LPU - Lovely Professional University
What you'll learn

By the end of this course, learners will be able to: Apply Python programming for data science tasks Perform data cleaning, manipulation, and analysis using libraries such as NumPy and Pandas Create data visualizations using tools like Matplotlib and Seaborn Understand and implement basic machine learning models using Scikit-learn Gain awareness of additional data science tools and libraries (e.g., TensorFlow, Keras) Develop an end-to-end data science project using real-world data

  • This week introduces the foundational concepts of Python programming required for data science. Students learn core programming constructs such as variables, data types, operators, control flow, functions, and file handling. Practical exercises based on real-life scenarios like student marks and attendance systems help in building logical thinking and coding skills.
  • This week focuses on data manipulation and analysis using powerful Python libraries such as NumPy and Pandas. Students learn how to load, clean, and transform data, handle missing values, and perform operations like filtering, grouping, and aggregation. Emphasis is placed on preparing raw data for analysis.
  • This week emphasizes visual representation of data using tools like Matplotlib and Seaborn. Students learn different types of plots, advanced visualization techniques, and how to interpret data visually. The concept of data storytelling is introduced to help students present insights effectively. A continuous assessment (CA 1) evaluates their understanding.
  • This week introduces core machine learning concepts using Scikit-learn. Students explore supervised learning algorithms such as Linear Regression, Logistic Regression, KNN, and Decision Trees. They also learn the machine learning workflow, model training, prediction, and evaluation techniques including accuracy and confusion matrix, along with concepts like overfitting and underfitting.
  • This week focuses on advanced concepts and practical implementation. Students learn unsupervised learning (clustering using K-Means), feature engineering, and data scaling techniques. They build an end-to-end machine learning pipeline and work on a mini project involving problem selection, data analysis, model building, and evaluation. This week enhances practical skills and prepares students for real-world applications.

Avatar
Pallvi
Assistant Professor

I am an Assistant Professor specializing in Python programming, Data Science, and AI/ML. My teaching focuses on combining core concepts with hands-on practice to help students build strong analytical and problem-solving skills. I aim to prepare learners to apply data-driven and intelligent solutions to real-world challenges.


Avatar
Rahul Kapoor
Senior Software Developer

Rahul Kapoor is a Senior Software Developer with expertise in AI and Machine Learning. He has worked on multiple projects involving intelligent systems and data-driven solutions. His experience includes developing practical applications using modern technologies, with a strong focus on problem-solving and innovation.