed are generic and are not tied to any specific version of SQL Server, but most of it the new features introduced with SQL Server have been included in this book.This book is not a shortcut or a sure to crack interview guide but this book gets you prepared in an organized manner. Let us also assure you this is neither a completely comprehensive guide but surely is a great starter nevertheless. Use this to guide you and be mentally prepared for the big day. When faced with this big day, we get overwhelmed and confused about where to start our preparation. And this book is just that secret recipe in your arsenal to get geared up. Sometimes these basics will help you narrow to a solution quickly when given a scenario.Now this book's flow is Question & Answer mode from start till the end to help you grasp the concepts faster and to the point. Once you get an understanding of concepts, then if we are twisted with the concept in a scenario it becomes easy to solve them. Most companies have a typical way to do interviews which are based on the scenario as per their environment and these are just combinations of the concepts to fit their need and SLA.Though each of these chapters is bucketed for convenience we highly recommend reading each of the sections nevertheless irrespective of the roles you might be doing as each of the sections have some interesting trivia's working with SQL Server. In the industry, the role of accidental DBA's especially with SQL Server is so common. Hence if you have performed the role of DBA for a short stink and want to brush-up your fundamentals then the respective sections will be a great skim. |
| dataquest vs datacamp vs 365 data science: Ultralearning Scott H. Young, 2019-08-06 Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success. |
| dataquest vs datacamp vs 365 data science: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| dataquest vs datacamp vs 365 data science: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| dataquest vs datacamp vs 365 data science: The Hitchhiker's Guide to Python Kenneth Reitz, Tanya Schlusser, 2016-08-30 The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist. |
| dataquest vs datacamp vs 365 data science: Think Stats Allen B. Downey, 2014-10-16 If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data |
| dataquest vs datacamp vs 365 data science: Hackers & Painters Paul Graham, 2004-05-18 The author examines issues such as the rightness of web-based applications, the programming language renaissance, spam filtering, the Open Source Movement, Internet startups and more. He also tells important stories about the kinds of people behind technical innovations, revealing their character and their craft. |
| dataquest vs datacamp vs 365 data science: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| dataquest vs datacamp vs 365 data science: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| dataquest vs datacamp vs 365 data science: Smart Sensors and Devices in Artificial Intelligence Dan Zhang, Xuechao Duan, 2021-04-07 Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices |
| dataquest vs datacamp vs 365 data science: Data Science for Healthcare Sergio Consoli, Diego Reforgiato Recupero, Milan Petković, 2019-02-23 This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book. |
| dataquest vs datacamp vs 365 data science: Natural Language Processing in Action Hannes Hapke, Cole Howard, Hobson Lane, 2019-03-16 Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets of thought (NLP overview) Build your vocabulary (word tokenization) Math with words (TF-IDF vectors) Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS) Baby steps with neural networks (perceptrons and backpropagation) Reasoning with word vectors (Word2vec) Getting words in order with convolutional neural networks (CNNs) Loopy (recurrent) neural networks (RNNs) Improving retention with long short-term memory networks Sequence-to-sequence models and attention PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES) Information extraction (named entity extraction and question answering) Getting chatty (dialog engines) Scaling up (optimization, parallelization, and batch processing) |
| dataquest vs datacamp vs 365 data science: Learning Apex Programming Matt Kaufman, Michael Wicherski, 2015-01-31 If you are a developer who has some object-oriented programming experience, Learning Apex Programming is the perfect book for you. This book is most appropriate for developers who wish to gain an understanding of the Force.com platform and how to use Apex to create business applications. |
| dataquest vs datacamp vs 365 data science: An Introduction to Survival Analysis Using Stata, Second Edition Mario Cleves, 2008-05-15 [This book] provides new researchers with the foundation for understanding the various approaches for analyzing time-to-event data. This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ...--Book jacket. |
| dataquest vs datacamp vs 365 data science: Big Data Analytics in Healthcare Anand J. Kulkarni, Patrick Siarry, Pramod Kumar Singh, Ajith Abraham, Mengjie Zhang, Albert Zomaya, Fazle Baki, 2019-10-01 This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms. |
| dataquest vs datacamp vs 365 data science: Learn More Python 3 the Hard Way Zed A. Shaw, 2017-09-01 Transform Your Ideas into High-Quality Python Code! Zed Shaw has perfected the world’s best system for becoming a truly effective Python 3.x developer. Follow it and you will succeed—just like the tens of millions of programmers he’s already taught. You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, Zed Shaw taught you the basics of Programming with Python 3. Now, in Learn More Python 3 the Hard Way, you’ll go far beyond the basics by working through 52 brilliantly crafted projects. Each one helps you build a key practical skill, combining demos to get you started and challenges to deepen your understanding. Zed then teaches you even more in 12 hours of online videos, where he shows you how to break, fix, and debug your code. First, you’ll discover how to analyze a concept, idea, or problem to implement in software. Then, step by step, you’ll learn to design solutions based on your