skilldevelopment.cpe@lpu.co.in

What you'll learn

This course offers an immersive, hands-on learning experience in key data analytics tools — Microsoft Excel, SQL, Python, Power BI, and Tableau. Designed for aspiring data professionals, it builds foundational and practical skills in data cleaning, analysis, visualization, dashboard creation, and storytelling. Participants will learn how to extract actionable insights and drive data-informed decisions, preparing them for careers in analytics and business intelligence.

  • Overview of Data Analytics: Understanding the role of data in decision-making and problem-solving. Key concepts: Types of data (structured vs. unstructured), sources (internal, external), and the life cycle of data analytics. Exploring different analytics types: Descriptive (what happened), diagnostic (why it happened), predictive (what might happen), and prescriptive (what should be done). Importance of leveraging tools like Excel, Python, SQL, and Power BI in real-world analytics. Data Analytics using MS Excel: Introduction to Microsoft Excel and its use in foundational analytics tasks. Basics Navigation in MS Excel, Essential formulas (COUNT, MIN, MAX, SUM, AVERAGE, MEDIAN), Cleaning and Preparing Data: Removing errors, handling missing values, finding & removing duplicates, and ensuring data integrity. Using conditional formatting and data validation for quality checks. Sorting and filtering data in Excel for analysis. Lookup functions and Statistical functions (SUMIF, AVERAGEIF, COUNTIF, and STDEV). Analysing Data: Summarization and visualization Pivot tables to summarize data and basic and advanced charts for visualization. Dashboarding in Excel: Introduction to dashboard creation, Linking pivot tables, slicers, and charts for creating a dashboard.
  • Foundations of SQL: Database basics and setup. Essential SQL commands: SELECT, INSERT, UPDATE, DELETE. Data Summarization using Aggregation functions (SUM, AVG, MIN, MAX, COUNT), and GROUP BY - HAVING. Data Relations and Transformation: Working with joins (INNER, LEFT, RIGHT, FULL joins), Using CASE statements for conditional data transformation. Subqueries. Data Pivoting using SQL using CASE statements and using PIVOT operator.
  • Introduction to Python: Installation, IDEs (Jupyter, VS Code. Google Colab), and Python essentials Keywords, Identifiers, Variables, Operators, Control Flow, Lists, Tuples, Sets, Dictionaries, Strings. Working with Pandas and NumPy to clean and analyse datasets: Import datasets from external source. Data Cleaning (Handling missing values and Removing duplicates). Data Transformation (Filtering and selecting subsets, Aggregating data, Merging multiple datasets). Handling missing data and outliers. Data Visualization: Creating visualizations Python.
  • Introduction to Power BI: Interface, Environment, Data Connections, Data Types, Aggregation, and Data Transformation. Design Fundamentals and Visualizations: Bar and Column Chart, Stacked and Clustered Bar/ Column Chart, Combo Charts, Pie Chart, Donut Chart, Gauge Chart, Ribbon Tree Map, Heat Map, Line Chart, Area Chart, Funnel Chart, Scatter Plot, Decomposition Tree, Filed Map, Symbol Map, and Other Advanced Charts like Infographics, Word Cloud, Scroller, Animated Bar Chart etc. Forecasting, Trend Analysis, Cross Tabulation: Tables and Matrix, Text and Number Filters, Filters on Visuals and page, Slicers, Cards, Hierarchies, Drilldown and Drill Through Functionality.
  • DAX Functions: New Measure Vs New Column, Number base calculation, Text based Calculations, Date base Calculation, Logic Based Calculation (IF Condition and Nested IF); Parameters Creation and Bin Creation. Data Blending: Working with multiple databases. Power Query (Get & Transform): Import, Transform, Clean, Combine data and files. Dashboard: Fundamentals, Design and Interactivity Publishing Report: Power BI Service or Power BI Cloud Services.
  • Introduction to Tableau: Interface, Environment, Cards, Shelfs, Marks, Data Connections, Extracts and Live Connections, Cross Database Joins and Union. Visualizations: Word Cloud, Time & Motion Chart, Dual Axis Chart, Maps, Map Options, Tree Map, Heat Map, Area Chart, Pie Chart, Field Map, Stacked Bar, Donut Chart, Scatter Plot, Constant, Reference and Drop, Line Chart, Cross Tabulation etc. Forecasting, Trend Analysis, Bin Creation, Group Creations, Text Filets, Number Filets, Date Filters, Drill Down Functionality and Hierarchies, Quick Table Calculations, Logical Calculations (Number, Date etc), IF Condition, Nested IF, Parameters Creation and Control. Dashboard Creation and Storytelling: Fundamentals, Design, Web Integrity, Interactivity.

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Sumit Mittu
Assistant Professor

Mr. Sumit Mittu, Assistant Professor in School of Computer Science and Engineering, Lovely Professional University, is strong academician with 20+ years of experience in teaching as well as academic administration. He has trained thousands of undergraduate and post-graduate students with skills of programming using several languages like C/C++, Java, Python, VB, C#, SQL, HTML, JS, ASP, etc. as well as contributed in building strong foundation in students for core computer science courses including but not limited to Data Structures, Operating Systems, Computer Networks, DBMS, Data Analytics etc. He is the author of the book “A Workbook on C++” published by Cengage Learning. He has also delivered several guest lectures and FDPs on topics like e-learning, smart teaching, computer programming etc. He has a strong hold on handling data for diversified application areas and problems statements over academia and understands the career expectation of the recruiters and skill gap in students.


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Dr. Harpreet Singh Bedi
Professor & Assistant Dean

Dr. Harpreet Singh Bedi