Nowadays, most
of the decisions are taken in various organizations/sectors by analyzing
stakeholder’s data. This is true for the education sector also. Therefore,
minimal knowledge of data analysis is mandatory at all levels in the education
sector, to take proactive decisions in improving the system. Education and
training are progressively taking place in digital environments. As a result,
these environments are generating both structured and unstructured amount of
interaction and behavioral data that can be used to design better learning and
teaching models for teaching, learning and assessment. The main objective of
this course is to use different kinds of methods from data analytics to identify
unique patterns from educational data. In particular, the learners will learn
about methods and models that are being used in data analytics, students'
behavior modeling, and personalized learning material recommendations. The
module will be covered both at the theoretical level as well as the practical
level where software tools will be used to analyze the data.
| Course Status : | Completed |
| Course Type : | Core |
| Language for course content : | English |
| Duration : | 8 weeks |
| Category : |
|
| Credit Points : | 2 |
| Level : | Continuing Education |
| Start Date : | 15 Jul 2024 |
| End Date : | 31 Oct 2024 |
| Enrollment Ends : | 31 Aug 2024 |
| Exam Date : | 07 Dec 2024 IST |
| Exam Shift : | Shift-2 |
Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.
|
swayam@nitttrc.edu.in, swayam@nitttrc.ac.in
Week
01: Data Analytics – An Overview (2.5 Hours)
Definition
of Data Analytics and its relevance; Types of Data – Structure vs Unstructured
and Quantitative vs Qualitative; Data Analytics workflow – Collection, Data
Cleansing & Transformation, Data Modelling, Data Visualization; Types of
Data Analytics; Data Security; Case studies.
Week
02: Clustering and Classification Techniques (2.5 Hours)
Introduction
to Data Science & Methodology, Various Methods of Data Science (Clustering
and Classification), Descriptive and Predictive Analytics. A Case Study of use
of clustering and classification methods on educational data.
Week
03: Machine Learning for Data Science (2.5 Hours)
Introduction
to Machine Learning, Neural Network and Deep Learning; A black box approach to
Regression Analysis; Popular Data Analytic Tools. Case studies on educational
data.
Week
04: Social Network Analysis (2.5 Hours)
Social
Network Analysis in Education, A Simple Case Study of analysing
Twitter/Facebook data.
Week
05: Educational Data Analytics (2.5 Hours)
Learning
Associations – Classification – Regression – role of educational data analytics
- Behaviour Detection - Data Synchronization - Feature Engineering - Feature
Generation and Feature Selection for behaviour detection.
Week
06: Performance Factors Analysis (2.5 Hours)
Latent
Knowledge Estimation - Bayesian Knowledge Tracing - Performance Factors
Analysis - Relationship Mining - Correlation Mining -Students' Interaction
Network Analysis.
Week
07: Data Visualization (2.5 Hours)
Visualization
- Educational Visualization and Learning Curves- Heat Maps, Parameter Space
Maps, State-space Network - Structure Discovery.
Week
08: Learning from Multiple Representations (2.5 Hours)
Applications
of Clustering in EDA, Factor Analysis, Knowledge Inference (Qmatrix and
Learning Factor Analysis) - Personalized Recommendation - Topic-based Content
Recommendation - Course Recommendation. Case studies on data analytics
practices by Google, Amazon, Healthcare, Government etc.
1.
H Almuallim, S Kaneda,
Y Akiba, Development and Applications of Decision Trees, Editor(s): Cornelius
T. Leondes, Expert Systems, Academic Press, 2002, Pages 53-77.
2.
Christopher Bishop,
Pattern Recognition and Machine Learning, Springer Pub. (2010).
3.
A Webb and KD Copsey,
Statistical Pattern Recognition, 3rd Edition, Willey Pub. (2011).
4.
Introduction to
Statistics and Data Analysis by C Heumann and MS Shalabh, Springer Pub., 2016.
5.
Goodfellow, Y. Bengio
and A. Courville, “Deep Learning,” MIT Press, 2016.
Prof.
Chandan Chakraborty is currently a professor in the Dept.
of Computer Science & Engineering at National Institute of Technical
Teachers' Training & Research Kolkata, India. His academic background
includes Graduation (Statistics Hons.) from Narendrapur Ramakrishna Mission
Residential College under University of Calcutta, Masters (Applied Statistics
& Informatics) from IIT Bombay and PhD from IIT Kharagpur. He is actively
involved in conducting short term training / faculty development / national
mentorship etc. programs majorly in emerging areas like AI/ML/DL, Data Science,
Applied Statistics etc. aligned with NEP-2020 towards quality improvement in
technical education. His research activities include Statistics, Machine
Learning, Deep Learning and Generative AI algorithms, Biomedical imaging
informatics for solving real-life problems. He has numerous peer-reviewed publications
in the IEEE, Nature, Elsevier, Springer, Wiley pubs. etc. His credentials
include more than 100 journal papers, 02 US and 02 Indian patents along with
many national/international conferences, book chapters etc. He received
prestigious Young Scientist Award from His Excellency Dr APJ Abdul Kalam,
President of India by Indian Science Congress. He was awarded by Young
Researcher Award from Dept. of Atomic Energy (DAE) and Fast Track Young
Scientist by DST, Govt. of India. Prof. Chakraborty was also selected for
biomedical fellowship by ICMR, Govt. of India for his contribution in
biomedical engineering.
Prof. Samir Roy
National
Institute of Technical Teachers Training and Research, Kolkata
Prof.
Samir Roy is presently attached as Professor
to the Dept. of Computer Science & Engineering of National Institute of
Technical Teachers’ Training and Research (NITTTR), Kolkata. After graduating
with honours in Physics from the Presidency College under the University of
Calcutta, he obtained his B. Tech, M. E and Ph. D, all in the field of Computer
Science and Engineering. He has taught and trained various topics of Computer
Science at undergraduate, postgraduate and teachers’ training level at various
institutes for the past 25 years. He has around fifty articles in different
international and national journals and conference proceedings. He has authored
a textbook on Soft Computing which is published by Pearson. His areas of
interest include Educational Informatics, Artificial Intelligence, Soft
Computing, Theory of Computation etc.
"The SWAYAM Course Enrolment and
learning is free. However, to obtain a certificate, the learner must register
and take the proctored exam in person at one of the designated exam centres.
The registration URL will be announced by NTA once the registration form
becomes available. To receive the certification, you need to complete the
online registration form and pay the examination fee. Additional details,
including any updates, will be provided upon the publication of the exam
registration form. For more information about the exam locations and the terms
associated with completing the form, please refer to the form itself."
Grading Policy:
- Internal Assignment Score: This
accounts for 30% of the final grade and is calculated based on the average of
the best three assignments out of all the assignments given in the course.
- Final Proctored Exam Score: This makes
up 70% of the final grade and is derived from the proctored exam score out of
100.
- Final Score: The final score is the
sum of the average assignment score and the exam score.
Eligibility for Certification:
- To qualify for a certificate, you must
achieve an average assignment score of at least 10 out of 30, and an exam score
of at least 30 out of 70. If one of the 2 criteria is not met, you will not get
the certificate even if the Final score >=40/100.
Certificate Details:
- The certificate will include your
name, photograph, roll number, and the percentage score from the final exam. It
will also feature the logos of the Ministry of Education, SWAYAM, and NITTTR.
- Certificate Format: Only electronic
certificates (e-certificates) will be issued; hard copies will not be
dispatched.
Once again, thanks for your interest in
our online courses and certification. Happy Learning.
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