Data Science Batch Roadmap

Join our Data Science Batch and master Python, Machine Learning, Deep Learning, NLP, and Generative AI. Build real-world projects and kickstart your data science career.

1. Python Basics

  • Syntax, Variables, and Data Types
  • Conditional Statements and Loops
  • Functions and Modules
  • File Handling
  • Exception Handling

2. Advanced Python

  • Object-Oriented Programming (OOP)
  • Generators and Iterators
  • Decorators
  • Working with APIs
  • Multithreading and Multiprocessing

3. Master Data Manipulation and Analysis

1. Libraries for Data Analysis

  • NumPy: Array manipulations, mathematical operations
  • Pandas: DataFrames, data cleaning, aggregation, and manipulation

2. Data Visualization

  • Matplotlib: Basic plotting
  • Seaborn: Statistical data visualization
  • Plotly: Interactive visualizations

3. SQL

  • Basics of SQL: SELECT, INSERT, UPDATE, DELETE
  • Joins, Subqueries, and Aggregations
  • Writing efficient queries for large datasets

4. Learn Statistics and Mathematics

Statistics

  • Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
  • Probability and Distributions
  • Hypothesis Testing and p-values

5. Learn Machine Learning

1. Introduction to Machine Learning

  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Train-Test Split, Cross-Validation

2. Supervised Learning

  • Regression: Linear Regression, Polynomial Regression
  • Optimization Techniques (Gradient Descent)
  • Classification: Logistic Regression, Decision Trees, Random Forests, SVM

3. Unsupervised Learning

  • Clustering: K-Means Clustering
  • Dimensionality Reduction: PCA

4. Model Evaluation

  • Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
  • Overfitting and Underfitting
  • Hyperparameter Tuning: Grid Search, Random Search

5. Deep Learning

  • Basics of Neural Networks
  • Advanced Topics: CNNs, RNNs, GANs

6. Work on Data Science Tools

  • Jupyter Notebook: Interactive Python environment
  • VS Code

7. Explore Natural Language Processing (NLP)

  • Tokenization and Stopwords
  • TF-IDF and Bag of Words
  • Sentiment Analysis
  • Word Embeddings: Word2Vec, GloVe
  • Transformers and BERT

8. Learn Generative AI

1. Introduction to Generative Models

  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)

2. Transformers

  • Attention Mechanism
  • Transformer Architecture (BERT, GPT)

3. Applications of Generative AI

  • Text Generation
  • Conversational AI (Q/A)

4. Fine-tuning Generative Models

  • Fine-tune GPT models with domain-specific data
  • Use libraries like Hugging Face and LangChain

9. Build Projects

1. Beginner Projects

  • Sales Analysis Dashboard
  • Movie Recommendation System

2. Intermediate Projects

  • Predict House Prices
  • Sentiment Analysis on Twitter Data

3. Advanced Projects

  • Generative AI Chatbot for Customer Support
  • AI-Powered Content Generation Tool

10. Learn Deployment

  • Flask for building APIs
  • Streamlit/Dash for creating web apps
  • Deployment on AWS/GCP/Heroku

11. Build a Portfolio

  • Upload your projects on GitHub
  • Create a LinkedIn profile showcasing your skills and projects
  • Write blogs about your data science journey and projects on Medium/Kaggle
Enroll Now