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| data science portfolio website template: blogdown Yihui Xie, Alison Presmanes Hill, Amber Thomas, 2017-12-01 blogdown: Creating Websites with R Markdown provides a practical guide for creating websites using the blogdown package in R. In this book, we show you how to use dynamic R Markdown documents to build static websites featuring R code (or other programming languages) with automatically rendered output such as graphics, tables, analysis results, and HTML widgets. The blogdown package is also suitable for technical writing with elements such as citations, footnotes, and LaTeX math. This makes blogdown an ideal platform for any website designed to communicate information about data science, data analysis, data visualization, or R programming. Note that blogdown is not just for blogging or sites about R; it can also be used to create general-purpose websites. By default, blogdown uses Hugo, a popular open-source static website generator, which provides a fast and flexible way to build your site content to be shared online. Other website generators like Jekyll and Hexo are also supported. In this book, you will learn how to: Build a website using the blogdown package; Create blog posts and other website content as dynamic documents that can be easily edited and updated; Customize Hugo templates to suit your site’s needs; Publish your website online; Migrate your existing websites to blogdown and Hugo. |
| data science portfolio website template: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science portfolio website template: The Kaggle Book Konrad Banachewicz, Luca Massaron, 2022-04-22 Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML A concise collection of smart data handling techniques for modeling and parameter tuning Book DescriptionMillions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!What you will learn Get acquainted with Kaggle as a competition platform Make the most of Kaggle Notebooks, Datasets, and Discussion forums Create a portfolio of projects and ideas to get further in your career Design k-fold and probabilistic validation schemes Get to grips with common and never-before-seen evaluation metrics Understand binary and multi-class classification and object detection Approach NLP and time series tasks more effectively Handle simulation and optimization competitions on Kaggle Who this book is for This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make the most of this book. |
| data science portfolio website template: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science portfolio website template: 625+ Easy Digital Product Ideas For Earning Passive Income While You Sleep Om Prakash Saini, 2024-02-16 Embark on a journey of endless possibilities with 625+ Easy Digital Product Ideas For Earning Passive Income While You Sleep. This book is your ultimate guide to unlocking the world of online entrepreneurship without breaking a sweat. Packed with over 625 simple and lucrative digital product ideas, it covers a wide range of categories like Ebooks and Written Content, Online Courses, Printables, Stock Photos, Videos, Audio Products, Software, 3D Models, Web Development Resources, and more. Whether you're a seasoned digital entrepreneur or a newbie looking to make money while you snooze, this book has something for everyone. Dive into the realm of passive income and discover how to turn your creativity into cash. From crafting engaging Ebooks to developing user-friendly applications, the book is a treasure trove of inspiration. With clear and accessible language, it breaks down complex ideas, making it perfect for anyone ready to take the leap into the world of online income. Don't just dream about financial freedom – make it a reality with 625+ Easy Digital Product Ideas For Earning Passive Income While You Sleep. Your journey to success starts now! |
| data science portfolio website template: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data science portfolio website template: Intelligent Data Analysis Michael R. Berthold, David J Hand, 2007-06-07 This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes. |
| data science portfolio website template: Machine Learning in Industry Shubhabrata Datta, J. Paulo Davim, 2021-07-24 This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems. |
| data science portfolio website template: Big Data Analytics and Knowledge Discovery Sanjay Madria, Takahiro Hara, 2015-08-09 This book constitutes the refereed proceedings of the 17th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The 31 revised full papers presented were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections similarity measure and clustering; data mining; social computing; heterogeneos networks and data; data warehouses; stream processing; applications of big data analysis; and big data. |
| data science portfolio website template: Python Real-World Projects Steven F. Lott, 2023-09-15 Develop Python applications using an enterprise-based approach with unit and acceptance tests by following agile methods to create a minimum viable product (MVP) and iteratively add features Key Features Master Python and related technologies by working on 12 hands-on projects Accelerate your career by building a personal project portfolio Explore data acquisition, preparation, and analysis applications Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's competitive job market, a project portfolio often outshines a traditional resume. Python Real-World Projects empowers you to get to grips with crucial Python concepts while building complete modules and applications. With two dozen meticulously designed projects to explore, this book will help you showcase your Python mastery and refine your skills. Tailored for beginners with a foundational understanding of class definitions, module creation, and Python's inherent data structures, this book is your gateway to programming excellence. You’ll learn how to harness the potential of the standard library and key external projects like JupyterLab, Pydantic, pytest, and requests. You’ll also gain experience with enterprise-oriented methodologies, including unit and acceptance testing, and an agile development approach. Additionally, you’ll dive into