ADDA

Overview

Through hands-on exercises and practical examples, participants will develop a versatile skill set for data analytics and reporting.

Objective

The objective of this Data Analytics and Visualization course is to provide participants with a solid foundation in data analytics. The course aims to equip learners with the skills needed to efficiently analyze and visualize data for effective decision-making.

Target Audience

Diploma / Any Graduate

Prerequisites

No prior coding experience is required.

Course Coverage

Data Management

Excel

• Introduction
• Formatting Worksheet
• Conditional Formatting
• Pivot Tables and Charts
• Formulas and Functions (VLOOKUP, INDEX-MATCH, SUMIF, etc.)
• Data Cleaning and Transformation
• Data Validation and Sorting
• Macros
• Excel VBA and User Forms
• Connect with External Data
• Creating an Automation Project

MySQL

• Creating and Modifying Tables
• Data Manipulation (INSERT, UPDATE, DELETE)
• Querying Databases (SELECT, JOIN, GROUP BY)
• Aggregation Functions (SUM, AVG, COUNT)
• JOIN and Subqueries

MongoDB

• Introduction to NoSQL Databases
• CRUD Operations
• Data Indexing & Aggregation
• Working with Unstructured Data

Data Visualization

Matplotlib

• Basic Plotting (Line Plots, Scatter Plots, Bar Plots)
• Customizing Plots (Labels, Titles, Legends)
• Multiple Subplots
• Histograms and Box Plots
• 3D Plotting

Power BI

• Connecting to Data Sources
• Data Transformation and Cleaning
• Building Visualizations (Charts, Tables, Maps)
• Creating Dashboards
• Applying Filters and Slicers
• Implementing Advanced Analytics in Power BI

Language Skills

Python

• Basic Syntax and Data Types
• Control Flow
• Functions and Modules
• File Handling
• Exception Handling
• List Comprehensions and Lambda Functions
• Object-Oriented Programming (OOP) Concepts

Data Wrangling

NumPy

• Arrays and Array Operations
• Indexing and Slicing
• Mathematical Functions
• Linear Algebra Operations
• Random Module
• Broadcasting

Pandas

• Series and DataFrame
• Indexing and Selecting Data
• Data Cleaning and Manipulation
• Merging and Joining Data
• Grouping and Aggregating Data
• Handling Missing Data
• Time Series Data

Code Management

Git

• Setting up a Git Repository
• Version Control
• Branching & Merging
• Collaboration

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