Data Science & Machine Learning Engineering
Master data extraction, mathematical modeling, exploratory data analysis, and predictive machine learning using Python. Build portfolio-ready ML models trained on real industry datasets in Kottayam & Online.
Data Science Curriculum Modules
Module 1: Python for Data Science & Mathematics
Advanced Python programming, vectorized computation with NumPy arrays, matrix mathematics, linear algebra fundamentals, and probability distributions for predictive modeling.
Module 2: Data Wrangling & Analysis with Pandas
DataFrames, data cleaning, handling missing values, filtering, groupby aggregations, joining relational datasets, and feature engineering for machine learning pipelines.
Module 3: Exploratory Data Analysis & Visualization
Univariate, bivariate, and multivariate analysis. Data visualization using Matplotlib and Seaborn to communicate statistical patterns, outliers, and trend correlations.
Module 4: Supervised & Unsupervised Machine Learning
Regression (Linear, Ridge), Classification (Logistic, Decision Trees, Random Forests, SVM), Clustering (K-Means), Dimensionality Reduction (PCA), and hyperparameter tuning with Scikit-learn.
Module 5: Model Evaluation & Deployment
Cross-validation, confusion matrix, ROC-AUC, precision-recall metrics, serializing models with Joblib/Pickle, and building an interactive prediction API using FastAPI or Flask.