Data science

Course Rate: INR 20000.00

Duration: 2 Months

About the Course:

Data Science syllabus covers fundamental to advanced** topics, including programming, statistics, machine learning, and big data. It is ideal for **beginners, professionals, and researchers** looking to build expertise in **data-driven decision-making**.

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## **1. Introduction to Data Science**
- What is Data Science?
- Applications of Data Science
- Data Science vs Data Analytics vs Machine Learning vs AI
- Data Science Life Cycle
- Roles & Responsibilities of a Data Scientist
- Tools for Data Science (Python, R, SQL, Excel, Hadoop, Spark)

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## **2. Programming for Data Science**
### **Python for Data Science**
- Python Basics (Variables, Data Types, Operators)
- Control Flow (Loops, Conditional Statements)
- Functions & Modules
- File Handling in Python
- Exception Handling
- Object-Oriented Programming in Python

### **R for Data Science (Optional)**
- Basics of R Programming
- Data Manipulation in R
- Data Visualization in R
- R Libraries for Data Science (ggplot2, dplyr, caret)

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## **3. Mathematics & Statistics for Data Science**
### **Linear Algebra**
- Vectors & Matrices
- Eigenvalues & Eigenvectors
- Matrix Operations

### **Probability & Statistics**
- Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Probability Theory (Bayes' Theorem, Conditional Probability)
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing (T-test, Chi-Square Test, ANOVA)
- Confidence Intervals & P-Values

### **Calculus for Machine Learning**
- Differentiation & Integration Basics
- Partial Derivatives & Gradient Descent
- Optimization Techniques

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## **4. Data Manipulation & Processing**
### **Data Handling with Pandas & NumPy (Python)**
- Loading & Reading Data (CSV, Excel, JSON, SQL)
- Data Cleaning (Handling Missing Data, Duplicates)
- Data Transformation & Feature Engineering
- Exploratory Data Analysis (EDA)

### **Data Visualization**
- Matplotlib & Seaborn for Data Visualization
- Advanced Plotting (Histograms, Boxplots, Scatter Plots)
- Interactive Visualizations (Plotly, Dash)

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## **5. Machine Learning (ML)**
### **Introduction to ML**
- What is Machine Learning?
- Types of ML (Supervised, Unsupervised, Reinforcement Learning)
- ML Workflow & Model Selection

### **Supervised Learning**
- Linear Regression & Multiple Regression
- Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- Naïve Bayes Classifier

### **Unsupervised Learning**
- Clustering (K-Means, Hierarchical Clustering, DBSCAN)
- Principal Component Analysis (PCA)

### **Model Evaluation & Validation**
- Train-Test Split
- Cross-Validation
- Performance Metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve)
- Hyperparameter Tuning (Grid Search, Random Search)

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## **6. Deep Learning & Neural Networks**
### **Introduction to Deep Learning**
- What is Deep Learning?
- Neural Networks Basics
- Activation Functions (ReLU, Sigmoid, Softmax)

### **Deep Learning Frameworks**
- TensorFlow Basics
- Keras for Deep Learning

### **Deep Learning Models**
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) & LSTMs
- Autoencoders
- Generative Adversarial Networks (GANs)

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## **7. Natural Language Processing (NLP)**
- Introduction to NLP
- Text Preprocessing (Tokenization, Lemmatization, Stemming, Stopwords Removal)
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Classification using Machine Learning
- Transformers & BERT Model

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## **8. Big Data & Cloud Computing**
- Introduction to Big Data & Hadoop
- Apache Spark for Large-Scale Data Processing
- Cloud Platforms for Data Science (AWS, GCP, Azure)
- Deploying ML Models on the Cloud

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## **9. Time Series Analysis**
- Introduction to Time Series Data
- Moving Averages & Exponential Smoothing
- ARIMA & SARIMA Models
- LSTMs for Time Series Forecasting

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## **10. Reinforcement Learning (Optional)**
- Basics of Reinforcement Learning
- Markov Decision Processes (MDP)
- Q-Learning & Deep Q Networks (DQN)

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## **11. Model Deployment & MLOps**
- Saving & Loading ML Models (`joblib`, `pickle`)
- Deploying Models with Flask & FastAPI
- Docker for ML Deployment
- CI/CD for Machine Learning Models

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## **12. Advanced Topics in Data Science**
- Anomaly Detection
- Recommender Systems (Collaborative Filtering, Content-Based Filtering)
- Ethical Considerations in AI & Data Science
- Edge AI & TinyML

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### **13. Real-World Projects**
- **Customer Churn Prediction**
- **Fraud Detection**
- **Stock Price Prediction**
- **Speech Recognition System**
- **Chatbot Development**

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