Python Programming for Data Analysis- Ultimate Guide

Python Programming for Data Analysis: Ultimate Guide, available at $54.99, has an average rating of 5, with 114 lectures, based on 4 reviews, and has 39 subscribers.

You will learn about Installing Python and necessary libraries for a seamless coding environment setup. Mastering data type conversion and formatting techniques for consistent data representation. Utilizing Pandas functions for efficient data manipulation tasks. Implementing various types of join operations to merge datasets effectively. Aggregating data and engineering new features for insightful analysis. Handling date and time data effectively using Python libraries. Creating customizable visualizations with libraries like Matplotlib and Seaborn for effective data communication. Completing a capstone project: E-commerce data using concepts and skills learned from this course to create effective visualizations and communicate findings. This course is ideal for individuals who are This course is designed for individuals with no prior experience in tools (e.g., R or Python). or For new graduates considering a data analytics career or For career switchers aiming to become data analysts or upgrade their skills beyond Excel spreadsheets. It is particularly useful for This course is designed for individuals with no prior experience in tools (e.g., R or Python). or For new graduates considering a data analytics career or For career switchers aiming to become data analysts or upgrade their skills beyond Excel spreadsheets.

Enroll now: Python Programming for Data Analysis: Ultimate Guide

Summary

Title: Python Programming for Data Analysis: Ultimate Guide

Price: $54.99

Average Rating: 5

Number of Lectures: 114

Number of Published Lectures: 114

Number of Curriculum Items: 114

Number of Published Curriculum Objects: 114

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Installing Python and necessary libraries for a seamless coding environment setup.

Mastering data type conversion and formatting techniques for consistent data representation.

Utilizing Pandas functions for efficient data manipulation tasks.

Implementing various types of join operations to merge datasets effectively.

Aggregating data and engineering new features for insightful analysis.

Handling date and time data effectively using Python libraries.

Creating customizable visualizations with libraries like Matplotlib and Seaborn for effective data communication.

Completing a capstone project: E-commerce data using concepts and skills learned from this course to create effective visualizations and communicate findings.

Who Should Attend

This course is designed for individuals with no prior experience in tools (e.g., R or Python).

For new graduates considering a data analytics career

For career switchers aiming to become data analysts or upgrade their skills beyond Excel spreadsheets.

Target Audiences

This course is designed for individuals with no prior experience in tools (e.g., R or Python).

For new graduates considering a data analytics career

For career switchers aiming to become data analysts or upgrade their skills beyond Excel spreadsheets.

Interested in becoming a Data Analyst? Want to gain practical skills and solve real-world business problems? Then this is the perfect course for you! This course is created by a Senior Data Analyst with 10 years of experience in Insurance and Health Care sectors. It will equip you with foundational knowledge and help you learn key concepts of loading data, data manipulation, data aggregation, and how to use libraries/packages in a simple manner.

I will guide you step-by-step into the World of Data Analysis. With every lecture and lab exercise, you will gain and develop an understanding of these concepts to tackle real data problems! This course primarily uses Python to solve labs and capstone project(s).

This course will be super useful and exciting. I’ve designed the course curriculum in the most natural, logical flow:

· Module 0 – Intro to Python: set up the Python environment and understand the basics of Python packages/libraries

· Module 1 – Load and Write Data: learn how to load and write data from flat files (e.g., .csv or Excel format)

· Module 2 – Data Types and Formatting: master the data types and learn how to convert data types for proper operations

· Module 3 – Data Manipulation: clean and preprocess data, perform sorting, ordering, and subsetting records

· Module 4 – Join Operations: learn how to perform joins using Python packages (e.g., pandas and SQL)

· Module 5 – Data Aggregation: learn how to aggregate data using summary statistics and perform feature engineering

· Module 6 – Time Intelligence: learn how to calculate business days and perform time dimension analysis

· Module 7 – Data Visualization: learn the basics of exploratory data analysis (EDA) and uni-variate/bi-variate visualizations

Each module contains independent content. Technically, you can take the course from start to end or jump into any specific topics of interest. However, I highly recommend students to take the course from Module 1 to 7 in order to complete the capstone project challenge!

