राष्ट्रीय इलेक्ट्रॉनिकी एवं सूचना प्रौद्योगिकी संस्थान ,कालीकट

National Institute of Electronics & Information Technology,Calicut

Ministry of Electronics & Information Technology
Government of India
Course Calendar

SW900 : PG Diploma in Data Analytics and Artificial Intelligence

Course : PG Diploma in Data Analytics and Artificial Intelligence
Code : SW900
Starting Date : 28 November,2022
Last Date To Apply : 21 November,22
Course Coordinator :

PrasoonKumar KG, Ph: 04952287266, Mob:9447305951 

Course Preamble

In today’s world there exists data available in abundance from variety of sources like web server logs, social media, and large databases and from diverse domains like Ecommerce, Medical, Scientific etc. Big data analytics is the process of examining these data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. Business people, Doctors, Scientists et al. can use this to improve their services.

The main challenge to the analysis of big data comes because of the 4 V’s– volume, velocity, variety and veracity. For effective analytics, we need to deal with high volume of data of different variety which is being generated in high velocity. The data what is available from such sources is highly unstructured which calls for analytics on the same.

This course is designed to make the participants capable of solving problems using Artificial Intelligence related technologies.

Course Objective

The objective is to make the participants capable of identifying and applying appropriate techniques and tools to solve problems in managing huge quantity of data.

Course Outcome

After completing the course, the participants will be capable of formulating AI problems that can be solved with the raw data available in different domains. They will be able to do basic data analysis and machine learning model development with structured data. They will also be able to do suitable predictions and decision making by handling unstructured data including text, images and video using deep learning and natural language processing.

Course Structure

Course Contents

Linux OS (2 weeks)

Linux environment, basic commands, shell concepts, Shell scripting, built-in tools for data analysis.

Python Programming (4 weeks)

Python -features, program execution, data structures, List, Dictionary, Tuples, If statements, looping and loop control statements, Functions and Modules, Generators, import statement, namespaces-packages, Class concepts, Exception handling, Regular Expressions, Database access, XML parsing, date time and time zones.

Statistical / Mathematical Foundation for Data Science (2 Weeks)

Basic probability concepts, Conditional probability, Bayes Theorem, Probability distributions, Continuous and discrete distributions, Normal distribution, Poisson distribution, Binomial distribution, Correlation and Covariance,  Hypothesis Testing.

Differential Calculus - Slope of a straight line/Curve, Derivatives and optimization, Partial derivatives, Gradient Descent.

Linear Algebra -Vectors, Norm of a vector, Dot product, Matrices, Matrix multiplication, transpose, Geometric applications of matrix operations.

Big Data, Data Analytics (4 Weeks)

Hadoop Architecture and HDFS, Configuring Hadoop, Mapreduce Architecture with examples, YARN Architecture, nosql databases,  Hadoop subprojects, Familiarization of Spark.

Analysis using spread sheets - Formulas and Functions, Charting, Pivot table, What if analysis.

Analysis using python – Exploring numpy module –arrays and array operations, indexing and slicing, mathematical and statistical functions. Pandas – Series and Data frames, Data loading and storage, Data cleaning and preparation, Data Wrangling, Data Aggregation, Time Series. Plotting using matplotlib, seaborn.

Machine Learning (5 weeks)

Supervised and Unsupervised Learning, Classification, Regression & Clustering, Model Evaluation Metrics, Machine Learning Algorithms-Linear Regression, KNN, K Means, Logistic Regression, Support Vector Machines, Decision Tree, Naïve Bayes, etc. Ensemble Learning and Random Forests, Bagging, Boosting, Dimensionality Reduction. 

Deep Learning (2 weeks)

Artificial Neural Networks, Implementing MLPs with Keras, Tensorflow, Deep Neural Networks, Optimizers, Image Processing using OpenCV, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and GANs.

Natural Language Processing and Reinforcement Learning (1 Week)

Natural Language Processing Methods, Basics of text processing, Lexical processing, Syntax and Semantics, Parts of Speech Tagging, Applications like Sentiment Analysis, Text Classification, Text Summarization, Document Clustering, Document Similarity, Web Crawling etc. Reinforcement Learning and its applications in AI.

Project Work (4 Weeks).

The participants have to do industry relevant project using real data.

Course Fees

General Candidates: Course fee is Rs.53,730 + all taxes as applicable

SC/ST Candidates : Limited seats are reserved for SC/ST candidates as per Govt. of India norms on merit basis, and tuition fee is waived for these candidates.

However they are required to remit an amount of Rs. 5000/- as Advance caution/security deposit. This amount will be refunded after successful completion of the course. If the student fails to complete the course successfully, this amount along with any other caution/security deposits by the student will be forfeited.

Modular wise Course Fee: Not Applicable for this course

  1. Registration Fee: An amount of Rs.1000/- (including all taxes as applicable) should be paid at the time of registering for the course. The amount is nonrefundable.

This fee shall be considered as part of course fee, if the student joins the course. If a student register and pay for more than one course and join for any one course, all such amount will be adjusted against the course fee payable.

If the student does not join for the registered course / any of the registered courses, fee paid shall be forfeited.

For SC/ST candidates, the registration fee is Rs.500/- and will be considered as part of caution/security deposit and will be refunded after successful completion of the course. If the candidate does not join or fails to complete the course the amount will be forfeited

However above the registration fee shall be refunded on few special cases as given below 

  • Course postponed and new date is not convenient for the student 
  • Course cancelled in advance, well before the admission date

Eligibility

BE/ BTech, BSc (IT/ Computer Science/ Electronics/ Physics/ Chemistry/ Mathematics/ Statistics), BCA, 3 year Diploma, Graduation in any stream with [PGDCA / NIELIT A or B level ], OR equivalent to any of these with good computer programming knowledge.

Important Dates

Last date for submitting application  : 21-Nov-2022

Selection intimation through website: 22-Nov-2022 (After 5.00 PM)

Counseling/Admission                     : 28-Nov-2022

More Details