Course Outcomes:
(The cognitive levels are given in bracket)

On completion of the course, students will be able to:
CO1: Carry out time, frequency, and Z -transform analysis on signals and systems (Understand).
CO2: Explain the significance of various filter structures (Understand).
CO3: Explain DFT and its fast computation (Understand).
CO4: Design a digital filter for a given specification (Apply).
CO5: Explain the basic concepts in power spectrum estimation (Understand).
CO6: Explain the basic concepts of multirate signal processing (Understand).
CO7: Explain the features of DSP processors compared to general-purpose microcontrollers (Understand).

Course Outcomes:
(The cognitive levels are given in bracket)
On completion of the course, students will be able to:
CO1: Obtain time domain response of first and second order systems (Understand).
CO2: Analyse first and second order systems using time domain and frequency domain
methods (Understand).
CO3: Apply basic control system tools for stability analysis (Apply).
CO4: Design basic compensators (Apply).

Its a Graduate level  course gives an Introduction to R and Python.  

The course provides foundations in two of the most popular programming languages used for data analytics – viz. R and Python. After having completed the course, the students should be knowledgeable in the principles of programming in R for the purpose of data management, visualisation of data and basic statistical calculations. Augmented to this, the course enables the student to acquire skills in Python programming for processing text based data and managing the results.

Modules 1 and 2 give a general introduction to Python programming, and students will learn and practice to write small Python programs to tackle problems in data analytics.

In the last 3 modules, students learn and practice R programming needed for data analysis, in particular for large dataset. The student will learn how to take a large dataset, break it up into manageable pieces and use a range of qualitative and quantitative methods to bring out the insight embedded into the data. They also learn tools that help to communicate the findings using R visualization packages.

Applications of the Analytics are span across various fields, some of them are listed below

  1. Marketing
  2. Retail
  3. Finance
  4. Sales
  5. Operations
  6. Manufacturing
  7. Consumer Goods
  8. Crime Analysis
  9. Travel and Hospitality
  10. Education
  11. Healthcare
  12. Telecom
  13. Agriculture
  14. HR
  15. Energy  ......................