Data Science 101
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Everyday Data Science 101
Author: E J Calden
language: en
Publisher: Independently Published
Release Date: 2025-07-28
Everyday Data Science 101 isn't just about learning data science - it's about living it. Through the story of Emily, a small café owner with no technical background and a lot of questions, this book gently guides readers from curious beginner to confident problem solver. When her afternoon sales start slipping and instinct alone isn't cutting it, Emily turns to data for help - but quickly realizes she doesn't know where to begin. What follows is a warm, practical journey through real-world decision-making, where concepts like data types, cleaning methods, visualizations, basic statistics, and even machine learning are introduced not in theory, but through the everyday dilemmas of running a business. With each chapter, Emily - and the reader - builds fluency in the core principles of data science, all while solving familiar problems like forecasting demand, designing loyalty programs, and understanding customer patterns. Written in an immersive, narrative expository style, this book doesn't rely on jargon or formulas. It's a mentor-guided, story-driven way to learn data science - from the café floor to the bigger picture. No prior experience needed, just curiosity and a willingness to think differently.
Data Science 101
★ 55% OFF for Bookstores! NOW at $ 21,97 instead of $31.97! LAST DAYS! ★ Your Customers Will Never Stop To Use This Amazing Guide! Do you want to know how Data science helps in business? This book will discuss everything that we need to know when it comes to data science and how to complete the process of data science with Python. There are so many different parts that come together when we work on data science, but if you are able to put it all together, and work to really analyze the information that you have to beat out the competition, you will find that data science with Python can be the right move for you. We will explore how so many businesses will take the time to gather up information, usually from a variety of sources, and then will be unsure of what they should do with that information once they have collected it. We can then take a look at the data life cycle and how we can take that information, clean it off, analyze it, and come up with insights and predictions that help grow our business more than ever before. We will spend this time looking what Python is about, how to download the program on your chosen operating system, and some of the basics that come with coding in Python. This guidebook went through all of the steps that you need to know in order to get started with data science and some of the basic parts of the Python code. We can then put all of this together in order to create the right analytical algorithm that, once it is trained properly and tested with the right kinds of data, will work to make predictions, provide information, and even show us insights that were never possible before. And all that you need to do to get this information is to use the steps that we outline and discuss in this guidebook. There is a lot of buzz in the business world, no matter what industry it is, about machine learning, the Python language, and of course, data science, and being able to put these terms together and learn how they work can make a big difference in how well your business will do now and in the future. There are already a ton of companies out there who have been able to gain a competitive edge with data science and the various models and algorithms of Python that go with it, and you can as well. This book covers: What is Data Science? The Python Coding Language Some of the Basic Coding in Python The Best Python Libraries to Use with Data Science The Basics of Jupyter and Why We Should Use It Working with Anaconda in Python The Basics of the Pandas Library What is WinPython and How Can We Use It? Common Tasks to Do in Info Science Different Data Types to Work With The Future of Data Science and Where It Will Go from Here There are so many great ways that you can use the data you have been collecting for some time now and being able to complete the process of data visualization will ensure that you get it all done. When you are ready to get started with Python data science, make sure to check out this guidebook to learn how. There is so much that can come into play when we work with data science, and it is one of the best ways for a business to differentiate from the competition and actually see some results in the process. And the Python language is a great option to learn to help us analyze and create a model that works with the info that we have. When we are ready to learn more about data science, and how to use the Python coding language to go with it, make sure to check out this guidebook to help you get started. Buy it NOW and let your customers get addicted to this amazing book!
Getting Started with Data Science
Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.