Learning goals
- Write Python code for data analysis
- Clean and prepare datasets
- Analyze data using Pandas
- Create effective visualizations
- Understand statistical concepts
- Build basic machine learning models
- Interpret and communicate insights
- Complete end-to-end data projects
Course description
Learn the complete data science workflow from collecting and cleaning data to analyzing trends and building predictive models. This beginner-friendly course introduces Python, data visualization, statistical thinking, and machine learning through practical projects that mirror real-world business and research scenarios.
Requirements
- Basic computer skills
- No programming experience required
- Python installation recommended
- Google Colab or Jupyter Notebook access
Intended audience
- Students
- Aspiring Data Analysts
- Aspiring Data Scientists
- Business Professionals
- Researchers
- Career Switchers
Course curriculum
01 Python for Data Analysis
-
Introduction to Python
-
Working with Data Structures
-
Introduction to Pandas
-
Python Fundamentals
-
Pandas & Data Structures
02 Data Cleaning & Visualization
-
Handling Missing Data
-
Data Visualization Fundamentals
-
Using Matplotlib and Seaborn
-
Data Cleaning
-
Visualization Assessment
03 Introduction to Machine Learning
-
What Is Machine Learning?
-
Supervised vs Unsupervised Learning
-
Building Your First Model
-
Machine Learning Basics
-
Model Building Assessment
Meet your instructor
Rating
9
Reviews
1
Students
10
Courses
Keep learning
Related Courses.
Free
admin
Free
Free