This course is packed with real-world data/business problems that I solved during my career as a senior data analyst. You will learn not just concepts but also gain practical, hands-on experience from the course. Enroll today and take the first step towards mastering the art of data analysis using Python.

Course Curriculum

Chapter 1: Welcome to the Course

Lecture 1: What You Will Learn: Module 0

Lecture 2: 0_1. Lecture: Part A – Course Intro

Lecture 3: 0_2. Lecture: Part B – Download and Install Anaconda

Lecture 4: 0_3. Lecture: Part C – Launching Spyder IDE

Lecture 5: 0_4. Lecture: Part D – Python Libraries Introduction

Lecture 6: 0_5a. Lecture: Part E – Python Libraries Installation_Anaconda Navigator

Lecture 7: 0_5b. Lecture: Part E – Python Libraries Installation_Anaconda Prompt

Lecture 8: 0_5c. Lecture: Part E – Python Libraries Installation_Spyder IDE

Lecture 9: DOWNLOAD COURSE PACK: Datasets, Coding Exercises, Course Outline and Cheatsheet

Lecture 10: 0_6. Demo: Overview of Course Folder Structure

Lecture 11: 0_7. Demo: Part A – How to Download Anaconda

Lecture 12: 0_8. Demo: Part B – How to Install Anaconda

Lecture 13: 0_9. Demo: Part C – How to Navigate Anaconda Navigator

Lecture 14: 0_10. Demo: Part D – How to Launch Spyder

Lecture 15: 0_11. Demo: Part E – Install Python Libraries using Anaconda Prompt

Chapter 2: Load and Write Data

Lecture 1: What You Will Learn: Module 1

Lecture 2: 1_1. Lecture: Part A – Summary of Data Objects and Structures

Lecture 3: 1_2. Lecture: Part B – Define Path and Load Data

Lecture 4: 1_3. Lecture: Part C – Write Data

Lecture 5: 1_4. Welcome to Lab 1 Overview

Lecture 6: 1_5. Problem 1: Install Python Libraries and Packages

Lecture 7: 1_6. Problem 2: Define Folder Paths and Setup Directories

Lecture 8: 1_7. Problem 3: Load Data into Python Workspace

Lecture 9: 1_8. Problem 4: Write Data into Python Workspace Part 1

Lecture 10: 1_9. Problem 4: Write Data into Python Workspace Part 2

Lecture 11: 1_10. Extra Problem: Capture a Snapshot Date from Filenames

Chapter 3: Data Types and Formatting

Lecture 1: What You Will Learn: Moudle 2

Lecture 2: 2_1. Lecture: Data Types and Data Type Conversion in Python

Lecture 3: 2_2. Lecture: Check Column Names and Rename Columns

Lecture 4: 2_3. Lecture: Date Formatting – Year, Month, etc.

Lecture 5: 2_4. Lecture: Character Formatting – Add Leading Zeros

Lecture 6: 2_5. Welcome to Lab 2 Overview

Lecture 7: 2_6. Problem 1: Check Data Types

Lecture 8: 2_7. Problem 2: Rename Columns

Lecture 9: 2_8. Problem 3: Date Formatting

Lecture 10: 2_9. Problem 4: Add Leading Zeros

Chapter 4: Data Manipulation

Lecture 1: What You Will Learn: Module 3

Lecture 2: 3_1. Lecture: Clean Data (drop columns, remove duplicates)

Lecture 3: 3_2. Lecture: Clean Data (recode and replace values)

Lecture 4: 3_3. Lecture: Sort and Order Data

Lecture 5: 3_4. Lecture: Subset Data (Columns, List, Conditions)

