Data Science using Python Training in Chandigarh

Data Science Using Python

Kickstart your Data Science career with our Data Science Foundation Program with Python. This program focuses on hands-on learning with real-world datasets. Develop skills in data analysis, visualization, and generating business insights using Python—aligned with industry needs.


📘 Module 1: Introduction to Data Science & Python
  • What is Data Science & its applications
  • Data Science lifecycle
  • Introduction to Python
  • Installing Python & Anaconda
  • Jupyter Notebook setup
  • Variables & Data Types
  • Operators & Expressions
  • Input & Output functions
📘 Module 2: Core Python Programming
  • Conditional Statements (if, else, elif)
  • Loops (for, while)
  • Lists, Tuples, Sets, Dictionaries
  • Functions & Lambda functions
  • File Handling (CSV, text files)
  • Exception Handling (try-except)
📘 Module 3: NumPy (Numerical Computing)
  • Introduction to NumPy
  • Array creation & operations
  • Indexing & slicing
  • Mathematical operations
  • Statistical functions (mean, median, std)
  • Broadcasting
  • Working with large datasets
📘 Module 4: Pandas (Handling Files)
  • Introduction to Pandas (Series & DataFrame)
  • Loading datasets (CSV, Excel)
  • Data exploration (head, info, describe)
  • Data cleaning:
    • Handling missing values
    • Removing duplicates
  • Data manipulation:
    • Filtering
    • Sorting
    • GroupBy & Aggregation
  • Merging & Joining datasets
  • Working with date & time data
📘 Module 5: Data Visualization
  • Introduction to data visualization
  • Matplotlib:
    • Line chart
    • Bar chart
    • Histogram
    • Pie chart
  • Seaborn:
    • Heatmaps
    • Pairplots
    • Distribution plots
  • Graph customization (labels, titles, styles)
  • Choosing the right chart for business problems
📘 Module 6: Exploratory Data Analysis (EDA)
  • Understanding data types
  • Statistical analysis (mean, median, correlation)
  • Outlier detection using boxplots
  • Pattern & trend analysis
  • Generating business insights
  • Case study: Sales & customer behavior
📘 Module 7: Introduction to Machine Learning
  • What is Machine Learning
  • Types: Supervised & Unsupervised
  • ML workflow
  • Linear Regression (basic implementation)
  • Model evaluation (basic accuracy concept)
📘 Module 8: Hands-on Projects
  • Sales Data Analysis Project
  • IPL Dataset Analysis
  • Netflix Data Analysis
  • End-to-end mini project with insights
📘 Module 9: Tools & Career Preparation
  • Working with Jupyter Notebook
  • GitHub basics (upload projects)
  • Resume building for Data Analyst roles
  • Interview preparation (Python + Data Science )