analyses and implement them as simply and elegantly as possible. Throughout, Shaw stresses process so you can get started and build momentum, creativity to solve new problems, and quality so you’ll build code people can rely on. Manage complex projects with a programmer’s text editor Leverage the immense power of data structures Apply algorithms to process your data structures Master indispensable text parsing and processing techniques Use SQL to efficiently and logically model stored data Learn powerful command-line tools and skills Combine multiple practices in complete projects It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll go beyond merely writing code that runs: you’ll craft high-quality Python code that solves real problems. You’ll be a serious Python programmer. Perfect for Everyone Who’s Already Started Working with Python, including Junior Developers and Seasoned Python Programmers Upgrading to Python 3.6+ Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available. |
| dataquest vs datacamp vs 365 data science: SAS Certification Prep Guide Sas Institute, 2017-12-22 Prepare for the SAS Base Programming for SAS 9 exam with the official guide by the SAS Global Certification Program. New and experienced SAS users who want to prepare for the SAS Base Programming for SAS 9 exam will find this guide to be an invaluable, convenient, and comprehensive resource that covers all of the objectives tested on the exam. Now in its fourth edition, the guide has been extensively updated, and revised to streamline explanations. Major topics include importing and exporting raw data files, creating and modifying SAS data sets, and identifying and correcting data syntax and programming logic errors. The chapter quizzes have been thoroughly updated and full solutions are included at the back of the book. In addition, links are provided to the exam objectives, practice exams, and other helpful resources, such as the updated Base SAS glossary and an expanded collection of practice data sets. |
| dataquest vs datacamp vs 365 data science: How to Hack Like a GHOST Sparc Flow, 2020-02-29 There are a thousand and one ways to hack an Active Directory environment. But, what happens when end up in a full Cloud environment with thousands of servers, containers and not a single Windows machine to get you going?When we land in an environment designed in the Cloud and engineered using the latest DevOps practices, our hacker intuition needs a little nudge to follow along. How did the company build their systems and what erroneous assumptions can we take advantage of?This book covers the basics of hacking in this new era of Cloud and DevOps: Break container isolation, achieve persistence on Kubernetes cluster and navigate the treacherous sea of AWS detection features to make way with the company's most precious data.Whether you are a fresh infosec student or a Windows veteran, you will certainly find a couple of interesting tricks to help you in your next adventure. |
| dataquest vs datacamp vs 365 data science: Learning from Data Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, 2012-01-01 |
| dataquest vs datacamp vs 365 data science: Data Management Using Stata Michael N Mitchell, Taylor & Francis Group, 2020-06-25 This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the nuts and bolts examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving--there is a good chance that even the experienced user will learn some new tricks. |
| dataquest vs datacamp vs 365 data science: Handling Strings with R Gaston Sanchez, 2021-02-25 This book aims to help you get started with handling strings in R. It provides an overview of several resources that you can use for string manipulation. It covers useful functions in packages base and stringr, printing and formatting characters, regular expressions, and other tricks. |
| dataquest vs datacamp vs 365 data science: Microeconometrics Using Stata, Revised Edition A. Colin Cameron, Pravin K. Trivedi, 2010-03-09 A complete and up-to-date survey of microeconometric methods available in Stata, Microeconometrics Using Stata, Revised Edition is an outstanding introduction to microeconometrics and how to execute microeconometric research using Stata. It covers topics left out of most microeconometrics textbooks and omitted from basic introductions to Stata. This revised edition has been updated to reflect the new features available in Stata 11 that are useful to microeconomists. Instead of using mfx and the user-written margeff commands, the authors employ the new margins command, emphasizing both marginal effects at the means and average marginal effects. They also replace the xi command with factor variables, which allow you to specify indicator variables and interaction effects. Along with several new examples, this edition presents the new gmm command for generalized method of moments and nonlinear instrumental-variables estimation. In addition, the chapter on maximum likelihood estimation incorporates enhancements made to ml in Stata 11. Throughout the book, the authors use simulation methods to illustrate features of the estimators and tests described and provide an in-depth Stata example for each topic discussed. They also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make this book an invaluable, hands-on addition to the library of anyone who uses microeconometric methods. |
| dataquest vs datacamp vs 365 data science: 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 |
TOP 20 PLATFORMS TO LEARN DATA SCIENCE - Data Valley
Mar 20, 2023 · DATA SCIENCE 01 03 05 ... Ware (MIT OCW) DataCamp 365 Data Science Udacity Analytics Vidhya DPhi edX Educative Kaggle Pluralsight FreeCodeCamp Cognitive …
Python for Data Science-FINAL - Cloudinary
It provides an in-depth discussion of data representation strategies, showing how data structures are implemented in Python along with demonstrating tools for data science and software …
Build Modern Data Engineering Skills with DataCamp
DataCamp's data platform performs ingestion from 50+ data sources and processes 1500+ transformations to deliver data marts across 10+ domains to power business insights via BI …
Our Guide to Open Source in Data Science - DataCamp
