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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