Description
Introduction of Python Programming for Data Enthusiasts
This course is tailored for individuals passionate about working with data and eager to dive deeper into Python programming. Whether you’re looking to enhance your data manipulation skills or aspiring to transition into a data-centric role, this course will equip you with the necessary Python knowledge and tools. By the end of the course, you’ll be proficient in using Python to analyze, manipulate, and visualize data, preparing you for more advanced data science and analytics projects.
Pre-requisites
Before starting this course, it’s recommended to have:
- Basic Python Knowledge: Understanding of basic Python syntax, variables, data types, and simple control flow (if statements, loops).
- Basic Understanding of Data: Familiarity with basic data concepts, including data types (numeric, categorical) and structures (tables, lists).
- Basic Computer Literacy: Ability to navigate files, use a text editor, and understand file paths.
- No Prior Data Science Experience Needed: While data science experience isn’t required, an interest in data and analytics will be beneficial.
Table of Contents
- Advanced Python Basics
1.1 Review of Core Python Concepts
1.2 Advanced Data Types and Structures
1.3 Pythonic Code: Best Practices and Idioms - Data Handling and Manipulation
2.1 Introduction to NumPy (Ref: NumPy for Data Science and Machine Learning in Python)
2.2 Array Operations and Manipulations
2.3 Data Handling with Pandas
2.4 Advanced DataFrame Operations
2.5 Merging, Joining, and Concatenating Data - Data Cleaning and Preparation
3.1 Identifying and Handling Missing Data
3.2 Data Transformation and Normalization
3.3 Working with Dates and Times
3.4 String Operations and Text Data Processing - Exploratory Data Analysis (EDA)
4.1 Introduction to EDA
4.2 Descriptive Statistics with Python
4.3 Visualizing Data Distributions
4.4 Grouping, Aggregation, and Pivoting in Pandas - Data Visualization Techniques
5.1 Advanced Plotting with Matplotlib
5.2 Seaborn for Statistical Plots
5.3 Customizing and Styling Visualizations
5.4 Creating Interactive Visualizations - Working with External Data Sources
6.1 Reading and Writing CSV, Excel, and JSON Files
6.2 Web Scraping Basics with BeautifulSoup
6.3 Accessing APIs and Handling JSON Data
6.4 Introduction to Databases and SQL with Python - Introduction to Machine Learning
7.1 What is Machine Learning?
7.2 Introduction to Scikit-Learn
7.3 Building and Evaluating Simple Models
7.4 Introduction to Feature Engineering - Automation and Scripting
8.1 Writing Python Scripts for Data Tasks
8.2 Automating Data Pipelines
8.3 Scheduling Python Scripts - Final Project
9.1 Applying Course Concepts to a Real-World Data Project
9.2 Analyzing and Visualizing Data
9.3 Presenting Your Findings - Conclusion and Next Steps
10.1 Summary of Key Learnings
10.2 Resources for Further Learning in Data Science and Machine Learning
10.3 Pathways to Advanced Data Science and Analytics
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