Data Analytics & Data Management

About Course

This comprehensive course introduces students to the full data analytics workflow, from collecting and cleaning data to analyzing, visualizing, and communicating insights. Students will gain hands-on experience with industry tools used by modern data analysts.

The course covers spreadsheets, SQL databases, data annotation, statistics, data visualization, and Python-based data analysis. Students will also learn how to create dashboards, manage data pipelines, and present insights through data storytelling.

Through real-world projects and practical assignments, learners will build a professional portfolio demonstrating their ability to transform raw data into actionable insights.

Understand the data analytics lifecycle
Collect and organize datasets from multiple sources
Clean and prepare messy data for analysis
Perform data annotation for AI and machine learning datasets
Analyze datasets using spreadsheets
Manage large datasets using MySQL
Retrieve and analyze data using SQL
Perform exploratory data analysis (EDA) to uncover patterns
Create charts, dashboards, and visual reports
Build interactive dashboards using Microsoft Power BI and Tableau
Use Python for data analysis
Apply data storytelling techniques to communicate insights
Understand data ethics and privacy principles
Build a professional data analytics portfolio

Course Content

Introduction to Data Analytics
This module introduces the field of data analytics, explaining how organizations use data to make strategic decisions. Students will learn the role of data analysts and the overall analytics workflow.

Introduction to Data Analytics
What is Data Analytics?
The Role of a Data Analyst
Types of Data
The Data Analytics Lifecycle
Applications of Data Analytics in Business

Data Collection and Data Sources
Students will learn how organizations collect and store data from various sources such as surveys, applications, sensors, and public datasets.

Data Annotation for AI
This module teaches students how data annotation works and how labeled datasets are created for machine learning and artificial intelligence applications.

Data Cleaning and Preparation
Students will learn how to clean messy datasets and prepare them for analysis.

Data Analysis
In this module, you will analyze datasets using spreadsheet tools like pivot tables, formulas and functions.

Data Visualization and Dashboards
This module focuses on turning data into visual insights.

Data Management with MySQL
You will learn how relational databases store and organize large datasets using MySQL

SQL for Data Analysis
In this module you will learn how to clean, transform and analyze large data set using SQL

Exploratory Data Analysis
You will explore techniques used to investigate datasets and uncover hidden patterns.

Data Analytics with Python
Students will learn how Python helps analysts handle large datasets.

Data Analysis with Python
Introduction to Python
Setting Up the Python Environment
Python Basics
Working with Data Structures
Control Flow in Python
Functions in Python
Introduction to NumPy for Numerical Data
Data Analysis with Pandas
Data Cleaning with Pandas
Data Visualization with Python

Data Ethics and Privacy
This module introduces students to the principles of ethical data use, responsible data management, and the protection of personal information. Students will learn how improper data practices can harm individuals and organizations, and why ethical standards are essential in data analytics.

About Course

What Will You Learn?

Course Content

Introduction to Data Analytics This module introduces the field of data analytics, explaining how organizations use data to make strategic decisions. Students will learn the role of data analysts and the overall analytics workflow.

Introduction to Data Analytics

What is Data Analytics?

The Role of a Data Analyst

Types of Data

The Data Analytics Lifecycle

Applications of Data Analytics in Business

Data Collection and Data Sources Students will learn how organizations collect and store data from various sources such as surveys, applications, sensors, and public datasets.

Understanding Data Sources

Types of Data Sources

Structured vs Unstructured Data

Data Formats (CSV, JSON, Excel)

Data Collection Techniques

Ethical Data Collection

Data Annotation for AI This module teaches students how data annotation works and how labeled datasets are created for machine learning and artificial intelligence applications.

Data Annotation for AI Systems

Introduction to Data Annotation

Importance of Labeled Data

Types of Annotation (Text, Image, Audio)

Annotation Tools

Quality Assurance in Data Labeling

Data Cleaning and Preparation Students will learn how to clean messy datasets and prepare them for analysis.

Cleaning and Preparing Data

Common Data Problems

Handling Missing Data

Removing Duplicate Records

Data Formatting and Transformation

Preparing Data for Analysis

Data Analysis In this module, you will analyze datasets using spreadsheet tools like pivot tables, formulas and functions.

Spreadsheet Data Analysis

Sorting and Filtering Data

Spreadsheet Formulas and Functions

Pivot Tables

Basic Data Analysis

Data Visualization and Dashboards This module focuses on turning data into visual insights.

Building Data Dashboards

Principles of Data Visualization

Chart Types and Use Cases

Importing Data into Power BI / Tableau

Data Modeling for Visualization

Creating Interactive Dashboards

Data Management with MySQL You will learn how relational databases store and organize large datasets using MySQL

Data Management with MySQL

Introduction to Databases

Relational Database Concepts

Installing MySQL

Creating Databases and Tables

Importing Data into Databases

Managing Data Records

SQL for Data Analysis In this module you will learn how to clean, transform and analyze large data set using SQL

SQL for Data Analysis

SQL Fundamentals

SELECT Queries

Filtering with WHERE

Sorting Data

Aggregation Functions

GROUP BY and HAVING

Joining Tables

Exploratory Data Analysis You will explore techniques used to investigate datasets and uncover hidden patterns.

Exploratory Data Analysis

Data Profiling

Identifying Patterns

Detecting Outliers

Understanding Relationships

Data Investigation Techniques

Data Analytics with Python Students will learn how Python helps analysts handle large datasets.

Data Analysis with Python

Introduction to Python

Setting Up the Python Environment

Python Basics

Working with Data Structures

Control Flow in Python

Functions in Python

Introduction to NumPy for Numerical Data

Data Analysis with Pandas

Data Cleaning with Pandas

Data Visualization with Python