Data Analytics

•    Duration: 6 Months
•    Eligibility: 12th Passed

The Data Analytics Program is an in-depth, hands-on curriculum designed for individuals seeking to gain expertise in data analysis, statistical methodologies, and data-driven decision-making. Throughout the program, students will learn essential data analytics concepts, tools, and techniques, preparing them to handle large datasets, uncover insights, and present actionable information.

Syllabus:

• Module 1: Excel for Data Analytics
  • Introduction to Excel for Analytics
  •  Importance of Excel in Data Analysis
  •  Overview of Excel Interface
  • Data Management Techniques
  •  Importing Data from Various Sources
  •  Data Cleaning and Preprocessing
  • Data Analysis Functions
  •  Statistical Functions (AVERAGE, MEDIAN, STDEV)
  •  Logical Functions (IF, AND, OR)
  •  Lookup Functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
  • Data Visualization
  •  Creating Charts (Column, Line, Pie)
  •  Using Pivot Tables and Pivot Charts
  •  Conditional Formatting for Insights
  • Data Analysis Toolpak
  •  Performing Regression Analysis
  •  Descriptive Statistics
  • Conclusive Result
  •  roficiency in data manipulation, analysis, and visualization using Excel.

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• Module 2: Python for Data Analytics
  • Introduction to Python
  • o Python Installation and Environment Setup
  • o Overview of Jupyter Notebooks
  • o Python Core (Full Syllabus)
  • Data Manipulation with Pandas
  • o Introduction to DataFrames and Series
  • o Data Cleaning and Transformation
  • o Handling Missing Data and Outliers
  • Exploratory Data Analysis (EDA)
  • o Descriptive Statistics with Pandas
  • o Data Visualization using Matplotlib, Seaborn and Plotly
  • o Correlation Analysis and Heatmaps
  • Statistical Analysis
  • o Hypothesis Testing with SciPy
  • o Regression Analysis using StatsModels
  • Introduction to Machine Learning
  • o Basic Concepts and Applications
  • o Supervised vs. Unsupervised Learning
  • Conclusive Result
  • o Ability to conduct data analysis and visualization using Python libraries.

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• Module 3: SQL for Data Analytics
  • Introduction to SQL
  • o Understanding Relational Databases
  • o SQL Installation and Environment Setup
  • Basic SQL Queries
  • o SELECT, WHERE, ORDER BY, and LIMIT Clauses
  • o Filtering and Sorting Data
  • Data Aggregation
  • o GROUP BY, HAVING, and Aggregate Functions (SUM, COUNT, AVG)
  • o Joins (INNER, LEFT, RIGHT, FULL) to combine tables
  • Data Manipulation
  • o INSERT, UPDATE, DELETE Statements
  • o Using Subqueries and Common Table Expressions (CTEs)
  • Database Management
  • o Basic Database Design Principles
  • o Indexing for Performance Improvement
  • Conclusive Result
  • o Ability to write and optimize SQL queries for data retrieval and manipulation.

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• Module 4: Tableau for Data Visualization
  • Introduction to Tableau
  • o Overview of Tableau Desktop Interface
  • o Connecting to Data Sources (Excel, SQL, etc.)
  • Creating Visualizations
  • o Building Basic Charts (Bar, Line, Scatter)
  • o Using Filters, Parameters, and Calculated Fields
  • Dashboard Design
  • o Principles of Effective Dashboard Design
  • o Creating Interactive Dashboards
  • Advanced Visualization Techniques
  • o Mapping and Geographic Analysis
  • o Using Tableau Prep for Data Cleaning
  • Sharing and Publishing
  • o Sharing Dashboards via Tableau Server/Public
  • o Best Practices for Presentation
  • Conclusive Result
  • o Proficiency in creating and sharing interactive visualizations using Tableau.

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• Module 5: Power BI for Business Intelligence
  • Introduction to Power BI
  • o Overview of Power BI Desktop
  • o Connecting to Data Sources
  • Data Transformation with Power Query
  • o Data Cleaning and Shaping
  • o Merging and Appending Queries
  • Creating Reports and Dashboards
  • o Building Interactive Visualizations
  • o Using DAX for Calculated Columns and Measures
  • Publishing and Sharing Reports
  • o Overview of Power BI Service
  • o Sharing Reports and Collaboration Features
  • Data Governance and Security
  • o Best Practices for Data Governance in Power BI
  • Conclusive Result
  • o Ability to create, publish, and share interactive reports in Power BI.

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• Module 6: Introduction to Machine Learning
  • Overview of Machine Learning
  • o Supervised vs. Unsupervised Learning
  • o Key Terminology (Features, Labels, Models)
  • Data Preparation
  • o Data Cleaning and Preprocessing
  • o Feature Engineering and Selection
  • Model Training and Evaluation
  • o Training vs. Testing Data
  • o Evaluation Metrics (Accuracy, Precision, Recall)
  • Common Algorithms
  • o Linear Regression and Classification
  • o Decision Trees and K-Means Clustering
  • Conclusive Result
  • o Ability to create, publish, and share interactive reports in Power BI.

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• Module 7: Ethical Considerations in Data Analytics
  • Understanding Data Ethics
  • o Importance of Ethics in Data Analytics
  • o Overview of Ethical Guidelines
  • Data Privacy and Security
  • o Key Regulations (GDPR, CCPA)
  • o Best Practices for Data Protection
  • Bias and Fairness in Data
  • o Identifying Bias in Data and Algorithms
  • o Techniques to Mitigate Bias
  • Data Governance
  • o Frameworks for Effective Data Governance
  • o Importance of Transparency and Accountability
  • Conclusive Result
  • o Ability to create, publish, and share interactive reports in Power BI.

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• Course Conclusion
  • Capstone Project
  • o A comprehensive project that requires the application of skills and tools learned throughout the course.
  • Final Assessment
  • o A comprehensive exam covering all tools and modules.

Assignment:  Self-Assessment by Faculty

Exit Profile: Data Analyst