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

Video description

Develop your data analysis skills in Python and gain practical experience analyzing real-world datasets.

About This Video

Experience analyzing real-world datasets in lectures and guided projects
The course is arranged in different sections based on the step-by-step process of REAL data analysis
Get exposed to basic statistical knowledge necessary for data analysis

In Detail

Data analysis is a critical skill and is getting more …

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

Video description

Develop your data analysis skills in Python and gain practical experience analyzing real-world datasets.

About This Video

Experience analyzing real-world datasets in lectures and guided projects
The course is arranged in different sections based on the step-by-step process of REAL data analysis
Get exposed to basic statistical knowledge necessary for data analysis

In Detail

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.

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.

Publisher resources

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Chapter 1 : Introduction

Introduction

Course Overview

Chapter 2 : Python Crash Course

Setting Up Python environment

Overview of Data Types, Numeric, Define Variables

Strings, Common Functions, and Methods

Lists, Tuples, Sets, Dictionaries, Booleans

If Statements, Loops

Define Functions, Use Packages

Lambda Functions, Conditional Expressions

Chapter 3 : Importing Data

Pandas Data Structures Overview

Loading Data

Previewing Data

Pandas Data Types Overview

Exporting Data

Chapter 4 : Exploring Data

Combining Datasets

Renaming Columns

Selecting Columns

Selecting Rows and Setting the Index (1)

Selecting Rows and Setting the Index (2)

Subsetting Both Rows and Columns

Modifying Values

Making a Copy

Sorting Data

Chapter 5 : Capstone Practice Project I

NBA Games Project Overview

Chapter 6 : Cleaning Data

Data Cleaning Overview

Removing Unnecessary Columns/Rows

Missing Data Overview

Tackling Missing Data (Dropping)

Tackling Missing Data (Imputing with Constant)

Tackling Missing Data (Imputing with Statistics) and Missing Indicators

Tackling Missing Data (Imputing with Model)

Handling Outliers (1)

Handling Outliers (2)

Cleaning Text

Chapter 7 : Transforming Columns/Features

Extracting Date and Time

Binning

Mapping New Values

Applying Functions

Chapter 8 : Capstone Practice Project II

Czech Bank Project Overview

Chapter 9 : Exploratory Data Analysis

EDA Overview

Aggregating Statistics

Group By

Pivoting Tables

Distribution of One Feature

Seaborn Library Overview

Relationship of Two Features (1)

Relationship of Two Features (2)

Relationship of Multiple Features

Seaborn Library Recap

Chapter 10 : Capstone Practice Project III

Olympic Games Project Overview

Chapter 11 : Dealing with Time Series Data

Introduction to Time Series

Review of Date and Time

Manipulating Datetime as an Index

Resampling Frequency: Downsampling

Resampling Frequency: Upsampling

Rolling/Shifting Time Windows

Chapter 12 : Thank You

Course Wrap Up

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