Data Science
Beginner
Python for Data Science
A path into data science for people who are new to Python itself: language fundamentals and file handling first, then NumPy, pandas and Matplotlib applied to CSV, Excel and JSON datasets.
30 chapters
2h 30m
What you'll learn
- Introduction to Python for Data Science
- Installing Python and Data Science Environment
- Python Basics for Data Science
- Variables, Data Types, and Operators
- Conditional Statements and Loops
- Functions and Modules in Python
- Working with Strings and Lists
- Tuples, Sets, and Dictionaries
Course content
30 chapters · 2h 30m- 1 Introduction to Python for Data Science 5 min
- 2 Installing Python and Data Science Environment 5 min
- 3 Python Basics for Data Science 5 min
- 4 Variables, Data Types, and Operators 5 min
- 5 Conditional Statements and Loops 5 min
- 6 Functions and Modules in Python 5 min
- 7 Working with Strings and Lists 5 min
- 8 Tuples, Sets, and Dictionaries 5 min
- 9 File Handling in Python 5 min
- 10 Introduction to NumPy 5 min
- 11 NumPy Arrays and Operations 5 min
- 12 NumPy Broadcasting and Vectorization 5 min
- 13 Introduction to Pandas 5 min
- 14 Pandas Series and DataFrames 5 min
- 15 Data Cleaning with Pandas 5 min
- 16 Data Analysis and Aggregation 5 min
- 17 Working with CSV, Excel, and JSON Data 5 min
- 18 Data Visualization with Matplotlib 5 min
- 19 Statistical Visualization with Seaborn 5 min
- 20 Exploratory Data Analysis (EDA) 5 min
- 21 Introduction to Machine Learning 5 min
- 22 Data Preprocessing for Machine Learning 5 min
- 23 Regression Algorithms 5 min
- 24 Classification Algorithms 5 min
- 25 Model Evaluation Techniques 5 min
- 26 Working with APIs and Web Data 5 min
- 27 Real-World Data Science Projects 5 min
- 28 Python for Data Science Interview Preparation 5 min
- 29 Advanced Data Science Techniques 5 min
- 30 Final Projects and Real-World Applications 5 min