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Data Analysis

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Course Syllabus

Module 1: Introduction to Data Analytics
  1. Data Analytics OVERVIEW
  • Benefits of Data Analytics
  • Real-World Applications of Data Analytics
  • Case Studies in Data Analytics
  • Overview of the analytics process
  1. Introduction to Data
  • Types of Data On The Basis of Sources
  • Types of Data On The Basis of Nature
  • Types of Data On The Basis of Format or Organization
  • The 5 Vs of Data: the Essence of Data
  • Data Collection Methods
  1. Introduction to Excel
  • Overview of Excel Interface and Features.
  • Understanding Data and Data Types.
  • Data Cleaning and Preprocessing:
    • Removing duplicates.
    • Handling missing values.
    • Formatting datasets.
  1. Data Manipulation and Management in Excel
  • Using Excel Functions:
    • Lookup functions (VLOOKUP, HLOOKUP).
    • Logical functions (IF, AND, OR).
    • Aggregation functions (SUMIF, COUNTIF).
  • Data Validation and Conditional Formatting.
  • Sorting, Filtering, and Pivot Tables.
  • Working with Excel Tables.
  1. Data Visualization
  • Creating Charts:
    • Bar, Line, Pie, Scatter, and more.
  • Advanced Visualizations:
    • Combo charts, Sparklines.
  • Conditional Formatting for Visual Insights.
  • Dashboards:
    • Design and automation principles.
  1. Statistical Analysis in Excel
  • Descriptive Statistics:
    • Mean, Median, Mode, Standard Deviation.
  • Correlation and Regression Analysis.
  • Hypothesis Testing.
  • Time Series Analysis.
  1. Advanced Excel Techniques
  • Using Macros and VBA for Automation.
  • Solver for Optimization Problems.
  • Data Analysis ToolPak: Advanced Analytical Functions.
  • Power Query for Data Transformation.
  1. Data Applications
  • Financial Analysis: ROI, Break-even Analysis.
  • Sales and Marketing Analytics: Forecasting, Trend Analysis.
  • HR Analytics: Attrition Rates, Productivity Analysis.
  • Supply Chain Analytics: Inventory Management.
  1. Capstone Project
  • Comprehensive Data case analysis.
  • Data cleaning, visualization, and insights generation.
  • Designing a dashboard for stakeholders.
  1. Introduction to SQL
  • Definition and Purpose
  • Importance in Data Management

 

  1. Basic SQL Commands
    • SELECT Statement
    • RANGE Keyword
    • Comparison and Logical Operators
    • IN Operator
    • Wildcard Characters
    • LIKE Clause for Pattern Matching
    • Constraints in SQL

 

  1. Advanced SQL Concepts
  • Aggregation Functions (COUNT, SUM, AVG, etc.)
  • Functions in SQL (String, Numeric, Date, etc.)
  • GROUP BY Clause for Grouping Data
  • ORDER BY Clause for Sorting Results
  • HAVING Clause for Filtering Grouped Data

 

  1. SQL Joins
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  1. Conditional Logic in SQL
    • CASE Statement for Conditional Processing
    • Using CASE in SELECT, WHERE, and ORDER BY clauses

 

  1. Complex Queries
  • Writing Complex SQL Queries
  • Subqueries and Nested Queries
  • Combining Multiple SQL Commands

 

  1. Doubt Clearing
    • Addressing Common SQL Query Issues
    • Troubleshooting SQL Errors
    • Tips for Efficient SQL Query Writing
  1. Basics of Python
    • Introduction to Python
      • Overview of Python and its applications in data analytics.
      • Installing Python and setting up the environment (Anaconda, Jupyter Notebooks).
    • Python Syntax and Data Types
      • Basic syntax: variables, data types (integers, floats, strings, booleans).
      • Control structures: if statements, loops (for, while).
      • Functions: defining and calling functions, understanding scope.
    • Data Structures
      • Lists, tuples, and dictionaries: creation, manipulation, and common methods.
      • Sets and their operations.
    • File Handling
      • Reading from and writing to files (text and CSV).
      • Understanding file paths and context managers.
  1. Data Manipulation with Numpy and Pandas
  • Introduction to Pandas DataFrames and Series.
  • Data cleaning: handling missing values, duplicates, and data types.
  • Data transformation: filtering, sorting, and aggregating data.
  1. Data Visualization
  • Introduction to Matplotlib and Seaborn.
  • Creating basic plots: line, bar, scatter, and histogram.
  • Customizing visualizations: titles, labels, and legends.
  1. Statistical Analysis
  • Descriptive statistics: mean, median, mode, standard deviation.
  • Correlation and regression analysis.
  • Hypothesis testing and confidence intervals.
  1. Introduction to Tableau
    • Overview of Tableau and its ecosystem.
    • Understanding Tableau Desktop, Tableau Server, and Tableau Public.

2.Connecting to Data Sources

  • Importing data from various sources (Excel, SQL, etc.).
  • Data preparation and cleaning within Tableau.

3.Creating Visualizations

  • Building basic charts: bar, line, pie, and maps.
  • Using calculated fields and parameters for dynamic analysis.
  • Creating dashboards and stories for data presentation.

4.Advanced Analytics in Tableau

  • Using table calculations and level of detail (LOD) expressions.
  • Implementing forecasting and trend analysis.
  • Integrating R and Python scripts for advanced analytics.

5.Best Practices for Dashboard Design

  • Principles of effective data visualization.
  • Designing user-friendly dashboards.
  • Performance optimization techniques.

1.Introduction to Power BI

  • Overview of Power BI and its components (Power BI Desktop, Power BI Service).
  • Understanding the Power BI interface.

2.Data Import and Transformation

  • Connecting to various data sources (Excel, SQL, web data).
  • Using Power Query for data cleaning and transformation.
  • Creating relationships between tables.

3.Creating Reports and Dashboards

  • Building visualizations: bar charts, line charts, maps, and tables.
  • Using slicers and filters for interactive reports.
  • Designing dashboards for effective storytelling.

4.DAX (Data Analysis Expressions)

  • Introduction to DAX and its syntax.
  • Creating calculated columns and measures.
  • Time intelligence functions for date-based analysis.

5.Sharing and Collaboration

  • Publishing reports to Power BI Service.
  • Setting up data refresh schedules.
  • Collaborating with team members and sharing insights.

Job Assistance

Program

Online Lectures

Delivery Mode

English

Language

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Helping You Understand Find My Tuition

Data Analysis is about extracting insights from data using statistics, business intelligence tools, and AI.

✅ Beginners with no prior coding experience

✅ Business professionals & analysts

✅ Anyone interested in data-driven decision-making

📌 Excel, SQL & Python

📌 Power BI & Tableau

📌 Data Cleaning & Visualization

📌 Predictive Analytics & Business Intelligence

✅ Data Analyst

✅ Business Intelligence Analyst

✅ Marketing Analyst

✅ Financial Data Analyst

  • Entry-level: $60,000 - $80,000 per year
  • Mid-level: $90,000 - $110,000 per year
  • Senior-level: $130,000+ per year

You start paying only after securing a job with a salary of $60,000+ per year.

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