the software development lifecycle, starting with a minimum viable product and seamlessly expanding it to add innovative features. By the end of this book, you’ll be armed with a myriad of practical Python projects and all set to accelerate your career as a Python programmer.What you will learn Explore core deliverables for an application including documentation and test cases Discover approaches to data acquisition such as file processing, RESTful APIs, and SQL queries Create a data inspection notebook to establish properties of source data Write applications to validate, clean, convert, and normalize source data Use foundational graphical analysis techniques to visualize data Build basic univariate and multivariate statistical analysis tools Create reports from raw data using JupyterLab publication tools Who this book is forThis book is for beginner-to-intermediate level Python programmers looking to enhance their resume by adding a portfolio of 12 practical projects. A basic understanding of the Python language and its aligned technologies is a must. The book helps you polish your Python skills and project-building prowess without delving into basic Python fundamentals. |
| data science portfolio website template: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science portfolio website template: Causal Factor Investing Marcos M. López de Prado, 2023-10-31 Virtually all journal articles in the factor investing literature make associational claims, instead of causal claims. This Element analyzes the current state of causal confusion and proposes solutions with the potential to transform factor investing into a truly scientific discipline. This title is also available as Open Access on Cambridge Core. |
| data science portfolio website template: Machine Learning for Algorithmic Trading Stefan Jansen, 2020-07-31 Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required. |
| data science portfolio website template: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| data science portfolio website template: Driving Digital Isaac Sacolick, 2017-08-24 Every organization makes plans for updating products, technologies, and business processes. But that’s not enough anymore for the twenty-first-century company. The race is now on for everyone to become a digital enterprise. For those individuals who have been charged with leading their company’s technology-driven change, the pressure is intense while the correct path forward unclear. Help has arrived! In Driving Digital, author Isaac Sacolick shares the lessons he’s learned over the years as he has successfully spearheaded multiple transformations and helped shape digital-business best practices. Readers no longer have to blindly trek through the mine field of their company’s digital transformation. In this thoroughly researched one-stop manual, learn how to: • Formulate a digital strategy • Transform business and IT practices • Align development and operations • Drive culture change • Bolster digital talent • Capture and track ROI • Develop innovative digital practices • Pilot emerging technologies • And more! Your company cannot avoid the digital disruption heading its way. The choice is yours: Will this mean the beginning of the end for your business, or will your digital practices be what catapults you into next-level success? |
| data science portfolio website template: The Academic Portfolio Peter Seldin, J. Elizabeth Miller, 2010-12-28 This comprehensive book focuses squarely on academic portfolios, which may prove to be the most innovative and promising faculty evaluation and development technique in years. The authors identify key issues, red flag warnings, and benchmarks for success, describing the what, why, and how of developing academic portfolios. The book includes an extensively tested step-by-step approach to creating portfolios and lists 21 possible portfolio items covering teaching, research/scholarship, and service from which faculty can choose the ones most relevant to them. The thrust of this book is unique: It provides time-tested strategies and proven advice for getting started with portfolios. It includes a research-based rubric grounded in input from 200 faculty members and department chairs from across disciplines and institutions. It examines specific guiding questions to consider when preparing every subsection of the portfolio. It presents 18 portfolio models from 16 different academic disciplines. Designed for faculty members, department chairs, deans, and members of promotion and tenure committees, all of whom are essential partners in developing successful academic portfolio programs, the book will also be useful to graduate students, especially those planning careers as faculty members. |
| data science portfolio website template: bookdown Yihui Xie, 2016-12-12 bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub. |
| data science portfolio website template: Bayesian Inference in Statistical Analysis George E. P. Box, George C. Tiao, 2011-01-25 Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. |
| data science portfolio website template: Domain Driven Data Mining Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao, 2010-01-08 This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output. |
| data science portfolio website template: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data science portfolio website template: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data science portfolio website template: 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 |
| data science portfolio website template: Science As Inquiry Jack Hassard, 2011-03 Aligns to Common Core state standards--Cover. |
| data science portfolio website template: Partnering With Students Mary Jane O'Connell, Kara Vandas, 2015-04-04 Empower students to own their learning This resource is designed to empower teachers and leaders with strategies to develop learners who have the confidence and tools to engage in any challenge. By flipping the focus to student ownership of learning, the authors provide clear and simple ways to: Develop collaborative relationships Jointly establish clear expectations for learning and criteria for success Intentionally build learner strategies that last a lifetime Use formative assessment results to monitor progress Harness the power of reciprocal feedback to improve teaching and learning Empower and motivate students set meaningful goals and prove learning |
