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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
English

Course Overview

The Data Science for All online course is developed under NIT Patna's joint online faculty development programme. Expert professors in the country are responsible for planning and organising the curriculum. The programme's curriculum is rather advanced so that applicants can learn better and develop themselves. In order to make them stronger, the Electronics & Information and Communication Technology Academy (E&ICT) of NIT Patna are conducting the Data Science for All class programme for faculty, PhD researchers and others.

7 academies of Electronics & Information and Communication Technology (E&ICT) were founded by the Ministry of Electronics and Information Technology of the Government of India. Data Science for All certification course of NIT Patna intends to construct and integrate basic and advanced training programmes in the speciality areas of electronics and ICT in order to enhance the skills, knowledge base and necessary resources to augment the teaching society with greater knowledge.

There are 18 NITs established by MHRD, the Government of India. The institute has always offered its students with high academic excellence. Participation in NIT Patna's Data Science for All programme will prove useful to all candidates.

The Highlights

  • Expert facilities
  • Online programme
  • Offered by E&ICT, NIT Patna
  • Certificate by NIT Patna 
  • 12 days programme

Programme Offerings

  • online programme

Courses and Certificate Fees

Certificate Availability
no
  • Fee for the Data Science for All programme varies by category. For faculty/ students, it will be Rs. 500 and for industry people/ others it will be Rs. 1,000.

Fee details of the Data Science for All programme

Particulars

Amount in INR

Fee for faculties/Ph.D. scholars

Rs. 500 (GEN/ OBC)

Rs. 250 (SC/ ST)

Fee for industry people/ others

Rs. 1,000 (GEN/ OBC)

Rs. 500 (SC/ ST)


Eligibility Criteria

Certification Qualifying Details

Enrolling by paying the Data Science for All programme fee and completing it will be making a candidate qualified for getting certified.

What you will learn

Data science knowledgeKnowledge of deep learningR ProgrammingMachine learning

The candidates of Data Science for All programme will learn these skills and knowledge after completing it;

  • Mathematical aspects like sciences, vectors, matrices, matrix decomposition, vector spaces, statistical measures, singular value decomposition, density function, probability basics, conditional probability, variance, and Markov chains.
  • Dimensionality reduction and principal component analysis.
  • Regression, decision tree, n Trees, Naïve bayesian classifier and clustering. 
  • Handling large datasets: MapReduce.
  • Data wrangling, data visualisation and programming.
  • Normal Python, NumPy, Pandas and Matplotip.
  • Deep learning skills.
  • ML topics practicing skills.

Who it is for

The course is helpful for:

  • Candidates who wish to gain all the knowledge about Data Science
  • People who have already been working in the field and wish to gain more knowledge and expertise on the same

Application Details

Do follow these steps to apply for the Data Science for All programme.

Step 1: Click the below-mentioned link and visit the official website of NIT Patna.

http://www.nitp.ac.in/ict/datascience.php

Step 2: There will be an “Apply Online” button. Click the button to enroll.

(The registration has been closed.)

The Syllabus

  • Vectors
  • Matrices
  • Matrix decomposition
  • Vector spaces
  • Statistical measures
  • Singular value decomposition
  • Density function
  • Probability basics
  • Conditional probability
  • Variance
  • Markov Chains

  • Principal component analysis
  • Dimensionality reduction

  • Regression
  • Naïve Bayesian classifier
  • Classification decision trees
  • Handling Large datasets: MapReduce
  • Clustering

  • Data wrangling
  • Programming
  • Data visualization

  • Normal python
  • Pandas
  • NumPy
  • Matplotlib

  • Practice on ML topics

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