LPU - Lovely Professional University
What you'll learn

This 30-hour skill development course, “Data to Decisions: Applied Data Science with Python”, is designed to provide learners with a strong foundation in data science using Python. The course focuses on practical skills required to collect, process, analyze, and visualize data for real-world decision-making. Participants will learn how to work with data using powerful Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn. The course emphasizes hands-on learning through real datasets, enabling students to understand data preprocessing, exploratory data analysis (EDA), and basic predictive modeling techniques. By the end of the course, learners will be able to transform raw data into meaningful insights and build simple data-driven solutions applicable across domains such as business, healthcare, and technology.

  • This unit introduces the fundamentals of data science, including its lifecycle, applications, and importance in modern decision-making. Learners will get familiar with Python programming essentials such as variables, data types, control structures, functions, and basic input/output operations. The focus is on building a strong programming foundation required for data analysis.
  • This unit covers data manipulation and numerical computing using Python libraries. Students will learn how to create and manipulate arrays using NumPy and work with structured datasets using Pandas DataFrames. Key topics include data loading, indexing, slicing, filtering, aggregation, and handling missing values.
  • This unit focuses on preparing raw data for analysis. Learners will understand techniques for handling missing data, removing duplicates, data transformation, normalization, and encoding categorical variables. Emphasis is placed on improving data quality to ensure accurate and meaningful analysis.
  • In this unit, students will explore datasets to identify patterns, trends, and relationships. Topics include descriptive statistics, correlation analysis, and feature understanding. Learners will use Pandas and visualization tools to summarize and interpret data effectively.
  • This unit introduces visualization techniques using Matplotlib and Seaborn. Students will learn to create various plots such as bar charts, histograms, scatter plots, and heatmaps. The focus is on communicating insights clearly through visual storytelling and dashboards.
  • This unit provides a basic introduction to machine learning concepts, including supervised learning and simple models like linear regression. Students will implement models using Scikit-learn and evaluate performance. The unit culminates in a mini project where learners apply the complete data science workflow to a real-world dataset.

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Dr. Manpreet Singh Sehgal
Associate Professor

Dr. Manpreet Singh Sehgal has been a data scientist from IIT Roorkee and is currently working as Associate Professor in the School of AI and Emerging Technologies. He has near 24 years of experience in academia and research