Introduction to Data Science in Python Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to data science using Python, designed for learners with some programming experience. Through a blend of theory and hands-on practice, you'll learn essential data manipulation, cleaning, analysis, and statistical techniques using core Python libraries. The course spans approximately 34 hours of content, divided into four core modules and a final project, allowing flexible pacing suitable for working professionals. By the end, you’ll complete a practical project demonstrating your ability to analyze real-world datasets using Python.
Module 1: Fundamentals of Data Manipulation with Python
Estimated time: 13 hours
- Introduction to Python programming for data science
- Working with functions and control flow
- Handling sequences: lists, tuples, and strings
- Reading and writing CSV files
- Introduction to NumPy for numerical computing
Module 2: Introduction to pandas
Estimated time: 7 hours
- Understanding pandas Series and DataFrame objects
- Creating and inspecting DataFrames
- Selecting and filtering data
- Indexing and label-based data access
Module 3: Data Wrangling with pandas
Estimated time: 7 hours
- Handling missing data and filtering strategies
- Merging and joining datasets
- Reshaping and pivoting DataFrames
- Transforming data using apply and map functions
Module 4: Basic Data Analysis with pandas
Estimated time: 7 hours
- Applying descriptive statistics to datasets
- Grouping data and using aggregate functions
- Creating pivot tables for multidimensional analysis
- Conducting t-tests for hypothesis testing
Module 5: Final Project
Estimated time: 10 hours
- Load and inspect a real-world dataset
- Clean and transform data using pandas
- Perform exploratory data analysis and statistical testing
Prerequisites
- Familiarity with basic Python programming concepts
- Experience with functions, loops, and data types in Python
- Basic understanding of mathematical and statistical concepts
What You'll Be Able to Do After
- Write Python code to manipulate and analyze structured data
- Use pandas to clean and transform real-world datasets
- Apply statistical methods to test hypotheses on data
- Combine NumPy and pandas for efficient data analysis
- Complete a data analysis project from start to finish