#Python Analyst

A Python Analyst uses Python programming to analyze, visualize, and interpret data for business insights. They work with libraries like Pandas, NumPy, Matplotlib, and Seaborn to handle and explore datasets efficiently. They collaborate with teams to translate raw data into actionable strategies and reports. This role is in high demand across sectors like finance, healthcare, e-commerce, and tech.

Training Highlights

✅ Hands-On Coding Practice
✅ Real-Time Banking / Finance Case Study
✅ Python Fundamentals to Advanced Analytics
✅ Pandas & NumPy Data Handling
✅ SQL Database Integration
✅ Data Cleaning, Transformation & Analysis
✅ AI-Assisted Debugging
✅ Job-Oriented, Career-Relevant Skills

Modules We learn

✅ Module 1: Python Analytics
✅ Module 2: Real-time Project

Course Content
FAQs

Python Analyst
Course Contents:

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Module 1 : Python Analytics

Chapter 1: Python Introduction

Python introduction
Python versions
Python job roles
Role of Python in data projects

Chapter 2: Python Architecture

Python architecture
PVM: Python Virtual Machine
Compiler and byte code
Execution process
Python implementations

Chapter 3: Python Installations

Python installation
Anaconda installation
Python IDE and usage
Jupyter Notebooks

Chapter 4: Python Print Statement

Python print statement
Using print()
Testing case sensitivity
Single-line and multi-line output
Working with single quotations
Debugging with AI assistants

Chapter 5: Python Variables

Python variables
Assigning values
Purpose and naming rules
Reading variable values
Multiple variables and print()

Chapter 6: Python Operators & String Literals

Arithmetic operators
Python string literals
Single and double quotes
Formatted strings (f-strings)
Comparison and indexing operators

Chapter 7: Python Data Types

Python data types
Integer, float and string data types
Type casting
Type identification
Multiple-value assignments

Chapter 8: Python Lists

Creating Python lists
Printing list items
List slicing
Length and type
Empty lists and append
Loops and list updates

Chapter 9: Python Dictionaries

Python dictionaries
Indexing dictionaries
Editing key values
Lists inside dictionaries
Delete and clear operations

Chapter 10: Python Tuples

Defining and indexing tuples
len() and type()
Mixed values in tuples
Tuple reassignment / overwriting concepts

Chapter 11: IF…ELSE Conditions

if / else conditions
if / elif / else and shorthand if
Composite conditions
Indentation and pass statement
in and negation operators
Range-based conditions

Chapter 12: Python Loops

for loops
for loop with range
for loop with sequence values
while loop and exit conditions
Nested loops
Control statements
break, continue and pass

Chapter 13: Python File Handling

File handling activities
Open, read, write and close files
Appending and overwriting
Using os and path.exists
File read/write methods

Chapter 14: Python DataFrames

DataFrame creation
Pandas DataFrames
DataFrames from a single list
DataFrames from dictionaries
Displaying DataFrames and list items
Identifying and replacing nulls; NumPy usage

Chapter 15: Python SQL Database Access

SQL database access with Python
Pandas DataFrame integration
pyodbc module and SQL functions
SQL database cursor connections
SQL query execution: DDL and DML
Filters and aggregations with SQL
DataFrame usage with SQL

Chapter 16: DataFrame Transformations – I

DataFrame transformations
Concat and append concepts
Merge function
Joining multiple DataFrames
Data type checks and conversions
Loops with DataFrames

Chapter 17: DataFrame Transformations – II

Pandas data cleaning
Replace and transform columns
Data discovery and column fill
Identify and remove duplicates
dropna() and fillna() functions

Chapter 18: DataFrame Transformations – III

Python functions for date and time
now() function
Date and time functions
Calendar data generation

Chapter 19: Python Functions & Lambda

Python functions and usage
Function parameters
Default and list parameters
Python lambda functions
Recursive functions and usage
Return and output with lambda

Chapter 20: Python Classes, Objects & OOP

Class variables and instances
Built-in class attributes
Objects and constructors
self variable / modifiers
Python garbage collection
Hierarchical inheritance
Multilevel, multiple and hybrid inheritance
Overloading and overriding
Polymorphism and abstraction

Chapter 21: Regular Expressions

Regular expression patterns
Literals and repetition cases
Groups and grouping
Common expressions such as \w+, ^ and \s
re.split()
re.match()
re.search() and re.findall() for text

Module 2 : Real-time Project

Real-time Case Study: Banking / Finance Data Analysis
The curriculum concludes with a real-time case study that brings together the core Python analytics skills covered
throughout the course. Learners apply programming, file/data handling, dataframe operations, SQL connectivity and
transformation techniques in a practical data-analysis scenario.