Probability And Statistics
Introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
Foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions.
Machine Learning & Artifical Intelligence
Methods and Algorithms for recognizing patterns and making predictions data mining and predictive analysis
Big Data & Hadoop
Big Data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG.
BI & Data Visualization
Business intelligence, Data warehousing, and Data visualization concepts and approaches for analyzing and presenting data views.
Code Course Theory/Practical Credits Hours DS01 Foundations of Data Science Both 0 20 Code Course Theory/Practical Credits Hours DS02 Data Science Overview Theory 2 20 DS03 Foundations of Probability and Statistics Theory 3 30 DS04 Probability and Statistics with R Practical 3 30 DS05 Advanced decision models Theory 2 20 DS06 Decision models using MS Excel Practical 2 20 DS07 Introduction to Database systems Theory 2 20 DS08 Structured Query Language Practical 2 20 DS09 Machine Learning -I Theory 2 20 DS10 Python for Data Analytics Practical 3 30 DS11 Business analytics for decisions Theory 2 20 DS12 Business analytics for decisions Lab Practical 3 30 TOTAL 26 260
|DS13||Machine Learning -II||Theory||2||20|
|DS14||Machine Learning with TensorFlow||Theory||3||30|
|DS15||Data Mining and Predictive Analytics||Theory||2||20|
|DS16||Data analytics with RapidMiner Studio||Practical||3||30|
|DS17||Business Intelligence & Data Visualization||Theory||2||20|
|DS18||Data visualisation with PowerBI & Tableau||Practical||3||30|
|DS19||Big Data engineering||Theory||2||20|
|DS20||Big Data and Hadoop ecosystem||Practical||3||30|
|DS21||Social network and Web Analytics||Theory||2||20|
|DS22||Streaming Analytics with Hadoop & Spark||Practical||3||30|
|DS23||Financial market risks analysis||Theory||2||20|
|DS24||Financial market risks analysis Lab||Practical||3||30|
The course builds foundation concepts and exposure to data science and analytics techniques, before entering the program. This is recommended for students with limited prior knowledge on statistics, databases, computing algorithms and languages.
The course provides overview of data science, associated concepts, tools and applications for decision making under uncertainty.
The course builds foundations on probability, descriptive and inferential statistics, exploratory data analysis, descriptive and predictive analysis required for advanced data analysis.
The course involves hand-on practice for relevant theory (DS03) with “R” packages.
The course explains use of advanced optimization, stochastic and mathematical models to meet multiple objectives under uncertainty.
The course illustrates use of advanced analytics tools in decision making under uncertainty, using business case studies.
The course covers Business intelligence, Data warehousing, and Data visualisation concepts and approaches for analysing and presenting data views.
The course builds theoretical foundations on big data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG.
The course covers practical approaches to big data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG.
The course builds understanding and approaches to web intelligence and social network analytics.
The course involves hand-on practice for relevant theory (DS05) with MS-Excel add-ins.
The course provides hands-on experience on data visualisation with PowerBI and Tableau.
The course explains basics of data models, database systems and query language.
The course involves hand-on practice for relevant theory (DS07) with SQL (MySQL or Microsoft SQL Server).
The course covers support vector machines, neural networks, graphical models and pattern recognition that can learn from data.
The course covers unsupervised and evolutionary learning algorithms, and advanced neural networks that enable advanced machine learning from data.
The course involves hand-on practice for relevant theory (DS09) with Python language and tool/ packages.
The course explains concepts, methods and algorithms for data mining and predictive analytics.
The course focuses on data mining and predictive analytics with RapidMiner Studio.
The course illustrates use of volatility risk analysis models in financial markets through case studies and MS Excel add-ins.
The course covers practical approaches to streaming analytics with Hadoop ecosystem components such as HDFS, Map-Reduce, Spark, Kafka, Flume and NoSQL.
The course involves students in a project team where they solve practical problems in an institutional environment such as industry, consulting or R&D unit. They may use relevant tools and languages learnt during the academic program or available at the institution.
The course involves hand-on practice for relevant theory (DS13) on machine learning, with Google TensorFlow framework.
The course involves practice exercises on business case studies for relevant theory (DS23), using various tools and languages learnt in other practical courses.