Data Analytics

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

Choose the training format that best fits your schedule and learning style:

Online Instructor-Led Training – Learn directly from certified experts in live interactive sessions.
Online Self-Paced Learning – Study anytime, anywhere with flexible access to course materials.
Onsite / Classroom Training – Experience hands-on, instructor-led sessions in a collaborative environment.

What’s Included:

Our comprehensive training program is designed to ensure a complete, career-ready learning experience. You’ll get:

Onsite Instructor-Led Sessions
Live Role
Certified Trainers
Post-Training Support
Resource Materials including eBooks, eGuides, and reference documentation.
Real-World Scenarios & Case Studies
Networking Opportunities
Group Projects and Collaborative Activities
Best Practices and Industry Insights
Access to Recorded Sessions (available only for online programs) for flexible review.
Follow-Up Sessions
Tool Demonstrations and Walkthroughs

Empower your career with expert-led, practical, and flexible training designed to help you master real-world skills and stay ahead in the industry.

Course Content

What is Data Analytics?

Defining Data Analytics and Its Role
Key Differences: Descriptive, Predictive, and Prescriptive Analytics
The Data Analytics Process: From Collection to Insights

Types of Data and Data Sources

Tools and Software for Data Analytics

Data Collection Techniques

Data Cleaning Fundamentals

Data Transformation and Normalization

Data Integration and Merging

Exploratory Data Analysis (EDA)

Introduction to Data Visualization

Creating Visualizations

Advanced Visualization Techniques

Basic Statistical Analysis

Advanced Data Analysis Techniques

Introduction to Machine Learning for Data Analytics

Model Evaluation and Validation

Introduction to Tools for Machine Learning

SQL Basics for Data Analytics

Advanced SQL Queries

Data Extraction, Transformation, and Loading (ETL)

Database Management and Optimization

Case Study 1: Data Analytics in Healthcare

Case Study 2: Financial Data Analysis

Case Study 3: Marketing Analytics

Final Project and Report

Deep Dive into Machine Learning

Automation with Python and SQL

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

What’s Included:

Course Content

What is Data Analytics?

Defining Data Analytics and Its Role

Key Differences: Descriptive, Predictive, and Prescriptive Analytics

The Data Analytics Process: From Collection to Insights

Types of Data and Data Sources

Structured vs Unstructured Data

Sources of Data: APIs, Databases, Web Scraping

Introduction to Data Formats: CSV, JSON, Excel, SQL

Tools and Software for Data Analytics

Overview of Popular Tools: Excel, SQL, Python, R

Introduction to Data Analytics Platforms (e.g., Power BI, Tableau)

Data Collection Techniques

Collecting Data via APIs

Web Scraping Basics

Working with Databases and Data Storage

Data Cleaning Fundamentals

Understanding Data Quality and Integrity

Removing Duplicates, Handling Missing Data, and Outliers

Standardizing Data Formats (e.g., Dates, Strings)

Data Transformation and Normalization

Normalizing Data for Consistency

Converting Data Types and Handling Categorical Variables

Using Functions for Data Transformation in Excel, Python, or SQL

Data Integration and Merging

Combining Datasets from Multiple Sources

Merging Data: Using SQL JOINs and Python (Pandas) Operations

Exploratory Data Analysis (EDA)

Overview of EDA and Its Role

Descriptive Statistics: Mean, Median, Mode, Variance

Identifying Patterns and Anomalies in Data

Introduction to Data Visualization

Principles of Effective Data Visualization

Overview of Visualization Tools: Tableau, Power BI, Python (Matplotlib, Seaborn)

Creating Visualizations

Line Graphs, Bar Charts, and Pie Charts

Histograms, Box Plots, and Scatter Plots

Heatmaps, Correlation Matrices, and Other Complex Visualizations

Advanced Visualization Techniques

Dashboards and Interactive Reports in Tableau and Power BI

Customizing Visual Elements for Audience Understanding

Basic Statistical Analysis

Correlation and Regression Analysis

Hypothesis Testing: T-tests, ANOVA, and Chi-Square Tests

Confidence Intervals and P-values

Advanced Data Analysis Techniques

Clustering: K-Means, Hierarchical Clustering

Time Series Analysis and Forecasting

Data Segmentation and Grouping Techniques

Introduction to Machine Learning for Data Analytics

What is Machine Learning and Its Role in Data Analytics?

Overview of Supervised vs Unsupervised Learning

Regression Models: Linear and Logistic Regression

Model Evaluation and Validation

Model Performance Metrics: Accuracy, Precision, Recall, F1 Score

Cross-Validation and Bias-Variance Tradeoff

Introduction to Tools for Machine Learning

Using Python (Scikit-learn) for Simple Models

Overview of R for Statistical Modeling and Machine Learning

SQL Basics for Data Analytics

Introduction to SQL and Relational Databases

Writing Basic SQL Queries: SELECT, WHERE, ORDER BY

Filtering, Sorting, and Aggregating Data

Advanced SQL Queries

JOIN Operations (INNER, LEFT, RIGHT, FULL)

Subqueries and Nested Queries

Window Functions and Grouping Techniques

Data Extraction, Transformation, and Loading (ETL)

Understanding ETL Processes

Using SQL for Data Manipulation and Integration

Database Management and Optimization

Indexing, Query Optimization

Database Normalization Techniques

Case Study 1: Data Analytics in Healthcare

Predictive Analytics in Patient Care and Outcomes

Healthcare Data: Challenges and Opportunities