Data Engineer vs Data Scientist - media.datacamp.com
Data Analyst - Roadmap
Online Courses and Certifications Enroll in online courses or pursue certifications to gain learning and validate your skills. Platforms like Coursera, edX, Udemy, and DataCamp o!er courses in …
The Definitive Guide to Machine Learning for Business …
True interactive learning for Women in Big Data DataCamp …
DataCamp video Create a DataCamp account. Follow the link received via email. Start earning XP by starting a Theory course like Introduction to Data Used DataCamp before? Enroll in one …
Data Engineering for Everyone - Lexicon - DataCamp
Democratizing Data Science in Your Organization
Data Trends and Predictions 2021: The Year of Data Fluency
Why Get DataCamp Certified: A Deep Dive into the DataCamp …
Certification is not the same as a certificate. certificate is usually automatically granted upon completion of learning. certification must be achieved via rigorous assessment. that requires …
Python For Data Science Cheat Sheet Lists Also see NumPy …
Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The …
Use Case: Data Analysis - media.datacamp.com
Python For Data Science Cheat Sheet - Amazon Web Services, …
Python For Data Science Cheat Sheet Scikit-Learn Learn Python for data science Interactively at www.DataCamp.com Scikit-learn DataCamp Learn Python for Data Science Interactively …
Your Organization's Guide to Data Maturity - DataCamp
Frequently Asked Questions When Purchasing a Business …
The Finance Leader’s Guide to Data Science Use Cases
Data Science Cheat Sheet for Business Leaders - DataCamp
Why is Data Literacy Important? - DataCamp
TOP 20 PLATFORMS TO LEARN DATA SCIENCE - Data Valley
Mar 20, 2023 · DATA SCIENCE 01 03 05 ... Ware (MIT OCW) DataCamp 365 Data Science Udacity Analytics Vidhya DPhi edX Educative Kaggle Pluralsight FreeCodeCamp Cognitive …
Python for Data Science-FINAL - Cloudinary
It provides an in-depth discussion of data representation strategies, showing how data structures are implemented in Python along with demonstrating tools for data science and software …
Build Modern Data Engineering Skills with DataCamp
DataCamp's data platform performs ingestion from 50+ data sources and processes 1500+ transformations to deliver data marts across 10+ domains to power business insights via BI …
Our Guide to Open Source in Data Science - DataCamp
This guide will demystify the most popular data science and machine learning packages and tools in R and Python and uncover their use cases throughout an organization.
Data Engineer vs Data Scientist - media.datacamp.com
DataCamp Certification is an official recognition that a person has achieved the required skill level as a data professional. Why DataCamp Certification? We objectively certify based on industry …
Data Analyst - Roadmap
Online Courses and Certifications Enroll in online courses or pursue certifications to gain learning and validate your skills. Platforms like Coursera, edX, Udemy, and DataCamp o!er courses in …
The Definitive Guide to Machine Learning for Business …
Data Science (DS) is about making discoveries and creating insights from data, and communicating these insights and discoveries to non-technical stakeholders. Machine learning …
True interactive learning for Women in Big Data DataCamp …
DataCamp video Create a DataCamp account. Follow the link received via email. Start earning XP by starting a Theory course like Introduction to Data Used DataCamp before? Enroll in one …
Data Engineering for Everyone - Lexicon - DataCamp
Data science: the process of extracting knowledge from data. Data warehouse: a central repository of integrated data from one or more disparate sources. ETL: Extract, Transform, …
Democratizing Data Science in Your Organization
data science organization drives the roadmap on how they provide value to the organization. Data science leaders may guide priorities to protect their teams from what they might view as …
Data Trends and Predictions 2021: The Year of Data Fluency
The data science revolution has always made the impossible possible. With data science methodologies and technologies, specialized teams have worked on solving important …
Why Get DataCamp Certified: A Deep Dive into the …
Certification is not the same as a certificate. certificate is usually automatically granted upon completion of learning. certification must be achieved via rigorous assessment. that requires …
Python For Data Science Cheat Sheet Lists Also see NumPy …
Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The …
Use Case: Data Analysis - media.datacamp.com
When Should I Use Python vs. R? Python and R are both great programming languages for data science and analytics. Since they’re open-source, they’re free to download for everyone, …
Python For Data Science Cheat Sheet - Amazon Web …
Python For Data Science Cheat Sheet Scikit-Learn Learn Python for data science Interactively at www.DataCamp.com Scikit-learn DataCamp Learn Python for Data Science Interactively …
Your Organization's Guide to Data Maturity - DataCamp
In this guide, we outline a framework for evaluating, and scaling data maturity throughout the organization, define the various data maturity stages an organization goes through, and draw …
Frequently Asked Questions When Purchasing a Business …
Note: If you are a current customer of DataCamp for Business, visit our DataCamp for Business FAQs in the help center. Read on if you are considering purchasing DataCamp for Business.
The Finance Leader’s Guide to Data Science Use Cases
achieve their data science and data "uency goals. For example, we partnered with AXA XL to upskill data scientists and actuaries on natural language processing and predictive analytics.
Data Science Cheat Sheet for Business Leaders - DataCamp
Deep Learning / Neural Networks enables unsupervised machine learning using data that is unstructured or unlabeled. Explainable AI is an emerging field in machine learning that applies …
Why is Data Literacy Important? - DataCamp
Why is Data Literacy Important? •Understanding technology is less important than understanding data •Pick the right tool for the user and use case •Self-service analytics (aka “data …