Lecture 6: 3_5. Welcome to Lab 3 Overview

Lecture 7: 3_6. Problem 1: Cleaning Data

Lecture 8: 3_7. Problem 2: Recode and Replace Data

Lecture 9: 3_8. Problem 3: Arrange Data

Lecture 10: 3_9. Problem 4: Sort Data

Lecture 11: 3_10. Problem 5: Subset Data

Chapter 5: Join Data Operations

Lecture 1: What You Will Learn: Module 4

Lecture 2: 4_1. Lecture: What is Join and Types of Join

Lecture 3: 4_2. Lecture: Perform Joins with Pandas .merge()

Lecture 4: 4_3. Lecture: Perform Joins with pandasql library

Lecture 5: 4_4. Lecture: Advanced Join Temporal

Lecture 6: 4_5. Lecture: Advanced Join Subquery with Max()

Lecture 7: 4_6. Weclome to Lab 4 Overview

Lecture 8: 4_7. Problem 1: Perform Joins with Pandas .merge()

Lecture 9: 4_8. Problem 2: Perform Joins with pandasql library

Lecture 10: 4_9. Problem 3: Perform Joins on Multiple Tables

Lecture 11: 4_10. Problem 4: Advanced Join Temporal

Lecture 12: 4_11. Problem 5: Advanced Subquery Max()

Lecture 13: 4_12. Extra Problem: Identify Changes in Account Information

Chapter 6: Data Aggregation and Feature Engineering

Lecture 1: What You Will Learn: Module 5

Lecture 2: 5_1. Lecture: Summarize Data (count(), sum(), etc.)

Lecture 3: 5_2. Lecture: Filtering Data

Lecture 4: 5_3. Lecture: Slicing Data

Lecture 5: 5_4. Lecture: Convert a Summary Table Format

Lecture 6: 5_5. Lecture: Feature Engineering

Lecture 7: 5_6. Welcome to Lab 5 Overview

Lecture 8: 5_7. Problem 1: Summarize Data with Pandas

Lecture 9: 5_8. Problem 2: Filter and Slice Data with Pandas

Lecture 10: 5_9. Problem 3: Sort Data with Pandas

Lecture 11: 5_10. Problem 4: Convert a Summary Table Format

Lecture 12: 5_11. Problem 5: Feature Engineering

Chapter 7: Time Intelligence

Lecture 1: What You Will Learn: Module 6

Lecture 2: 6_1. Lecture: Calculate Time Features using Date Manipulation

Lecture 3: 6_2. Lecture: Calculate Event Sequence Analysis

Lecture 4: 6_3. Lecture: Calculate Number of Business Days

Lecture 5: 6_4. Lecture: Calculate KPIs with Different Frequencies

Lecture 6: 6_5. Welcome to Lab 6 Overview

Lecture 7: 6_6. Problem 1: Date Manipulation – Time Dimension

Lecture 8: 6_7. Problem 1: Date Manipulation – Durations

Lecture 9: 6_8. Problem 2: Calculate Event Sequence Analysis

Lecture 10: 6_9. Problem 3: Calculate Business Days using Pandas

Lecture 11: 6_10. Problem 4: Calculate a Measure at Daily Snapshot

Lecture 12: 6_11. Extra Problem: Calculate a Measure at Monthly Snapshot

Chapter 8: Data Visualization with matplotlib and seaborn

Lecture 1: What You Will Learn: Module 7

Lecture 2: 7_1. Lecture: Intro to Exploratory Data Analysis

Lecture 3: 7_2. Lecture: Uni-Variate: Bar Chart

Lecture 4: 7_3. Lecture: Uni-Variate: Pie Chart

Lecture 5: 7_4. Lecture: Uni-Variate: Line Chart

Lecture 6: 7_5. Lecture: Uni-Variate: Histogram

Lecture 7: 7_6. Lecture: Uni-Variate: Density Plot

Lecture 8: 7_7. Lecture: Bi-Variate: Box Plot

Instructors

Taesun Yoo
Senior Data Analyst | Online Course Instructor

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5 stars: 4 votes

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