Data Science Course Curriculum

A Data Science Course Curriculum typically covers programming, statistics, machine learning, data handling, and real-world applications. Here’s a detailed course structure:

📌 Module 1: Introduction to Data Science

  1. What is Data Science?

  2. Applications in industries (Finance, Healthcare, E-commerce, etc.)

  3. Data Science Lifecycle

  4. Overview of tools: Python, R, SQL, Excel

  5. Understanding AI, ML, and Big Data

📌 Module 2: Programming for Data Science (Python & R)

Python for Data Science

  1. Python basics: Variables, Data types, Loops, Functions

  2. NumPy for numerical computing

  3. Pandas for data manipulation

  4. Matplotlib & Seaborn for data visualization

  5. Handling missing data, outliers

R for Data Science (Optional)

  1. R basics: Vectors, Lists, Data Frames

  2. ggplot2 for visualization

  3. dplyr & tidyr for data manipulation

📌 Module 3: Mathematics & Statistics for Data Science

  1. Descriptive Statistics: Mean, Median, Mode, Variance

  2. Probability Theory: Bayes’ Theorem, Normal Distribution

  3. Inferential Statistics: Hypothesis Testing, p-values

  4. Linear Algebra: Matrices, Eigenvalues

  5. Calculus: Derivatives, Gradient Descent (for ML)

📌 Module 4: SQL & Databases

  1. Introduction to Databases & SQL

  2. Writing SQL Queries (SELECT, JOIN, GROUP BY)

  3. NoSQL Databases (MongoDB basics)

  4. Data Warehousing concepts

📌 Module 5: Data Wrangling & Preprocessing

  1. Handling missing data

  2. Data Cleaning techniques

  3. Feature Engineering

  4. Normalization & Standardization

  5. Outlier detection & removal

📌 Module 6: Exploratory Data Analysis (EDA)

  1. Understanding data distributions

  2. Visualization techniques (Histograms, Scatter Plots, Box Plots)

  3. Correlation & Causation

  4. Dimensionality Reduction (PCA, t-SNE)

📌 Module 7: Machine Learning Fundamentals

  1. Supervised vs Unsupervised Learning

  2. Linear Regression & Multiple Regression

  3. Logistic Regression

  4. Decision Trees & Random Forests

  5. Support Vector Machines (SVM)

  6. k-Nearest Neighbors (KNN)

  7. Naïve Bayes Classifier

  8. Model evaluation: RMSE, Accuracy, Confusion Matrix

📌 Module 8: Advanced Machine Learning

  1. Ensemble Learning (Bagging, Boosting)

  2. XGBoost, LightGBM, CatBoost

  3. Feature Selection & Hyperparameter Tuning

  4. Model Deployment (Flask, FastAPI)

📌 Module 9: Deep Learning & Neural Networks

  1. Introduction to Neural Networks

  2. Backpropagation & Optimization

  3. Convolutional Neural Networks (CNN)

  4. Recurrent Neural Networks (RNN, LSTM)

  5. Transfer Learning (ResNet, VGG)

  6. Deep Learning Frameworks: TensorFlow, PyTorch

📌 Module 10: Natural Language Processing (NLP)

  1. Text Preprocessing (Tokenization, Lemmatization)

  2. Sentiment Analysis

  3. Word Embeddings (Word2Vec, GloVe, BERT)

  4. Chatbot Development

📌 Module 11: Time Series Analysis

  1. Moving Averages, Exponential Smoothing

  2. ARIMA, SARIMA Models

  3. Forecasting & Anomaly Detection

📌 Module 12: Big Data & Cloud Computing

  1. Introduction to Hadoop & Spark

  2. Google BigQuery & AWS S3

  3. Stream Processing (Kafka)

📌 Module 13: Data Science in Real-World Projects

  1. Case Studies (E-commerce, Finance, Healthcare)

  2. End-to-End Data Science Project

  3. Model Deployment using Flask/FastAPI

  4. Data Science Interview Preparation

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