Python for Data Analysis: Students and Professionals
Most Honest Crash Course to Become a Data Analyst using Python by Solving Real-World Data Problems
Python,Data Analysis,Data Visualization
Lectures -114
Resources -1
Duration -11.5 hours
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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
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Curriculum
Check out the detailed breakdown of what’s inside the course
Welcome to the Course
15 Lectures
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What You Will Learn: Module 0
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0_1. Lecture: Part A - Course Intro 06:20 06:20
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0_2. Lecture: Part B - Download and Install Anaconda 02:25 02:25
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0_3. Lecture: Part C - Launching Spyder IDE 04:16 04:16
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0_4. Lecture: Part D - Python Libraries Introduction 02:06 02:06
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0_5a. Lecture: Part E - Python Libraries Installation_Anaconda Navigator 01:11 01:11
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0_5b. Lecture: Part E - Python Libraries Installation_Anaconda Prompt 01:26 01:26
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0_5c. Lecture: Part E - Python Libraries Installation_Spyder IDE 03:05 03:05
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DOWNLOAD COURSE PACK: Datasets, Coding Exercises, Course Outline and Cheatsheet
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0_6. Demo: Overview of Course Folder Structure 05:39 05:39
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0_7. Demo: Part A - How to Download Anaconda 02:15 02:15
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0_8. Demo: Part B - How to Install Anaconda 03:00 03:00
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0_9. Demo: Part C - How to Navigate Anaconda Navigator 03:46 03:46
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0_10. Demo: Part D - How to Launch Spyder 05:26 05:26
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0_11. Demo: Part E - Install Python Libraries using Anaconda Prompt 08:31 08:31
Load and Write Data
11 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Types and Formatting
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Manipulation
11 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Join Data Operations
13 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Aggregation and Feature Engineering
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Time Intelligence
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Visualization with matplotlib and seaborn
20 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Capstone Projects
7 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
CONGRATULATIONS!!!
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![Taesun Yoo](https://www.tutorialspoint.com/assets/profiles/1166350/profile/200_3543859-1715630906.png)
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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