LPU - Lovely Professional University
What you'll learn

Description:  This course is designed to: 1.To gain a solid foundation in data science principles and techniques using Python, covering data manipulation, analysis, and visualization with popular libraries like Pandas, NumPy, Matplotlib, and Seaborn. 2.To explore basic machine learning and natural language processing concepts, including hands-on implementation with scikit-learn and nltk, along with model evaluation techniques. 3.Designed for both beginners and professionals, this course emphasizes practical, real-world applications to develop essential data science skills for making career in related fields. Learning Outcome: 1)To gain proficiency in Python for data analysis and visualization. 2)To acquire skills to manipulate and interpret complex datasets. 3)To build foundational knowledge in machine learning and Natural Language Processing techniques. 4)To prepare for careers in data analytics and related fields.

  • 1.Introduction to Python IDEs (e.g., Jupyter Notebook, PyCharm, VS Code) 2.Python control flow structures (if, elif, else, for, while etc.) 3.Python data structures (string, list, tuple, dictionary, set etc.) 4.Python Functions (built-in and user-defined)
  • 1.Introduction to NumPy and its functionality 2.NumPy 1D, 2D arrays handling 3.NumPy array Indexing and Slicing 4.NumPy math’s and aggerate functions 5.NumPy array operations and missing values
  • 1.Introduction to Pandas and its functionality 2.Working with datasets and csv files 3.Data cleaning and pre-processing using Pandas 4.Grouping and aggregation of data inside datasets 5.Data manipulation (sorting, apply, map, reduce etc.)
  • 1.Introduction to machine learning and its usage 2.Regression analysis for future prediction (linear and non-linear) 3.Supervised vs unsupervised machine learning 4.Support Vector Machine and K-Nearest Neighbor classification 5.Decision Tree and Random Forest classification 6.K-means clustering
  • 1.Introduction to Natural Language Processing (NLP) 2.NLP using nltk python library 3.Evaluating and validating machine learning models 4.Performance metrics (accuracy, precision, recall, f1-score etc.) 5.Cross validation and hyper-parameter tuning
  • 1.Introduction to Matplotlib python library 2.Exploratory Data Analysis (EDA) Visualizations 3.Visualizing machine learning model’s performance 4.Visualizing Clusters for outliers detection 5.Data visualization using Seaborn python library

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Dr. Ajay Rastogi
Assistant Professor

Dr. Ajay Rastogi received the master’s and Ph.D. degrees in computer science from Jamia Millia Islamia, New Delhi, India, in 2013, 2021, respectively. Currently, he is an Assistant Professor with Lovely Professional University, Phagwara, India. His research interests include data mining, machine learning, graph mining, complex networks, and social computing.