Intro to Data Science

Understand the fundamentals of data science, its applications, and career paths. Learn how data drives decision-making across industries.

Math Basics

Covers linear algebra, probability, and calculus concepts essential for data science. Helps build a strong foundation for statistical modeling.

Python

Learn Python programming with NumPy, Pandas, and Matplotlib. Perform data manipulation, analysis, and visualization efficiently.

R Programming

Master R for statistical computing and visualization. Use ggplot2, dplyr, and other libraries for data analysis.

Data Cleaning

Preprocess raw data by handling missing values, duplicates, and inconsistencies. Ensure data quality for accurate analysis.

EDA (Exploratory Data Analysis)

Analyze data patterns, distributions, and relationships using visualization and statistical methods. Gain insights for model building.

SQL

Learn SQL queries to extract, filter, and manipulate structured data. Work with databases efficiently for analysis.

Data Visualization

Create compelling charts and graphs using Matplotlib, Seaborn, and Plotly. Present insights visually for better decision-making.

ML Basics

Understand machine learning fundamentals, including bias-variance tradeoff and overfitting. Learn how models learn from data.

Regression

Apply linear, ridge, and lasso regression for predictive modeling. Learn how to assess model accuracy.

Classification

Use logistic regression, decision trees, and SVM to classify data into different categories. Learn performance evaluation metrics.

Clustering

Group similar data points using K-Means, DBSCAN, and hierarchical clustering. Identify patterns in large datasets.

Deep Learning

Explore neural networks, backpropagation, and activation functions. Learn how AI mimics human intelligence.

TensorFlow/Keras

Build and train deep learning models using TensorFlow and Keras. Implement image and text recognition.

NLP (Natural Language Processing)

Process text data for sentiment analysis, chatbots, and AI-driven content generation. Learn tokenization and word embeddings.

Time Series Analysis

Analyze trends and make future predictions using ARIMA, LSTMs, and Prophet models. Ideal for stock price forecasting.

Recommender Systems

Learn collaborative and content-based filtering for personalized recommendations. Used in Netflix and Amazon.

Feature Engineering

Improve model accuracy by selecting and transforming data features. Handle categorical, numerical, and missing values.

Model Tuning

Optimize models using hyperparameter tuning, grid search, and cross-validation for better performance.

Big Data

Work with Hadoop, Spark, and distributed computing. Handle large-scale datasets efficiently.

Cloud AI

Deploy AI models on AWS, GCP, and Azure. Learn cloud-based data storage and processing.

Business Analytics

Use data-driven insights for business strategy and decision-making. Learn key metrics and KPIs.

AI Ethics

Understand bias in AI models and ethical AI development. Ensure fairness and transparency in data science.

Computer Vision

Build models for image recognition, object detection, and facial recognition. Apply CNNs for deep learning in vision.

Reinforcement Learning

Train AI agents using rewards and penalties. Apply RL in robotics and gaming.

AutoML

Forecast trends using historical time-dependent data. Apply ARIMA and exponential smoothing models for prediction.

Model Deployment

Use Flask, FastAPI, and Docker to deploy ML models into production-ready applications.

AI for IoT

Integrate AI with IoT devices for smart automation. Process real-time sensor data.

Advanced ML

Explore semi-supervised learning, transfer learning, and ensemble models for improved performance.

AI in Finance

Predict stock market trends, detect fraud, and optimize trading strategies using AI.

AI in Healthcare

Use AI for medical diagnosis, drug discovery, and patient care optimization.

Marketing Analytics

Analyze customer behavior, segment audiences, and improve ad targeting using data.

Explainable AI

Make AI decisions transparent with SHAP, LIME, and interpretable ML models.

MLOps

Learn CI/CD pipelines for ML models. Automate deployment and monitoring.

Generative AI

Explore GPT, BERT, and diffusion models for AI-generated content and deepfakes.

AI & Blockchain

Combine AI with blockchain for secure data processing and decentralized applications.

Data Engineering

Use data Science to track blockchain transactions. Monitor cryptocurrency trends and detect fraud.

Projects

Work on hands-on projects with real-world datasets. Apply knowledge to practical scenarios.

Job Prep

Build and maintain data pipelines for Science. Learn about ETL processes, data lakes, and warehousing.

Case Studies & Projects