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

Master Excel, SQL, Python, statistics, and visualization tools for real-world data analysis.

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)

Take the Next Step in Your Career

Join our comprehensive Data Analytics program and gain real-world skills that employers demand.

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