Python For Data Analysis - Syllabus
Python is essential for data analysis due to its simplicity, versatility, and powerful libraries like Pandas, NumPy, and Matplotlib. It enables efficient data manipulation, visualization, and statistical modeling. With Python, analysts can automate workflows, handle large datasets, and gain actionable insights, making it a top choice in data-driven fields.
Omegatech.ai
5/8/20241 min read
Python & Data Analysis Curriculum
Intro to Python
Introduction to Python
Variables
Functions
Python Operators
Programming Constructs in Python
Python Flow Controls
Conditional Statements
Loops
Python Objects
Python Collection Objects
String
List
Tuple
Dictionary
List Comprehension + Functions
List Comprehension
User-defined Functions
Function Arguments
Built-in Functions
Lambda Functions
Numpy
Introduction to Numpy
Numpy Array
Creating Numpy Array
Array Attributes
Accessing Array
Array Slicing
Array Reshaping
Array Operations
Iterating through Arrays
Pandas
Introduction to Pandas
Series
DataFrame
DataFrame Operations
Accessing Elements
Conditional Filtering
Aggregation
Changing Index
Converting to Series
Data Frame Manipulation
Pandas DataFrame - Introduction
DataFrame Creation
Reading Data from Various Files
Understanding Data
Accessing DataFrame Elements using Indexing
DataFrame Slicing
Selection Conditions
DataFrame Operations
DataFrame Joins
Broadcasting
Combining DataFrames
Chain Indexing
Pivot Tables
Checking Duplicates
Dropping Rows and Columns
Groupby
Checking Null Values
Missing Values Analysis & Treatment
Visualization - Part 1
Visualizations using Matplotlib
Line Plot
Figure and Subplots
Multiple Subplots
Histogram
Pie Chart
Scatter Plot
Visualization - Part 2
Visualizations using Seaborn
Strip Plot
Distribution Plot
Box Plot
Violin Plot
Line Plot
Bar Plot
Count Plot
Heat Map
EDA
Summary Statistics
Handling Missing Values
Advance Analysis using GroupBy
Advanced Data Exploration