| data science portfolio website template: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub. |
| data science portfolio website template: 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. |
| data science portfolio website template: Reproducible Finance with R Jonathan K. Regenstein, Jr., 2018-09-24 Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards. |
| data science portfolio website template: 50 Successful Harvard Medical School Essays Staff of the Harvard Crimson, 2020-05-05 Fifty all-new essays that got their authors into Harvard Medical School, including MCAT scores, showing what worked, what didn’t, and how you can do it too. Competition to get into the nation’s top medical schools has never been more intense. Harvard Medical School in particular draws thousands of elite applicants from around the world. As admissions departments become increasingly selective, even the best and brightest need an edge. Writing a personal statement is a daunting part of the application process. In less than 5,300 characters, applicants must weave together experiences and passions into a memorable narrative to set them apart from thousands of other applicants. While there is no magic formula for writing the perfect essay, picking up this book will put them on the right track. 50 Successful Harvard Medical School Essays is the first in a new line of books published by the Staff of the Harvard Crimson. It includes fifty standout essays from students who successfully secured a spot at Harvard Medical School. Each student has a unique set of experiences that led them to medicine. Each essay includes analysis by Crimson editors on essay qualities and techniques that worked, so readers can apply them to their own writing. This book will aid applicants in composing essays that reveal their passion for medicine and the discipline they will bring to this demanding program and profession. It will give them the extra help they need to get into the best medical school programs in the world. |
| data science portfolio website template: 3D Deeksha Pandey, 2015-07-06 Riya, the girl next door, is very much one among us. Being born in a simple middle-class family in a small town of India and growing up with big dreams and aspiration, she doesn't know that the real name of life is 'compromise' and 'settle down with what you get easily'. And then she gets a ravishing opportunity interregnum she falls in love, a virtual one though. Life starts changing its colours. Life is going fine, but a test done just for curiosity changes everything. She is broken mentally and emotionally. What is her fault for which she is being punished? Does love really exist? Or is the expression of love also just a matter of convenience? |
| data science portfolio website template: Design Methods and Practices for Research of Project Management Beverly Pasian, Rodney Turner, 2016-08-12 Design Methods and Practices for Research of Project Management is the most comprehensive guide on how to do research of and in project management. Project management as a discipline has experienced near-exponential growth in its application across the business and not-for-profit sectors. This second edition of the authoritative reference book offers a substantial update on the first edition with over 60% new content and so provides both practitioner and student researchers with a fully up-to-date and complete guide to research practice on project management. In Design Methods and Practices for Research of Project Management, Beverly Pasian and Rodney Turner have brought together 27 original chapters from many of the leading international thinkers in project management research. The collection looks at each step in the research stages, including research strategy, management, methodology (quantitative and qualitative), techniques as well as how to share and publish research findings. The chapters offer an international perspective with examples from a wide range of project management applications; engineering, construction, mega-projects, high-risk environments and social transformation. Each chapter includes tips and exercises for the research student, as well as a complete set of further references. The book is the go-to text for practitioners undertaking research in companies, and also doctoral and masters students and their supervisors who are involved in research projects in and for universities. |
| data science portfolio website template: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science portfolio website template: Likeonomics Rohit Bhargava, 2012-04-25 How to become a trusted resource for consumers in a society of constant manipulation People decide who to trust, what advice to heed, and which individuals to forge personal or transactional relationships with based on a simple metric of believability. Success, in turn, comes from understanding one basic principle: how to be more trusted. Likeonomics offers a new vision of a world beyond Facebook where personal relationships, likeability, brutal honesty, extreme simplicity, and basic humanity are behind everything from multi-million dollar mergers to record-breaking product sales. There is a real ROI to likeability, and exactly how big it is will amaze you. Likeonomics provides real-world case studies of brands and individuals that have used these principles to become wildly successful, including: An iconic technology brand that awakened a revolution among their employees by standing for something bigger than their products A Portuguese singer who used YouTube to rack up more than 30 million views and launch her professional career. A regional team of financial advisors that went from being last in the nation among 176 branches to first, and stayed there for 13 of the next 15 years A tiny professional sports talent agent who achieved the impossible by landing the #1 drafted player in the NFL draft as a client through the power of relationships Author Rohit Bhargava is a founding member of the world's largest group of social media strategists at Ogilvy, where he has led marketing strategy for clients including Intel, Pepsi, Lenovo, Seiko, Unilever, and dozens of other large companies With Likeonomics as a guide, readers will get unconventional advice on how to stand out in a good way, avoid the hype and strategic traps of social media, and appeal to customers in a way that secures your company as a trusted and believable resource. |
