At PeopleClick Learning our Data Analytics using Python course is thoughtfully designed to empower learners with the tools techniques and mindset required to explore clean analyze and visualize data all using Python. This course bridges the gap between business needs and data science capabilities by making learners job ready through a hands-on project based approach. The curriculum not only teaches technical skills but also develops analytical thinking to drive business impact using data.
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Skills Covered
We are providing the advanced course content with the corporate level of trainings with our industry experts.
- Data Analytics
- Power BI
- Tableu
- Python
- MySQL
Industry Relevant Tools
Course Description
Data Manipulation & Analysis with Python
This module introduces students to the Python programming language and its core libraries such as NumPy and Pandas. Learners will gain proficiency in importing cleaning and transforming data to make it analysis ready. From simple transformations to complex joins and aggregations this module lays the foundation for any kind of data driven decision making.
Data Visualization and Storytelling
Numbers tell a story and this module teaches you how to make it compelling. Students will learn to create powerful visualizations using libraries like Matplotlib Seaborn and Plotly. Through real datasets they’ll build dashboards explore patterns and develop the storytelling skills needed to communicate insights to stakeholders and decisionmakers.
Statistical Thinking for Business
Good data analysts don’t just describe what happened — they understand why. In this module learners explore descriptive and inferential statistics hypothesis testing and confidence intervals. These concepts help in making data backed decisions validating marketing strategies and identifying meaningful trends.
Working with Real Time & Business Datasets
To prepare learners for actual work scenarios students will analyze datasets from marketing finance ecommerce healthcare and operations. They’ll apply their knowledge to derive insights track KPIs and recommend solutions using structured project workflows that simulate real job challenges.
SQL for Data Analytics
Complementing Python skills this module teaches learners how to extract filter and manipulate data using Structured Query Language (SQL). Understanding SQL is essential for accessing data from relational databases — a skill in high demand across industries.
Business Intelligence & Dashboard Tools
This module focuses on using BI tools such as Power BI or Tableau. Students will learn how to build dashboards create visual reports and blend Python driven insights into professional presentations. By the end of this module learners will be able to deliver executive level dashboards and performance summaries.
Capstone Projects
Each student will complete a capstone project that involves cleaning a raw dataset performing analysis visualizing insights and presenting a summary to mimic real world analytics tasks. Projects include topics like churn prediction sales performance customer segmentation and marketing ROI analysis.
Entrepreneurship & Freelancing with Analytics
This module equips learners to monetize their data skills. Whether it’s launching a freelance analytics career or building a data product students will learn to validate ideas pitch clients and build a personal brand as a data analyst.
Course Curriculam
Module 1: Overview
- Why do we need Python?
- Program structure of Python
Module 2: Environment Setup
- Python Installation
- Execution Types
- What is an Interpreter?
- Interpreters vs Compilers
- Using the Python Interpreter
- Interactive Mode
- Running Python files
- Working with Python shell
- Multiple programming ways in Python
Module 3: Basic Concepts
- Types of Operators
- Python Arithmetic Operators
- Python Comparison Operators
- Python Assignment Operators
- Python Bitwise Operators
- Python Logical Operators
- Python Membership Operators (in, not in)
- Python Identity Operators (is, is not)
- Python Operator Precedence
- Data Types
- Variables
- Assigning Values to Variables
- Multiple Assignment
- Python Numbers
- Python Strings
- Accessing Values in Strings
- String Special Operators
- String Formatting Operator
- Triple Quotes
- Built-in String Operations
Module 4: Python Sequences and Functions
Lists
- Accessing Values in Lists
- Updating Lists
- Deleting List Elements
- Basic List Operations
- Indexing, Slicing, and Matrixes
- Built-in List Functions & Methods
Tuples
- Accessing Values in Tuples
- Updating Tuples
- Deleting Tuple Elements
- Basic Tuple Operations
- Indexing, Slicing, and Matrixes
- Built-in Tuple Functions
Sets & Dictionaries
- Creating Sets and Dictionaries
- Processing with Sets and Dictionaries
Loops and Decision Making
- if Statements
- if...else Statements
- Nested if Statements
- while Loop
- for Loop
- Nested Loops
- Loop Control Statements
- break Statement
- continue Statement
- pass Statement
Functions
- Defining a Function
- Calling a Function
- Pass by Reference vs Value
- Function Arguments
- Required Arguments
- Keyword Arguments
- Default Arguments
- Variable-length Arguments
- The return Statement
- Scope of Variables (Global vs. Local)
Module 5: Object-Oriented Programming (OOP)
- Basic OOP Concepts
- Creating Classes in Python
- Documented Strings
- Private Identifiers
- Constructors
- Inheritance
- Polymorphism
- Modules and Packages
- Framework vs Packages
- Why are Modules Used?
- Creating Modules
- The import Statement
- The from...import Statement
- The from...import * Statement
- Namespaces and Scoping
- The dir() Function
- The globals() and locals() Functions
- The reload() Function
- Packages in Python
- Advanced Functional Programming
- Decorators, Iterators, and Generators
- Anonymous Functions (lambda, map, filter, reduce)
Module 6: File Manipulation and Database Connection
File Handling
- Opening Text Files
- Working with Files in Python
- The open() Function
- File Modes
- File Object Attributes
- close() Method
- write() Method
- read() Method
- Reading Files
- Renaming & Deleting Files
- Writing into a File
- remove() Method
- Working with Directories
Database Connection
- Download and Install MySQL Workbench
- Creating the Connection String for MySQL with Python
- Reading and Processing Tables from Python
- Executing SQL Queries from Python
Module 7: Errors and Exception Handling
- Standard Exceptions
- Assertions in Python
- The assert Statement
- What is an Exception?
- Handling an Exception
- The except Clause with No Exceptions
- The except Clause with Multiple Exceptions
- The try-finally Clause
- Arguments of an Exception
- Additional Topics
- Regular Expressions
- Powerful Utilities
- Multithreading
- List Comprehensions
- Shallow and Deep Copy
Module 8: Pandas, NumPy, and Matplotlib
- Importing pandas, numpy, and matplotlib Libraries
- Creating Arrays, Series, and DataFrames
- Processing with DataFrames and Series
- Reading .csv, .xls Files using Pandas
- Inbuilt Functions
- Workaround Projects
- Processing Transaction Details from a Store
- Detailed Mark Analysis Using Pandas Join
Module 9: Projects
- Domain: Banking (Introduction to Python) – Checking eligibility of customers for credit card for Cred-Pay
- Domain: Social Media / Entertainment (EDA using Python) – MovieLens Case Study
- Domain: Automobile (EDA using Python) – Uber Trips Analysis
- Domain: Social Media / Entertainment (Lexicon Analyzer) – Product Review Sentiment Analysis
- Domain: E-commerce (Complete Analysis & Visualization) – Food Hub Analysis
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