Curriculum and Learning Track

Start your data science journey without coding! Learn to analyze, visualize, and build machine learning models using no-code tools, making data science accessible to everyone. This module covers data science roles, AI vs ML vs DS vs DL, data cleaning, preprocessing, statistical analysis, feature engineering, supervised & unsupervised learning, model evaluation, and deployment. 

Master Python from the ground up for data science applications! Learn Python basics, file handling with Pandas, and explore essential libraries for data analysis. This module also covers data preprocessing, data wrangling, and setting up virtual environments for seamless workflow management. 

Learn to manipulate, analyze, and visualize data using tools such as Matplotlib and Seaborn to uncover hidden patterns and insights.

Build a strong statistical foundation by exploring descriptive and inferential statistics, probability theories, and hypothesis testing.​

Enhance your data for better model performance! Learn advanced feature engineering, dimensionality reduction techniques like PCA & t-SNE, and automate preprocessing with tools like ColumnTransformer and Pipelines. 

Understand the fundamentals of Machine Learning, its types, and how models make predictions. Learn about model evaluation techniques like cross-validation to measure performance.

Master supervised learning with Regression and Classification models. Explore key evaluation metrics like MSE, F1-score, Precision, Recall, and Confusion Matrix, along with Decision Trees, Ensemble Models, and Regularization techniques to optimize performance.

Discover clustering techniques like K-Means and Hierarchical Clustering, and learn how to evaluate unsupervised models. Get hands-on with hyperparameter tuning methods like Grid Search, Bayesian, and Random Search to enhance model accuracy.

Learn the essentials of model deployment, from integrating machine learning models into applications to maintaining and optimizing them for real-world use. Understand best practices for ensuring long-term model performance.

Learn how to analyze time-dependent data and make accurate future predictions. Explore key techniques like trend analysis, seasonality detection, and forecasting models used in real-world applications.

Dive into the fundamentals of Deep Learning, Neural Networks, and Natural Language Processing (NLP). Understand how AI models process text and images, enabling applications like chatbots and speech recognition

Gain hands-on experience with essential tools used in the data science industry. Learn the fundamentals of Cloud Computing, Power BI & Tableau for data visualization, and SQL & NoSQL Databases for efficient data management. Additionally, explore CI/CD tools like Jenkins or Concourse for automation and GitHub for version control and collaboration.