| data science portfolio website template: Code Complete Steve McConnell, 2004-06-09 Widely considered one of the best practical guides to programming, Steve McConnell’s original CODE COMPLETE has been helping developers write better software for more than a decade. Now this classic book has been fully updated and revised with leading-edge practices—and hundreds of new code samples—illustrating the art and science of software construction. Capturing the body of knowledge available from research, academia, and everyday commercial practice, McConnell synthesizes the most effective techniques and must-know principles into clear, pragmatic guidance. No matter what your experience level, development environment, or project size, this book will inform and stimulate your thinking—and help you build the highest quality code. Discover the timeless techniques and strategies that help you: Design for minimum complexity and maximum creativity Reap the benefits of collaborative development Apply defensive programming techniques to reduce and flush out errors Exploit opportunities to refactor—or evolve—code, and do it safely Use construction practices that are right-weight for your project Debug problems quickly and effectively Resolve critical construction issues early and correctly Build quality into the beginning, middle, and end of your project |
| data science portfolio website template: Practical Web Scraping for Data Science Seppe vanden Broucke, Bart Baesens, 2018-04-18 This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set. Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. What You'll Learn Leverage well-established best practices and commonly-used Python packages Handle today's web, including JavaScript, cookies, and common web scraping mitigation techniques Understand the managerial and legal concerns regarding web scraping Who This Book is For A data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well. |
| data science portfolio website template: Designing for Science Kevin Crowley, Christian D. Schunn, Takeshi Okada, 2001-03-01 This volume explores the integration of recent research on everyday, classroom, and professional scientific thinking. It brings together an international group of researchers to present core findings from each context; discuss connections between contexts, and explore structures; technologies, and environments to facilitate the development and practice of scientific thinking. The chapters focus on: * situations from young children visiting museums, * middle-school students collaborating in classrooms, * undergraduates learning about research methods, and * professional scientists engaged in cutting-edge research. A diverse set of approaches are represented, including sociocultural description of situated cognition, cognitive enthnography, educational design experiments, laboratory studies, and artificial intelligence. This unique mix of work from the three contexts deepens our understanding of each subfield while at the same time broadening our understanding of how each subfield articulates with broader issues of scientific thinking. To provide a common focus for exploring connections between everyday, instructional, and professional scientific thinking, the book uses a practical implications subtheme. In particular, each chapter has direct implications for the design of learning environments to facilitate scientific thinking. |
| data science portfolio website template: Better Data Visualizations Jonathan Schwabish, 2021-02-09 Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message. |
| data science portfolio website template: Proceedings of the International Conference on Systems, Science, Control, Communication, Engineering and Technology 2015 Kokula Krishna Hari K, Keerthivasan M, D Bhanu, 2015-08-10 ICSSCCET 2015 will be the most comprehensive conference focused on the various aspects of advances in Systems, Science, Management, Medical Sciences, Communication, Engineering, Technology, Interdisciplinary Research Theory and Technology. This Conference provides a chance for academic and industry professionals to discuss recent progress in the area of Interdisciplinary Research Theory and Technology. Furthermore, we expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject. The goal of this conference is to bring together the researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of Interdisciplinary Research Theory and Technology. |
| data science portfolio website template: Computer Science and Education in Computer Science Tanya Zlateva, Rossitza Goleva, 2022-11-02 This book constitutes the refereed post-conference proceedings of the 18th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2022, held in June 2022 in Sofia, Bulgaria. Due to COVID-19 pandemic the conference was held On-Site and virtually. The 15 full papers and 9 short papers were carefully reviewed and selected from 53 submissions. The papers present are grouped into 2 tracks, i.e., computer science implementations and education in computer science. CSECS conference presents research in software engineering and information systems design, cryptography, the theoretical foundation of the algorithms, and implementation of machine learning and big data technologies. Another important topic of the conference is the education in computer science which includes the introduction and evaluation of computing programs, curricula, and online courses, to syllabus, laboratories, teaching, and pedagogy aspects. The technical and education topics evolved multiple existing and emerging technologies, solutions, and services for design and training providing a heterogeneous approach towards delivering Software 4.0 and Education 4.0 to a broad range of citizens and societies. |
| data science portfolio website template: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| data science portfolio website template: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
