● Introduction to Data Science and Python
● What is Data Science?
● Why is Data Science Important?
● The Role of Python in Data Science
● Why Python for Data Science?
● Beyond Technical Advantages
● Setting Up Your Python Environment (Anaconda, Jupyter Notebooks)
● Basic Python Syntax and Data Types (Numbers, Strings, Booleans, Lists, Tuples, Dictionaries)
● Control Flow Statements (if, else, for, while)
● Functions and Modules
CHAPTER 2
● Essential Tools for Data Exploration and Analysis
● The IPython Shell and Jupyter Notebooks for Interactive Computing
● Choosing Between IPython Shell and Jupyter Notebooks
● Version Control with Git (Optional)
● Learning Resources
● Data Visualization Libraries (Matplotlib, Seaborn) (Introduction only, detailed use covered later)
CHAPTER 3
● Intermediate Python Programming for Data Science
● Object-Oriented Programming (Classes and Objects)
● Introduction to Object-Oriented Programming (OOP)
● Advantages of OOP in Data Science
● Working with Files and Exceptions
● Regular Expressions for Text Manipulation
● NumPy Fundamentals: Arrays and VectorizedOperations (Detailed coverage)
● Introduction to NumPy Arrays
CHAPTER 4
● Deep Dive into NumPy Arrays
● Creating Arrays from Various DataStructures
● Creating Arrays from Various Data Structures
● Array Attributes (Shape, Dtype, Indexing and Slicing)
● Mathematical Operations on Arrays (Element-wise and Universal Functions)
● Array Broadcasting for Efficient Calculations
● Linear Algebra with NumPy (Matrices, Vectors,
● Dot Product, Linear Systems)
● Random Number Generation for Simulations
CHAPTER 5
● Advanced NumPy Techniques
● Fancy Indexing and Selection for Complex Data Access
● Fancy Indexing: Fine-Grained Selection
● Array Reshaping and Transpose Operations
● Working with Multidimensional Data (NDArrays)
● Handling Missing Data with NumPy
● (NA values)
● File I/O with NumPy (Loading and Saving Data)
CHAPTER 6
● Performance Optimization with NumPy
● Vectorization vs. Loops for Efficiency
● Profiling Code to Identify Bottlenecks
● Leveraging NumPy with Other Powerful Libraries
CHAPTER 7
● Introduction to Pandas Data Structures
● Series: One-Dimensional Labeled Data
● DataFrames: Two-Dimensional Labeled Data with Columns
● Accessing Data within a DataFrame
● Creating DataFrames from Various Sources (Lists, Dictionaries, CSV Files)
● Indexing, Selection, and Accessing Data in DataFrames
CHAPTER 8
● Essential Data Manipulation with Pandas
● Handling Missing Data Cleaning and Imputation Techniques
● Data Transformation (Filtering, Sorting, Grouping)
● Merging and Joining DataFrames for Combining Datasets
● Reshaping and Pivoting Data for Different Views
CHAPTER 9
● Working with Time Series Data with Pandas
● DatetimeIndex and Time Series Operations
● Resampling and Time-Based Aggregations
● Date and Time Manipulation Techniques
● Analyzing Time Series Data with Pandas Tools
CHAPTER 10
● Data Exploration and Visualization with Pandas
● Creating Informative Visualisations with Pandas
● (Building on prior Matplotlib/Seaborn intro)
● Grouping and Aggregation for Deep Data Insights
● Handling Categorical Data with Pandas
CHAPTER 11
● High-Performance Data Analysis with Pandas
● Vectorized Operations and Performance Considerations
CHAPTER 12
● Case Study 1: [Specific Data Science Domain] Analysis with Python
● Problem Definition and Data Acquisition
CHAPTER 13
● Data Cleaning, Exploration, and Feature Engineering with Python Libraries
● Data Cleaning with Pandas and NumPy
CHAPTER 14
● Model Building and Evaluation (NumPy & Pandas for Data Prep)
Appendix
CONCLUSION