Data Science

Programme Specific Outcomes

• Build a strong foundation of statistics for data science.
• Use all the features and new updates of Python and R for data science.
• Perform scientific and technical computing using the Python SciPy package and its sub packages Integrate, Optimize, Statistics, IO and Weave.
• Gain expertise in mathematical computing using the NumPy and Scikit-Learn package.
• Gain an in-depth understanding of data structure and data manipulation.
• Understand and use linear and non-linear regression models and classification techniques for data analysis.
• Obtain a comprehensive knowledge of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline.
• Master the concepts recommendation engine, time series modelling, gain practical mastery over principles, algorithms and applications of Machine Learning.
• Learn to analyse data using Tableau and Power BI and become proficient in building interactive dash boards.
• Understand deep reinforcement learning techniques applied in Natural Language Processing.
• Understand the different components of the Hadoop ecosystem and learn to work with HBase, its architecture and data storage, learning the difference between HBase and RDBMS, and use Hive and Impala for partitioning.
• Understand MapReduce and its characteristics and learn how to ingest data using Sqoopand Flume.