skilldevelopment.cpe@lpu.co.in

What you'll learn

In today’s data-driven world, the ability to analyze, interpret, and make decisions based on data is a critical skill across industries. "From Data to Decision: A Hands-On Approach to Data Science" is designed to equip learners with the fundamental tools and techniques of data science, from data collection and cleaning to visualization, analysis, and machine learning. This 55-60 hour course will provide a comprehensive, hands-on approach to working with SQL, Excel, Power BI, and Python (including libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn). Learners will gain practical experience with real-world datasets, apply data science techniques to solve business problems, and build machine learning models for predictive insights. By the end of this course, participants will be confident in leveraging data science tools to extract meaningful insights, visualize data effectively, and make informed, data-driven decisions. Course Outcomes: 1) Explain key concepts of data science, including data processing, analytics, and decision-making 2) Apply SQL queries to retrieve, filter, and manipulate structured data efficiently, utilizing functions such as joins, aggregations, and subqueries for data analysis. 3) Use Excel functions, PivotTables, and Power Query to clean, manipulate, and analyze data, enabling effective data-driven decision-making. 4) Develop dynamic dashboards and visualizations in Power BI to derive business insights. 5) Utilize NumPy, Pandas, Matplotlib, and Seaborn to perform data cleaning, transformation, and visualization, identifying key trends and patterns in datasets. 6) Build and evaluate machine learning models using Scikit-Learn, applying regression, classification, and clustering techniques to extract predictive insights from data.

  • Understanding Data Science & its Applications, Role of a Data Scientist & Industry Use Cases, Data Science Tools: Python, SQL, Excel, Power BI Introduction to SQL & Databases, Working with Relational Databases, Basic SQL Queries: SELECT, INSERT, UPDATE, DELETE, CONSTRAINTS in SQL Filtering & Aggregation: WHERE, GROUP BY, HAVING, ORDER BY Joins & Subqueries: INNER JOIN, LEFT JOIN, RIGHT JOIN Advanced SQL: Window Functions, CTEs, Indexing
  • Excel for Data Analysis : Data Cleaning & Manipulation Essential Formulas: LOOKUP, INDEX-MATCH, DATE/TIME , TEXT functions Pivot Tables, Pivot Charts, Slicers and Timelines Power Query & Automation with Macros Hands-on: Analyzing Datasets in Excel
  • Introduction to PowerBI interface, Importing Data into Power BI, Data Cleaning Building and Structuring Data Models Advanced Calculations with DAX Designing and Enhancing Visual Reports Map Visuals: Using geographic data for map visuals, Adding slicers for filtering data, Filtering Options - TOP N filtering
  • Python Fundamentals : Data Types, Lists, Tuples, Dictionaries, Loops NumPy for Numerical Computing : Creating & Manipulating Arrays, Mathematical & Statistical Operations, Data Cleaning Pandas for Data Manipulation, Loading & Cleaning Data (Handling Missing Values) Data Transformation & Feature Engineering, Merging, Filtering, and Grouping Data Matplotlib & Seaborn for Data Visualization
  • Introduction to Machine Learning, Supervised vs. Unsupervised Learning, Overview of Libraries like Scikit-Learn, TensorFlow, Keras Regression & Classification Models, Linear Regression & Multiple Regression Logistic Regression & Decision Trees Random Forests, Support Vector Machine Model Evaluation: RMSE, R², Confusion Matrix, Precision, Recall
  • Un-Supervised Learning:K-Means Clustering, K-Nearest Neighbor (K-NN) Principal Components Analysis (PCA), Pipeline, Model Persistence and Evaluation, Building a model for prediction Building & Deploying ML Models - Hyperparameter Tuning Final Project
  • "Understanding Data Science & its Applications, Role of a Data Scientist & Industry Use Cases, Data Science Tools: Python, SQL, Excel, Power BI " Introduction to SQL & Databases, Working with Relational Databases, Basic SQL Queries: SELECT, INSERT, UPDATE, DELETE, CONSTRAINTS in SQL Filtering & Aggregation: WHERE, GROUP BY, HAVING, ORDER BY Joins & Subqueries: INNER JOIN, LEFT JOIN, RIGHT JOIN Advanced SQL: Window Functions
  • Excel for Data Analysis : Data Cleaning & Manipulation Essential Formulas: LOOKUP, INDEX-MATCH, DATE/TIME , TEXT functions Power Query Pivot Tables, Pivot Charts, Slicers and Timelines Hands-on: Analyzing Datasets in Excel
  • Introduction to PowerBI interface, Importing Data into Power BI, Data Cleaning Building and Structuring Data Models Advanced Calculations with DAX Designing and Enhancing Visual Reports Map Visuals: Using geographic data for map visuals, Adding slicers for filtering data, Filtering Options - TOP N filtering
  • Python Fundamentals : Data Types, Lists, Tuples, Dictionaries, Loops NumPy for Numerical Computing : Creating & Manipulating Arrays, Mathematical & Statistical Operations, Data Cleaning Pandas for Data Manipulation, Loading & Cleaning Data (Handling Missing Values) Data Transformation & Feature Engineering, Merging, Filtering, and Grouping Data Matplotlib & Seaborn for Data Visualization
  • Introduction to Machine Learning, Supervised vs. Unsupervised Learning, Overview of Libraries like Scikit-Learn, TensorFlow, Keras Regression & Classification Models, Linear Regression & Multiple Regression Logistic Regression Decision Trees Model Evaluation: RMSE, R², Confusion Matrix, Precision, Recall
  • Un-Supervised Learning:K-Means Clustering Principal Components Analysis (PCA) Model Persistence and Evaluation, Building a model for prediction Building & Deploying ML Models - Hyperparameter Tuning Final Project

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Sandeep kaur
Assistant Professor

I am an Assistant Professor and Mentor dedicated to fostering academic excellence and personal growth. With more than 7 years of experience in higher education, I have had the privilege of instructing and mentoring a diverse range of students in the field of Data Science and Web Development. Alongside my teaching, I also work on freelance projects, provide placement-oriented training sessions and mentor students to achieve their goals. My expertise extends to programming languages such as Python and R, data visualisation tools like Tableau and PowerBI, performing Data Analysis using Microsoft Excel. I am proficient in SQL for efficient data retrieval and manipulation,and have experience working with ETL tools such as SSIS and Informatica. Moreover, I have worked on Projects using Front-End Technologies like React/Angular. I actively engage in freelance projects, continuous professional development, attending industry conferences, participating in online courses, and contributing to data science communities.