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Python for Data Analysis: Students and Professionals

person icon Taesun Yoo

4.4

Python for Data Analysis: Students and Professionals

Most Honest Crash Course to Become a Data Analyst using Python by Solving Real-World Data Problems

updated on icon Updated on Jul, 2024

language icon Language - English

person icon Taesun Yoo

category icon Python,Data Analysis,Data Visualization

Lectures -114

Resources -1

Duration -11.5 hours

4.4

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Course Description

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.

Goals

  • 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.

Prerequisites

  • Operating Systems: 64-bit versions of Microsoft Windows 7, 8.1 and 10 or Mac
  • Installation of Python and necessary libraries using Anaconda
  • No prior experience in Python but highly desirable to know some basic analytics with Excel
Python for Data Analysis: Students and Professionals

Curriculum

Check out the detailed breakdown of what’s inside the course

Welcome to the Course
15 Lectures
  • play icon What You Will Learn: Module 0
  • play icon 0_1. Lecture: Part A - Course Intro 06:20 06:20
  • play icon 0_2. Lecture: Part B - Download and Install Anaconda 02:25 02:25
  • play icon 0_3. Lecture: Part C - Launching Spyder IDE 04:16 04:16
  • play icon 0_4. Lecture: Part D - Python Libraries Introduction 02:06 02:06
  • play icon 0_5a. Lecture: Part E - Python Libraries Installation_Anaconda Navigator 01:11 01:11
  • play icon 0_5b. Lecture: Part E - Python Libraries Installation_Anaconda Prompt 01:26 01:26
  • play icon 0_5c. Lecture: Part E - Python Libraries Installation_Spyder IDE 03:05 03:05
  • play icon DOWNLOAD COURSE PACK: Datasets, Coding Exercises, Course Outline and Cheatsheet
  • play icon 0_6. Demo: Overview of Course Folder Structure 05:39 05:39
  • play icon 0_7. Demo: Part A - How to Download Anaconda 02:15 02:15
  • play icon 0_8. Demo: Part B - How to Install Anaconda 03:00 03:00
  • play icon 0_9. Demo: Part C - How to Navigate Anaconda Navigator 03:46 03:46
  • play icon 0_10. Demo: Part D - How to Launch Spyder 05:26 05:26
  • play icon 0_11. Demo: Part E - Install Python Libraries using Anaconda Prompt 08:31 08:31
Load and Write Data
11 Lectures
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Data Types and Formatting
10 Lectures
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Data Manipulation
11 Lectures
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Join Data Operations
13 Lectures
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Data Aggregation and Feature Engineering
12 Lectures
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Time Intelligence
12 Lectures
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Data Visualization with matplotlib and seaborn
20 Lectures
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Capstone Projects
7 Lectures
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CONGRATULATIONS!!!
3 Lectures
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Instructor Details

Taesun Yoo

Taesun Yoo

Taesun is a seasoned data analytics specialist with a wealth of experience in managing large-scale projects. His expertise spans from overseeing data quality and integration testing to crafting informed ad-hoc reports and developing insightful analytics products. Additionally, he serves as a freelance mentor and is the proud owner of the YouTube Channel 'DataScienceOne,' dedicated to showcasing data science projects for aspiring enthusiasts and career-switchers. Taesun's vision is to influence and educate as many individuals as possible, guiding them to launch their first data analytics career.

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