Data Analytics

Course Overview

This instructor-led Data Analytics training course is designed to build your skills in understanding, analyzing, and presenting data in real-world scenarios. Whether you’re looking to shift into a data-driven role or enhance your business decision-making, this program covers the full spectrum of data analytics – from data cleaning and exploration to visualization and storytelling.

Participants will get hands-on experience using industry-standard tools including Microsoft Excel, SQL, and Python (pandas, matplotlib, seaborn). By the end of this course, you will be confident in handling datasets, performing statistical analysis, building visual dashboards, and deriving insights that drive smarter decisions.

Week 1–2: Introduction to Data & Excel Analytics

Session 1: Fundamentals of Data Analytics

• What is data analytics? Key types: descriptive, diagnostic, predictive, prescriptive

• Real-world applications in business, healthcare, finance, and tech

• Introduction to the data analytics process (define, prepare, analyze, visualize, act)

Session 2: Excel for Data Analysis

• Sorting, filtering, and cleaning data in Excel

• Essential functions and formulas (IF, VLOOKUP, INDEX/MATCH)

• Creating PivotTables and charts for analysis

• Best practices for Excel dashboards

Week 3–4: SQL for Data Analysts

Session 3: SQL Basics

• Database and table structures

• Writing SQL queries: SELECT, WHERE, ORDER BY

• Filtering and aggregating data with GROUP BY

• Using JOINs to combine data across tables

Session 4: Intermediate SQL Skills

• Subqueries, aliases, and CTEs (Common Table Expressions)

• SQL functions: COUNT, AVG, SUM, MIN, MAX

• Cleaning and transforming data using SQL

• Real-world query practice with business datasets

Week 5–6: Python for Data Analytics

Session 5: Python Essentials

• Getting started with Python: syntax, data types, loops, functions

• Using NumPy and pandas for data manipulation

• Loading CSV files, cleaning null values, filtering data

• Performing basic EDA (Exploratory Data Analysis)

Session 6: Data Visualization with Python

• Introduction to Matplotlib and Seaborn libraries

• Creating informative line, bar, scatter, and heatmap plots

• Enhancing visualizations with labels, colors, and annotations

• Visual storytelling techniques to present data effectively

Week 7–8: Insights, Dashboards & Final Project

Session 7: Statistical Data Analysis

• Understanding mean, median, mode, standard deviation

• Identifying trends and relationships (correlation)

• Introduction to regression and hypothesis testing

• Using statistics to make data-backed business decisions

Session 8: Dashboards & Final Project

• Data visualization principles and storytelling

• Building interactive dashboards using Excel or Tableau

• Final project: Analyze a dataset, visualize insights, and present findings

• Peer feedback and improvement suggestions

Course Features

• Instructor-led interactive sessions

• Hands-on exercises with real datasets

• Final capstone project to demonstrate skills

• Personalized feedback from instructors

• Certificate of Completion for your portfolio

Tools You’ll Use

• Microsoft Excel

• SQL (MySQL or PostgreSQL)

• Python with Jupyter Notebooks

• Pandas, NumPy, Matplotlib, Seaborn

• Optional: Tableau or Power BI

Assessments

• Weekly quizzes and coding challenges

• End-of-course project with report and presentation

• Certificate awarded upon successful completion