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Data Science & Machine Learning

Learn Data Science from scratch — analyze data, build machine learning models, and gain skills required for high-demand data-driven careers.

Course Description

This course covers complete Data Science workflow including Python programming, data analysis, visualization, statistics, and machine learning. You will work on real-world datasets and build practical projects.

Detailed Course Syllabus

L.N. Contents Hours
1
  1. Introduction to Data Science
  2. Role of Data Scientist
  3. Python Basics for Data Science
 3
2
  1. Python Libraries
    • NumPy
    • Pandas
    • Matplotlib
    • Seaborn
 3
3
  1. Data Cleaning & Preprocessing
    • Handling Missing Data
    • Data Transformation
    • Feature Engineering
 3
4
  1. Exploratory Data Analysis (EDA)
    • Data Visualization
    • Pattern Identification
 3
5
  1. Statistics Basics
    • Mean, Median, Mode
    • Probability
    • Distributions
 3
6
  1. Machine Learning Introduction
    • Supervised Learning
    • Unsupervised Learning
 3
7
  1. Regression Algorithms
    • Linear Regression
    • Multiple Regression
 3
8
  1. Classification Algorithms
    • Logistic Regression
    • Decision Trees
    • KNN
 3
9
  1. Model Evaluation
    • Accuracy, Precision, Recall
    • Confusion Matrix
 3
10
  1. Advanced Topics
    • Clustering
    • Time Series Basics
 3
11
  1. Projects
    • Real Dataset Analysis
    • ML Model Deployment Basics
  2. Interview Preparation
 3