Data Science

1. Introduction to Data Science/ Analysis

Overview of Data Science,

Importance and Applications of Data Science,

Types of Data (Structured, Unstructured),

Data Science Lifecycle.

2. Python for Data Science

Python Basics (Variables, Data Structures, Functions),
Libraries: NumPy, Pandas, Matplotlib, Seaborn,
Data Manipulation with Pandas, Data Visualization
with Matplotlib and Seaborn

3. Data Preprocessing

Handling Missing Data, Data Cleaning and Transformation,
Data Normalization and Standardization
Feature Engineering and Selection

4. Exploratory Data Analysis (EDA)

Descriptive Statistics (Mean, Median, Mode, Variance, etc.)
Data Visualization Techniques
Histograms, Boxplots, Scatter Plots
Identifying Patterns and Outliers in Data

5. Probability and Statistics for Data Science

Probability Theory Basics, Random Variables and Distributions,
Hypothesis Testing (t-tests, Chi-square), Statistical Inference
6. Machine Learning

Introduction to Machine Learning, Supervised Learning
(Linear Regression,
Logistic Regression) , Unsupervised Learning (K-Means Clustering, PCA)
Model Evaluation (Accuracy, Precision, Recall, F1-Score)
7. Advanced Machine Learning Algorithms

Decision Trees and Random Forests, Support Vector Machines (SVM)
K-Nearest Neighbors (KNN), Neural Networks and Deep

8. Model Deployment

Model Deployment Concepts
Introduction to Cloud Platforms (AWS, Azure, GCP)
Creating and Deploying Machine Learning Models
Version Control for Models (Git, Docker)
9. Big Data Technologies

Introduction to Big Data
Hadoop Ecosystem
Spark and its Applications
Data Storage Systems (SQL, NoSQL)
10. Data Science Ethics and Privacy

Ethical Issues in Data Science
Bias in Data and Models
Data Privacy Regulations (GDPR, CCPA)
11. Case Studies & Projects

Industry Use Cases of Data Science,

Hands-on Projects (E.g., Predictive Analytics,

Recommendation Systems)

Project on Data

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