Dive Into Data Science

Download or read book Dive Into Data Science written by Bradford Tuckfield and published by No Starch Press. This book was released on 2023-07-04 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use data science and Python to solve everyday business problems. Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges. With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions. Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms. You’ll also learn how to: Forecast consumer demand Optimize marketing campaigns Reduce customer attrition Predict website traffic Build recommendation systems With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in!

Download or read book Dive Into Algorithms written by Bradford Tuckfield and published by No Starch Press. This book was released on 2021-01-05 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language. Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares. You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers. You'll also learn how to: • Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees • Measure the efficiency and speed of algorithms • Generate Voronoi diagrams for use in various geometric applications • Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles • Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions • Use simulated annealing to perform global optimization • Build a decision tree to predict happiness based on a person's characteristics Once you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms.

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Build a Career in Data Science written by Emily Robinson and published by Manning. This book was released on 2020-03-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Download or read book Pandas for Everyone written by Daniel Y. Chen and published by Addison-Wesley Professional. This book was released on 2017-12-15 with total page 1093 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Science in Education Using R written by Ryan A. Estrellado and published by Routledge. This book was released on 2020-10-26 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.