Program Details

Data Science is currently one of the hottest and trending career options for the decade. The demand for data scientists is growing into becoming huge where the number as quoted by industry is much higher than the available candidates. Hence, it can be concluded that, choosing Data Science as a career option has a lot of scopes and will remain so in the near future and there is the possible scope of the gap in demand and supply for job roles pertaining to data scientists, which includes professionals for information management, software development, data analytics, artificial intelligence, data discovery, and its various other related fields. As per the industrial demand, Python, R, Machine Learning, etc. have now become the most sought after skills.

PG Diploma program in Data Science and AI for executives aim towards making working professionals/ technology graduates, belonging to various diverse sectors, equipped skill sets pertaining to the data science field. The program includes in-depth concepts of Machine Learning, Neural Networks, and Business Intelligence along with introductory concepts of AI.

Course Content


  1. Introduction to Data Science and ApplicationsThis course gives an overview of the basic concepts of data, big data, data processing, data manipulations, and tools that data analysts work with. The course cover introduction of various tools that are used by data scientists to find insignificant insights or patterns in the data.
  2. Basic StatisticsThe course covers the fundamentals of Basic statistics which helps the learner to understand how to measure the data in a scientific manner and how to compute and apply measures of central tendency, measures of dispersion, measures of skewness & kurtosis tools while analyzing the data. This course also covers a brief introduction of probability and probability distributions which is useful while using data science tools.
  3. Advanced StatisticsThis course covers how to measure the relationship between the variables using correlation and regression techniques. It also covers how to use different sampling methods and test the claim and using hypothesis testing. The course covers the fundamentals of statistical inference which helps the learner to understand the process of drawing conclusions about populations or scientific truths from data using different modes of performing inference.
  4. R ProgrammingThis course provides an in-depth understanding of R, R-studio, and R packages. Learners will learn how to program in R with the various types of functions, data structure, and perform data visualizations, data manipulation, pre-processing and summary statistics using R for any specific need.
  5. Python ProgrammingThis course helps the learner to understand the essential concepts of Python programming like data types, basic operators, and functions. Learners will perform high-level computing using NumPy, SciPy packages along with the Pandas package used for data analysis and manipulation. Learners will gain expertise in machine learning using the Scikit package, matplotlib library for data visualization, and web scraping using BeautifulSoup.
  6. Machine Learning with R and PythonThe course covers Data Processing, Exploratory data analysis, Supervised Learning (Regression, classification), Unsupervised learning (Clustering, Dimensionality Reduction).
  7. Business Intelligence ToolsThis course helps the learner to visualize the data and present the information in the forms of reports and dashboards by performing business analysis using tableau or Power BI.
  8. Natural Language ProcessingStudents can learn, how to use the NLTK module in Python for developing various skill sets pertaining to Natural Language Processing, how to build a Spam Detector, how to build a Twitter, Sentiment Analyser, Latent Semantic Analysis.
  9. Neural NetworksThis course covers Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann Machines, Tensor flow with Python/R.
  10. Capstone ProjectProject in AI or automation in one of our focus sectors including Retail, Banking, Manufacturing, Education, SCM, and others.

Highlights


  • Experienced Trainers having worked in multinational enterprises and technology projects to provide industrial exposure.
  • 70% learning through hands-on, practical, case studies, and projects.
  • Content curated with the help of sector skill councils and industry needs.
  • Flexible batch timings to suit working professionals.
  • Comprehensive 12-month program giving opportunity to ride the data science career.
  • Optional Module on Basics of Math for Machine Learning – before the start of the program.
  • We provide 2 months Industry Internships

PROGRAM STRUCTURE

SEMESTER I SEMESTER II
Introduction to Data Science and Applications Machine Learning with R and Python
Basic Statistics Business Intelligence Tools
Advanced Statistics Natural Language Processing
R Programming Neural Networks
Python Programming Capstone Project
Project/Case Study

Program Structure:-Download pdf file