skilldevelopment.cpe@lpu.co.in

What you'll learn

Description: This course is designed to: • Help students in attempting competitive exams related to Python programming in an effective manner. • Students will have in depth clarity of the basics of Python Programming, use of data structures and solving of problems using it. • Help students to crack programming based placement exams. Learning Outcome: • Students will be well - versed with type of questions for various competitive exams and Hackathons. • Students will be able to apply strategies while solving Python programming related questions in the paper. • Learning Python Programming and its applications • Students will be well equipped while attending interviews • Bolster the chances of getting success in the placement drives.

  • Getting started with Python: Installation, setup, and IDEs, Python basics: Variables, data types, and basic operators, Control flow: If statements, loops, and logical operators, Functions: Defining functions, parameters, and return statements, Modules and packages: Importing modules, creating packages.
  • Advanced data types: Lists, tuples, dictionaries, and sets, String manipulation and formatting File handling: Reading from and writing to files, Exception handling: Try-except blocks, raising exceptions, Object-oriented programming (OOP) basics: Classes, objects, methods.
  • Introduction to NumPy: Arrays, indexing, and slicing, NumPy operations: Arithmetic, broadcasting, and aggregation, Introduction to pandas: Series, DataFrames, and basic operations, Data cleaning and preprocessing with pandas, Grouping and aggregation with pandas.
  • Introduction to Matplotlib: Basic plotting techniques, Customizing plots: Labels, titles, legends, and annotations, Introduction to Seaborn: Statistical data visualization, Advanced visualization techniques: Histograms, box plots, and heatmaps, Interactive visualization with Plotly
  • Overview of machine learning and its applications, Introduction to scikit-learn library for machine learning in Python, Supervised learning algorithms: Linear regression, logistic regression, Model evaluation: Cross-validation, metrics, and scoring, Unsupervised learning algorithms: Clustering, dimensionality reduction.
  • Hands-on project: Applying Python programming, data manipulation, visualization, and machine learning techniques to a real-world dataset, Project Improvement, and presentation preparation.

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Harjeet Kaur
Assistant Professor

Ms. Harjeet is currently working as Assistant Professor in the School of Computer Science & Engineering at Lovely Professional University. She has 25+ years of experience as a Data Structure and Algorithms tutor in various colleges. He have conducted lots of workshops and skill development courses on programming languages, data structures and algorithms.