Curriculum
16 Sections
40 Lessons
16 Weeks
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Module 1: Python Fundamentals for Data Science
3
1.1
Introduction to Python
1.2
Variables, data types, input/output
1.3
Functions and basic error handling
Module 2: Python Data Structures
3
2.1
Lists, tuples, sets, dictionaries
2.2
Nested data structures
2.3
Practical usage in data-related tasks
Module 3: NumPy for Numerical Computing
2
3.1
Indexing, slicing, reshaping
3.2
Mathematical functions and broadcasting
Module 4: Data Analysis with Pandas
3
4.1
Series and DataFrames
4.2
Reading/writing CSV, Excel, JSON
4.3
Filtering, grouping, merging, and pivoting data
Module 5: Data Visualization with Matplotlib & Seaborn
3
5.1
Customizing visuals for presentations
5.2
Line plots, bar charts, histograms
5.3
Scatter plots, heatmaps, pairplots
Module 6: Data Cleaning & Preprocessing
3
6.1
Handling missing values
6.2
Data transformation and encoding
6.3
Feature scaling and normalization
Module 7: Exploratory Data Analysis (EDA)
2
7.1
Univariate and multivariate analysis
7.2
Visual EDA projects with real-world data
Module 8: Introduction to Machine Learning
3
8.1
What is ML? Types of ML (Supervised, Unsupervised)
8.2
ML lifecycle overview
8.3
Model evaluation metrics (accuracy, precision, recall, F1-score)
Module 9: Supervised Learning – Regression
3
9.1
Multiple Linear Regression
9.2
Hands-on mini-projects using Scikit-learn
9.3
Linear Regression and its assumptions
Module 10: Supervised Learning – Classification
2
10.1
KNN, Logistic Regression, Decision Trees
10.2
Model training, testing, and validation
Module 11: Ensemble Techniques
3
11.1
Random Forest
11.2
Gradient Boosting, AdaBoost
11.3
Voting and Bagging methods
Module 12: Unsupervised Learning – Clustering
2
12.1
K-Means, Hierarchical Clustering
12.2
Dimensionality reduction using PCA
Module 13: Natural Language Processing (NLP) Basics
3
13.1
Text cleaning and preprocessing
13.2
Sentiment analysis using ML
13.3
Tokenization, stop words, stemming, lemmatization
Module 14: Introduction to Deep Learning
2
14.1
Intro to TensorFlow/Keras
14.2
What is Deep Learning?
Module 15: Capstone Project – End-to-End ML Pipeline
1
15.1
Submit report and present findings
Module 16: Career & Portfolio Development
2
16.1
Resume & LinkedIn tips for data science
16.2
Job roles in data science and interview guidance
Data Science & Machine Learning with Python – Zero to Pro
Curriculum
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