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Data Analytics

Who this course is for

  • Students & Beginners: Freshers & Graduates (B.Tech, BCA, B.Sc, M.Tech, MCA, M.Sc, MBA, etc.) Anyone who wants to start a career in Data Analytics, AI, or Business Intelligence. Those with no prior coding experience but willing to learn Python, SQL, and Data Visualization tools.
  • IT & Software Professionals: Software Developers & Engineers looking to transition into Data Analytics or Data Science. Database Administrators (DBAs) who want to work with big data and advanced analytics. Python & Java developers who want to enhance their skills with data processing.

Why take this course

  • Data-driven decision-making is a critical aspect of every industry today. Companies are investing heavily in data analytics to stay competitive.
  • Gain expertise in data analysis tools like Excel, SQL, Python, Power BI, and Tableau, which are highly sought after in the job market.
  • By learning how to analyze and interpret data, you gain the ability to make informed decisions, whether in business, marketing, finance, or any other field.
  • The world is moving towards automation and AI, which means the demand for professionals who can interpret data will continue to grow.
  • Data analytics is foundational to other advanced technologies like AI, machine learning, and business intelligence, making these skills transferable across many tech-driven roles.

Course Content

Module 1: Fundamentals of Statistics and Probability
  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis Testing
Module 2: Programming for Data Analytics
  • Python for Data Analytics
  • R for Data Analytics (Optional)
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
Module 3: Excel for Data Analysis
  • Advanced Excel Functions
  • Pivot Tables and Charts
  • Macros and VBA Basics
Module 4: Data Visualization
  • Data Visualization Techniques
  • Tools: Tableau, Power BI, Matplotlib, Seaborn
Module 5: Database Management and SQL
  • Basics of Relational Databases
  • SQL Queries and Joins
  • Advanced SQL Functions
Module 6: Data Cleaning and Preprocessing
  • Handling Missing Data
  • Data Transformation and Normalization
  • Feature Engineering
Module 7: Exploratory Data Analysis (EDA)
  • Identifying Patterns and Trends
  • Creating Summary Reports
Module 8: Business Analytics
  • Key Performance Indicators (KPIs)
  • Business Intelligence Tools
  • Case Studies in Business Analytics
Module 9: Predictive Analytics
  • Introduction to Predictive Modeling
  • Regression and Classification
  • Model Evaluation Metrics
Module 10: Big Data Analytics
  • Introduction to Big Data
  • Tools: Hadoop, Spark, and Hive
  • Processing Large Datasets
Module 11: Time Series Analysis
  • Introduction to Time Series Data
  • Forecasting Techniques
Module 12: Google Analytics
  • Website Analytics Basics
  • Campaign Performance Analysis
Module 13: Data Reporting and Dashboards
  • Creating Dashboards with Power BI/Tableau
  • Automating Reports
Module 14: Data Analytics in Cloud Platforms
  • AWS Analytics Tools
  • Google Cloud BigQuery
  • Azure Data Analytics
Module 15: Data Privacy and Ethics
  • Understanding GDPR and CCPA
  • Ethical Practices in Data Analytics
Module 16: Capstone Projects
  • Real-World Data Analytics Projects
  • End-to-End Analysis and Reporting
Module 17: Advanced Topics
  • A/B Testing and Experimental Design
  • Text Analytics
  • Sentiment Analysis