Python for Data Science in East Delhi

• Learn Python fundamentals such as variables, data types, conditionals, and loops.

• Understand Python’s role in modern data science workflows.

• Get comfortable with writing clean, readable, and reusable Python code.

• Work with Jupyter Notebooks for hands-on learning and experimentation.

• Declaring variables
• Numbers, strings, and booleans
• Type conversion and input() function
• Best practices for naming variables

• Arithmetic, relational, and logical operators
• Operator precedence
• Evaluating expressions
• Simple coding problems

Series and DataFrames

Data cleaning, filtering, aggregation

Merging, joining, and reshaping data

Line, bar, histogram, scatter plots

Custom styling and layouts

Seaborn for correlation, heatmaps, box plots

Exploratory data analysis (EDA)

Importing datasets from CSV, Excel, APIs

End-to-end mini project: From loading to visualization

• Learn basic ML concepts like supervised vs unsupervised learning.

• Build models like Linear Regression and K-Nearest Neighbors.

• Split data into training and testing sets for performance evaluation.

• Use Scikit-learn pipelines for model building and validation.

• Solve data science problems using real datasets from Kaggle and UCI.

• Projects may include sales prediction, customer segmentation, or COVID analysis.

• Use complete workflows from data loading to model evaluation.

• Get personalized feedback from mentors on your project work.

• Learn about arrays, vectors, and matrices using NumPy.

• Perform fast mathematical and statistical operations on large datasets.

• Understand broadcasting, indexing, slicing, and reshaping arrays.

• Integrate NumPy with Pandas for efficient data pipelines.

• Work on a comprehensive end-to-end project that includes all concepts.

• Choose a dataset of your interest or get one from the instructor.

• Present your findings using visualizations and model results.

• Receive a certificate of completion that adds weight to your resume.