Learn Data Science Using Python : A Quick-Start Guide

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you'll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. What You'll Learn Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Who This Book Is For Data Analysts, data scientists, Python programmers, and software developers new to data science. Table of Contents About the Author About the Technical Reviewer Introduction Chapter 1: Data Science in Action Data Science Process Case Study: Presidential Elections in Maine Population Gender Race Age Voter Turnout Winning Candidates in 2012 Categories/Issues Issues Facing the Country Categories: Issues Facing Maine Factors Affecting Maine’s Economy Comparing the Unemployment Rate of Maine to That of the Rest of the States Median Income for Maine How Did Exit Polls Show the Income Factor in the 2016 Election in Maine? Reviewing Past Elections to See If There Are Any Predictable Outcomes (Patterns) Modeling My 2016 Predictions My 2020 Predictions Summary Chapter 2: Getting Started Installation Python on the Cloud What Is Python? Tour Datatypes Typecasting Scalar Type Casting Container Type Casting Advanced Type Casting Pandas Why Use Pandas? Key Concepts Basic Operations with Pandas Numpy Why Use Numpy? Key Concepts Basic Operations with Numpy Summary Chapter 3: Data Visualizations Scatterplot Scatterplot Relationships Plotting More Than One Scatterplot in the Same Image Histogram Series Plot Bar Chart How to Sort a Bar Chart? Create a Histogram Using a Bar Chart Bubble Chart Summary Chapter 4: Statistical Analysis and Linear Models Statistical Analysis Frequency Tables Summary Statistics Correlation Analysis Hypothesis Testing T-Test One-Sample T-Test Two-Sample T-Test Summary Chapter 5: Data Preprocessing and Feature Engineering Comment Statement Arithmetic and Assignment Operators Python Operator Precedence Rule: PEMDAS Comparison Operators Logical, Membership, and Identity Operators How to Represent Missing Values in Raw Data Loops While Loop For Loop Looping over a Sequence of Numbers Looping over a String Looping over a List Looping over a DataFrame Summary Chapter 6: Preparing Data for Analysis Rename Format Creating New Variables Rearrange the Dataset Variables IF Statement IF-ELIF Statements Drop a Row Drop a Column Subset Append Merge Summary Chapter 7: Regression Simple Linear Regression Multiple Linear Regression Logistic Regression Summary Index