Share this link via
Or copy link
About The Course
Data Science involves use of tools/technologies for managing large amounts of data, statistica techniques to analyze and interpret this data and understanding of domains such as finance,marketing, and pharma which provides the context for such analysis.
- Over 250 hrs of live faculty lectures, 24×7 access to video content
- Combines theory, programming skills and business application
- Comprehensive curriculum covering Analytics, AI and ML, Big Data and more
- Learn from practicing Data Scientists, Data Engineers and CTO’s
- Personal placement mentor + access to over 100 curated Data Science jobs
Syllabus
| Topics | |
| Term 1 : Business Analytics | › Statistics and statistical methods › Python – conditional loops, functions, file input and output › Exploratory analysis – Univariate, Bivariate, Outliers › Linear regression – Output interpretation, validation › Logical regression – model valuation, confusion matrix › Time Series – Forecasting and ARIMA › Classification – Decision trees, Random Forest, Assembling › BI and Tableau – Workflows, Visualization, Dashboards › SQL – Joins, functions, operators › 20 cases studies / projects |
| Term 2 : AI and Advanced ML | › Regularization Boosting – Hyper parameter Tuning › Support Vector Machine – Linear and Non-Linear Classifier › Principle Component Analysis (PCA) – Factor Analysis › Neural Networks –Tensor Flow, Kera’s, Deep Learning, Neural Networks › Recommendation Systems / NLP – Data Processing, Sentiment Analysis ›10 cases studies / projects |
| Term 3: Data Tools and Projects | › Big Data – Hadoop, Hive, Spark, Scala › Microsoft Azure – ML Classic Studio › Data Visualization – Power BI › 4 case studies / project › Final Project – Deployment level, Internship level |