Course Overview:
This data science program follows the CRISP-DM Methodology. The premier
modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence,
and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis
Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso and Ridge
Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal
with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine
Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final
modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural
Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is
the most enriching training program in terms of the array of topics covered
- Module 1: Python Introduction
- Module 2: SQL
- Module 3: Data Science - Preliminaries
- Module 4: Data Mining - Unsupervised Learning
- Module 5: Data Mining - Supervised Learning
- Module 6: Forecasting/Time Series
- Module 7: Black Box method (ANN, CNN, RNN)
- Module 8: Real-Time Data Science Projects
- Module 9: Capstone Project
