Masterclass: Business Intelligence & Advanced Data Analytics

Learn to analyze data, generate insights, and build dashboards using modern analytics tools.

12 Weeks

Online / Offline

English & Tamil

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Course Overview

Become a master of business intelligence with our Data Analytics course. Focusing on practical tools like Excel, SQL, Tableau, and PowerBI, you will learn how to clean complex datasets, build interactive dashboards, and present compelling data narratives that help companies optimize their operations.

Skills You Will Master

Data Analysis Excel & SQL Data Visualization Statistical Analysis Python for Analytics Business Intelligence Tools

Course Curriculum

1. Introduction to Data Analytics

What is Data Analytics? Importance & Applications
Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Role of a Data Analyst
Data Analytics workflow
Tools & Technologies: Excel, SQL, Python, R, Tableau, Power BI

2. Data Types & Data Collection

Types of data: Structured, Semi-Structured, Unstructured
Data sources: Databases, CSV, APIs, Web Scraping
Data quality: accuracy, completeness, consistency
Data collection methods & best practices

3. Excel for Data Analytics

Basic functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, HLOOKUP
Data cleaning: Remove duplicates, Text to columns, Find & Replace
Pivot Tables & Pivot Charts
Conditional Formatting
Data visualization in Excel: charts, graphs

4. SQL for Data Analytics

Introduction to relational databases
CRUD operations: SELECT, INSERT, UPDATE, DELETE
Filtering & sorting data (WHERE, ORDER BY, LIMIT)
Aggregation & grouping (GROUP BY, HAVING, COUNT, SUM, AVG)
JOINs, Subqueries, Nested Queries
Advanced SQL functions: Window functions, CASE statements

5. Data Cleaning & Preprocessing

Handling missing values, duplicates, and outliers
Data type conversions
Data normalization & scaling
Handling categorical data (Encoding)
Best practices in data preprocessing

6. Data Visualization

Principles of effective visualization
Charts & graphs: Bar, Line, Pie, Histogram, Scatter plots
Python libraries: Matplotlib, Seaborn
Interactive visualization: Plotly
Dashboards using Tableau / Power BI

7. Statistical Analysis

Descriptive statistics: mean, median, mode, variance, standard deviation
Data distribution & probability
Inferential statistics: Hypothesis testing, Confidence intervals, t-tests, chi-square tests
Correlation & regression analysis
Analyzing trends & patterns

8. Python for Data Analytics

Python basics: variables, loops, functions, data structures
Pandas for data manipulation: DataFrames, indexing, filtering, grouping
NumPy for numerical computations
Data cleaning & preprocessing using Python
Visualizations with Matplotlib and Seaborn

9. Advanced Analytics Techniques

Predictive analytics basics
Regression analysis & forecasting
Classification analysis (Decision Tree, Logistic Regression)
Clustering (K-Means, Hierarchical)
Time series analysis basics

10. Business Intelligence (BI) Tools

Introduction to BI tools: Tableau, Power BI
Connecting to data sources
Creating dashboards & reports
Data storytelling & insights presentation
Drill-down, filters, and interactive visuals

11. Big Data & Analytics

Introduction to Big Data concepts
Overview of Hadoop & Spark
Working with large datasets
Cloud-based analytics (AWS, Azure, Google Cloud)