Applied Data Analytics

Applied Data Analytics

Course Duration: 4 Months + 2month internship

Skills Covered: Advanced Excel, SQL, PowerBI, Python, Unlimited Mock Interviews, Case Studies

1. Intro to Data Analytics
2. Business Analytics with Excel (Module 1)
3. Data Analysis with SQL (Module 2)
4. Storytelling and Dashboard building using Power BI (Module 3)
5. Python for Data Analytics (Module 4)

Get Ready for Mock Interviews after completing at least 2 Modules

Module 1: Business Analytics with Excel

• Introduction to Data Analytics and Business Analytics
  • Understand the role of data analytics in business.
  • Introduction to Microsoft Excel for data analysis.
• Basic Excel Functions and Data Cleaning
  • Learn spreadsheet fundamentals.
  • Explore data cleaning and preparation in Excel.
  • Practical Case Study: Cleaning and organizing real-world data in Excel.
• Data Visualization in Excel
  • Create charts and graphs for data visualization.
  • Conduct exploratory data analysis using Excel.
  • Practical Case Study: Creating visualizations for a business dataset.
• Advanced Excel Techniques and Pivot Tables
  • Work with pivot tables and pivot charts.
  • Apply statistical analysis using Excel functions.
  • Practical Case Study: Analyzing sales data and creating insightful pivot tables.

Module 2: Data Analysis with SQL

• Introduction to SQL and Relational Databases
  • Gain a basic understanding of Structured Query Language.
  • Explore the concept of relational databases.
• Retrieving Data with SELECT Statements
  • Learn to write SQL SELECT statements for data retrieval.
  • Practical Case Study: Extracting relevant data from a database.
• Filtering, Sorting, and Aggregating Data
  • Use WHERE and ORDER BY clauses for data filtering and sorting.
  • Apply advanced filtering techniques.
  • Practical Case Study: Analyzing and aggregating sales data.
• Joins, Subqueries, and Advanced SQL Concepts
  • Understand JOIN operations for data from multiple tables.
  • Work with subqueries and explore advanced SQL concepts.
  • Practical Case Study: Joining tables and solving complex data problems.

Module 3: Storytelling and Dashboard Building with Power BI

• Introduction to Power BI and Data Visualization
  • Overview of Power BI and its components.
  • Connecting to data sources.
• Data Transformation and Modeling in Power BI
  • Clean and shape data using Power Query.
  • Create data models in Power BI Desktop.
  • Practical Case Study: Transforming and modeling a real-world dataset.
• Creating Interactive Dashboards
  • Design and build interactive visualizations.
  • Develop dashboards for data storytelling.
  • Practical Case Study: Designing an interactive sales dashboard.
• Advanced Features and DAX (Data Analysis Expressions)
  • Implement advanced features in Power BI.
  • Utilize DAX for custom calculations and measures.
  • Practical Case Study: Applying DAX calculations to solve business challenges.

Module 4: Python for Data Analysis (Optional)

• Introduction to Python for Data Analysis
  • Learn basics of Python programming.
  • Explore data manipulation with Pandas.
• Data Visualization with Matplotlib and Seaborn
  • Create data visualizations using Matplotlib and Seaborn.
  • Conduct exploratory data analysis with Python.
• Advanced Pandas Techniques and Statistical Analysis (Optional)
  • Apply advanced data manipulation with Pandas.
  • Explore statistical techniques using Pandas.
  • Practical Case Study (Optional): Conducting statistical analysis on a real-world dataset.

Each module includes practical case studies to reinforce learning and provide hands-on experience with data analysis tools and techniques. The optional Python module offers additional practical exercises for those interested in advanced data analysis using Python.

Benefits of taking the course:

• Comprehensive Skill Development: Students gain a comprehensive set of data analytics skills, covering Excel, SQL, Power BI, and Python for data analysis.
• Real-World Expertise: The course is taught by experienced professionals from reputable companies, providing insights into real-world applications and industry best practices.
• Practical Experience: Practical exercises, projects, and hands-on learning opportunities equip students with the practical skills needed in data analytics roles.
• Interview Readiness: Unlimited mock interviews help students hone their interview skills, increasing their chances of landing a data analytics job.
• Career Advancement: The course prepares students for roles such as data analyst, business analyst, reporting analyst, and data visualization specialist.
• Potential for High Earnings: Graduates can expect a minimum starting salary of 6 LPA (Lakhs Per Annum) with the potential to earn up to 30 LPA in data analytics positions.
• Networking: Interaction with industry professionals can create networking opportunities for future career growth.
• Resume and LinkedIn Support: Assistance with resume and LinkedIn profile development enhances students’ job search efforts.
• Placement Assistance: The commitment to provide placement assistance until a student is placed demonstrates a strong focus on student success.

The potential for high earnings underscores the attractive financial prospects of a career in data analytics, making this course even more appealing to individuals seeking to enter or advance within the field.