Advanced Data Analytics using Python

This unit serves as a primer for data science, introducing key tools and concepts. It’s designed to equip non-programmers with foundational Python skills, facilitating a deeper understanding and practical application throughout the course.

Module 1: Introduction to Data Analysis

• Introduction to tools, key concepts, and definitions

• Real-time project applications in different domains

• Practical applications of data science in various industries

Module 2: Fundamentals of Python Programming

• Introduction to Python tools

• Installation of Python

• Python fundamentals

Tools Covered: Python, Anaconda, Jupyter, Google Colab

Module 3: Fundamentals of Statistics

• Importance and use of statistics in data science

• Descriptive statistics & predictive statistics

• Learn how predictive statistics connects with machine learning

Note:
Modules 2 and 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

This unit provides an extensive roadmap for building a robust portfolio in data science. You’ll master GitHub, a version control system, for efficient collaboration and project management. Additionally, you’ll harness LinkedIn‘s power for networking and career advancement.

Module 1: Git & GitHub (VCS)

• Introduction to Version Control Systems
• Installing and Configuring Git
• Git Essentials

• Branching and Merging
• GitHub Essentials

• Collaborating on GitHub
• Forking repositories
• Creating pull requests

• Best Practices and Workflows

Application: Initiate, collaborate, and work on a real-time project

Tools Covered: Git, GitHub

Module 2: LinkedIn Profile building

• Introduction to LinkedIn as a Professional Networking Platform
• Crafting a Compelling LinkedIn Profile

• Leveraging LinkedIn Features for Engagement

• Growing Your Network on LinkedIn

• Increasing Followers and Engagement

• Enhancing Professional Branding on LinkedIn

• Leveraging LinkedIn for Career Advancement

This Python course introduces fundamental to advanced concepts tailored for data science and AI applications. Learn Python step by step—from basics to advanced. Master essential libraries, functions, and modules to perform data science projects by analyzing data and building ML & AI models using Python.

Module 1: Core Python Programming

• Python Environment

• Data Types & Operators

• Operators & Loop Controls

Project: Build a Simple Calculator

Module 2: Advanced Python Programming

• Functions & Modules

• Regular Expressions (RegEx)

• File Handling & Exception Handling

• Generators & Decorators

Class Hands-on:
25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, RegEx, and exception handling

Module 3: Web Scraping using Python

• Introduction to Web Scraping

• Web Requests & HTTP

• Parsing HTML with Beautiful Soup

Project: Scrape and Analyze Data from a Website (2–3 projects)

Module 4: OOPs in Python

• Classes and Objects

• Encapsulation, Inheritance, and Polymorphism

• Abstraction and Interfaces

• Method Overriding and Overloading

• Class Variables and Instance Variables

Module 5: Data Analytics using Python

• Data Analytics Fundamentals

• Data Analysis using NumPy (Array Operations)

• Data Analysis using Pandas (on DataFrames)

• Data Visualization using Matplotlib

• Data Visualization using Seaborn

Tools Covered: NumPy, Pandas, Matplotlib, Seaborn, Beautiful Soup

EDA Project: Create insights using data analytics

Capstone:
2 full-length projects on data analytics using Pandas, Matplotlib & Seaborn to analyze data, gain insights, and identify patterns.

Learn practical data mining using SQL databases, data visualization and analytics using Power BI & Tableau, and data handling using Excel.

Module 1: SQL – Structured Query Language

• Introduction to SQL

• SQL & RDBMS

• SQL Syntax and Data Types

• CRUD Operations in SQL

• Retrieving Data with SQL

• Filtering, Sorting & Formatting Query Results

• Advanced SQL Queries

• Database Design and Normalization

• Advanced Database Concepts

• Stored Procedures

• Integrating SQL with Python for Data

Hands-on Practice:

• Joins, Sub-queries, Aggregation Queries

• Views, Filtering, Sorting

• Group By and Having Clause

Module 2: Power BI

• Introduction to Power BI

• Data Preparation and Modeling

• Clean, Transform & Load Data in Power BI

• Data Visualization Techniques

• Advanced Analytics in Power BI

• Designing Interactive Dashboards

• Power Query

• Design Power BI Reports

• Connecting Power BI to SQL

• Create, Share, and Collaborate on Power BI Dashboards

Class Projects & Assignments:

• Project 1: Education Institute’s Student Data Analysis

• Project 2: Sales Data Analysis

• Learn to visualize data to find patterns & insights using interactive charts

Module 3: Tableau

• Introduction to Tableau

• Connecting Tableau to Data Sources

• Data Types in Tableau

• Data Preparation and Transformation

• Building Visualizations in Tableau

• Advanced Analytics in Tableau

• Tableau Dashboards and Storytelling

• Connecting Tableau to SQL

• Tableau Online to Collaborate, Share & Publish Dashboards

Class Projects & Assignments:

• Project 1: Supermarket Data Analysis

• Project 2: COVID Data Analysis

• Learn to visualize data to find patterns & insights using interactive charts

• Deployment of Predictive Model in Tableau

Tools Covered: Power BI, Tableau, Excel

Module 4: Excel for Analytics

• Introduction to Excel for Analytics

• Basic Formulas & Functions

• Data Preparation and Cleaning

• Charts & Graphs in Excel

• Data Analysis Techniques in Excel

• PivotTables and PivotCharts for Data Summarization

• Data Visualization Techniques in Excel

• Excel’s Data Analysis Add-ons

Learn modern techniques of Data Analysis using Python and popular open-source libraries like pandas, scikit-learn, and NumPy to transform data into insights.

Module 1: Introduction

• Introduction to Data Analysis with Python

• The Problem

• Understanding the Data

• Python Packages for Data Science

• Importing and Exporting Data in Python

• Getting Started Analyzing Data in Python

• Lab 1

• Graded Review Questions (7 Questions)

Module 2: Data Wrangling

• Pre-processing Data in Python

• Dealing with Missing Values in Python

• Data Formatting in Python

• Data Normalization in Python

• Binning in Python

• Turning Categorical Variables into Quantitative Variables in Python

• Lab 2

• Graded Review Questions (6 Questions)

Module 3: Exploratory Data Analysis

• Exploratory Data Analysis

• Descriptive Statistics

• GroupBy in Python

• Analysis of Variance (ANOVA)

• Correlation

• Correlation – Statistics

• Lab 3

• Graded Review Questions (5 Questions)

Module 4: Model Development

• Model Development

• Linear Regression and Multiple Linear Regression

• Model Evaluation Using Visualization

• Polynomial Regression and Pipelines

• Measures for In-Sample Evaluation

• Prediction and Decision Making

• Lab 4

• Graded Review Questions (5 Questions)

Module 5: Model Evaluation

• Model Evaluation and Refinement

• Model Evaluation

• Overfitting, Underfitting, and Model Selection

• Ridge Regression

• Grid Search

• Lab 5

• Graded Review Questions (5 Questions)

• Final Assessment

• Final Exam (21 Questions)