Python for Data Analytics
Week 1: Python Fundamentals
Introduction to Python in Data Analytics
Installing Python, IDEs
Variables, Data Types, and Type Conversion
Arithmetic, Logical & Comparison Operators
Print statements,
input(),type()
Week 2: Control Flow & Loops
Conditional Statements: if, elif, else
Loops: for, while,
range()Loop control: break, continue, pass
Real-world use: looping through dataset rows
Week 3: Data Structures
Lists, Tuples, Sets, Dictionaries
Indexing, Slicing, Nested Data Structures
Built-in functions like
len(),sorted(),sum()List and Dictionary Comprehensions
Week 4: Functions
Defining and Calling Functions
Positional, Keyword, and Default Arguments
Return values, Scope of Variables
Lambda Functions
Writing Reusable Analytics Functions
Week 5: Object-Oriented Programming (OOP)
Why OOP in Analytics
Creating Classes and Objects
__init__()constructorInstance Variables and Methods
Use Case: Creating a Data Preprocessing Class
Week 6: File & Exception Handling
Reading/Writing CSV, TXT, JSON
Using
with open(),readlines(),write()Try, Except, Finally
Use Case: Reading and Cleaning a File with OOP
Week 7: NumPy for Numerical Analysis
Why NumPy: Speed & Efficiency
Arrays, Array Slicing, Reshaping
Mathematical Operations on Arrays
Broadcasting and Vectorization
Aggregation:
mean(),sum(),std()
Week 8: Pandas for Data Manipulation
Series and DataFrame Creation
Importing Data (CSV, Excel)
Data Cleaning: Missing Values, Duplicates
Filtering, Sorting, GroupBy
Use Case: Build a DataAnalyzer Class
Week 9: Data Visualization
Matplotlib: Line, Bar, Pie, Scatter
Seaborn: Countplot, Boxplot, Heatmap, Pairplot
Customizing Titles, Legends, Colors
Plotly for Interactive Charts
Create Plotter class for automation
Week 10: Exploratory Data Analysis (EDA)
Statistical Measures: Mean, Median, Mode, Std
Correlation Matrix
Outlier Detection (Z-score, IQR)
Combining Pandas + Visualization
Mini Project: Sales or Survey Dataset