Python for Data Analysis: Step-By-Step with Projects

Python for Data Analysis: Step-By-Step with Projects

by Just Into Data
Released December 2021
Publisher(s): Packt Publishing
ISBN: 9781803243979

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Video description

Data analysis is a critical skill and is getting more popular. Nowadays, every organization has some data. Data could be extremely useful, but not without appropriate analysis. Data analysis enables us to transform data into insights for businesses to make informative decisions.

You can find data analysis being used in every industry, be it healthcare, finance, or technology. Python is one of the most in-demand skills for data science by employers. It is not only easy to learn but also powerful.

The course follows the approach of rather than dumping all the available Python libraries or functions to you, we picked only the most useful ones based on our industry experience to cover in the course. This allows you to focus and master the foundations. Besides Python programming, you will also get exposed to the basic statistical knowledge necessary for data analysis. Combined with detailed video lectures, you will be given a few projects to work on to reinforce your knowledge.

By the end of the course, you will have a solid foundation of data analysis, and be able to use Python for the complete process.

What You Will Learn

  • Learn to use Python for data analysis
  • Explore Python data analysis libraries (Pandas, Scikit-learn, Seaborn)
  • Learn about time data series in Python
  • Create data visualizations in Python
  • Learn to calculate summary statistics
  • Learn how to clean, manipulate, and transform data

Audience

This course is helpful for anyone interested in analyzing data effectively. You want to become a data analyst or a data scientist, or you just want the skills to work on your projects.

This course is beginner friendly. However, we recommend you have some basic knowledge of Python or at least another programming language.

About The Author

Just Into Data: Just Into Data is the brainchild of Justin and Lianne. Justin is an experienced data scientist in many different fields, such as marketing, anti-money laundering, and big data technologies. He also has a bachelor’s degree in computer engineering and a master’s degree in statistics.

Lianne is an experienced statistician who has worked in the central bank as well as commercial banks, where she monitored major financial institutions and conducted fraud analysis. She has both a bachelor’s and a master’s degree in statistics.

Table of contents

  1. Chapter 1 : Introduction
    1. Introduction
    2. Course Overview
  2. Chapter 2 : Python Crash Course
    1. Setting Up Python environment
    2. Overview of Data Types, Numeric, Define Variables
    3. Strings, Common Functions, and Methods
    4. Lists, Tuples, Sets, Dictionaries, Booleans
    5. If Statements, Loops
    6. Define Functions, Use Packages
    7. Lambda Functions, Conditional Expressions
  3. Chapter 3 : Importing Data
    1. Pandas Data Structures Overview
    2. Loading Data
    3. Previewing Data
    4. Pandas Data Types Overview
    5. Exporting Data
  4. Chapter 4 : Exploring Data
    1. Combining Datasets
    2. Renaming Columns
    3. Selecting Columns
    4. Selecting Rows and Setting the Index (1)
    5. Selecting Rows and Setting the Index (2)
    6. Subsetting Both Rows and Columns
    7. Modifying Values
    8. Making a Copy
    9. Sorting Data
  5. Chapter 5 : Capstone Practice Project I
    1. NBA Games Project Overview
  6. Chapter 6 : Cleaning Data
    1. Data Cleaning Overview
    2. Removing Unnecessary Columns/Rows
    3. Missing Data Overview
    4. Tackling Missing Data (Dropping)
    5. Tackling Missing Data (Imputing with Constant)
    6. Tackling Missing Data (Imputing with Statistics) and Missing Indicators
    7. Tackling Missing Data (Imputing with Model)
    8. Handling Outliers (1)
    9. Handling Outliers (2)
    10. Cleaning Text
  7. Chapter 7 : Transforming Columns/Features
    1. Extracting Date and Time
    2. Binning
    3. Mapping New Values
    4. Applying Functions
  8. Chapter 8 : Capstone Practice Project II
    1. Czech Bank Project Overview
  9. Chapter 9 : Exploratory Data Analysis
    1. EDA Overview
    2. Aggregating Statistics
    3. Group By
    4. Pivoting Tables
    5. Distribution of One Feature
    6. Seaborn Library Overview
    7. Relationship of Two Features (1)
    8. Relationship of Two Features (2)
    9. Relationship of Multiple Features
    10. Seaborn Library Recap
  10. Chapter 10 : Capstone Practice Project III
    1. Olympic Games Project Overview
  11. Chapter 11 : Dealing with Time Series Data
    1. Introduction to Time Series
    2. Review of Date and Time
    3. Manipulating Datetime as an Index
    4. Resampling Frequency: Downsampling
    5. Resampling Frequency: Upsampling
    6. Rolling/Shifting Time Windows
  12. Chapter 12 : Thank You
    1. Course Wrap Up

Product information

  • Title: Python for Data Analysis: Step-By-Step with Projects
  • Author(s): Just Into Data
  • Release date: December 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781803243979