Course Description: This course provides students with a foundational understanding of data science concepts, techniques, and applications. Students will learn how to acquire, clean, explore, and analyze data, as well as communicate insights effectively through data visualization and storytelling. The course also introduces basic statistical methods and prepares students for more advanced topics in later courses.
Duration : One Year (Two Semesters)
Eligibility : Graduates of any discipline
Paperwise Distribution :
SEMESTER I :
Paper 1 : Introduction to Data Science
o Overview of data science concepts and applications
o Data acquisition, cleaning, and exploration
o Basics of data visualization
o Introduction to statistical analysis for data science
Paper 2 : Programming for Data Science
o Fundamentals of programming using Python/R
o Data structures and algorithms
o Data manipulation and analysis using libraries like Pandas and NumPy
o Hands-on coding exercises and projects
Paper 3 : Machine Learning Fundamentals
o Introduction to machine learning concepts
o Supervised, unsupervised, and reinforcement learning
o Model evaluation and validation techniques
o Implementing machine learning algorithms
Paper 4 : Statistical Methods for Data Science
o Probability distributions and statistical inference
o Hypothesis testing and confidence intervals
o Regression analysis
o Experimental design and A/B testing
Paper 5 : Data Visualization and Communication
o Advanced data visualization techniques
o Storytelling with data
o Interactive visualizations using libraries like Matplotlib, Seaborn, and
Tableau
o Effective communication of data insights
SEMESTER II :
Paper 6 : Advanced Machine Learning
o Deep learning fundamentals
o Neural networks and architectures
o Convolutional and recurrent neural networks
o Transfer learning and model fine-tuning
Paper 7 : Big Data and Cloud Computing
o Introduction to big data technologies (Hadoop, Spark)
o Data storage and processing in distributed environments
o Cloud computing platforms (AWS, Azure, GCP)
o Scalable data analysis and machine learning on the cloud
Paper 8 : Natural Language Processing
o Basics of text processing and analysis
o Tokenization, stemming, and lemmatization
o Sentiment analysis, text classification, and named entity recognition
o Building NLP applications and chatbots
Paper 9 : Time Series Analysis
o Time series data and its characteristics
o Forecasting techniques (ARIMA, Exponential Smoothing)
o Seasonal decomposition and trend analysis
o Applications in finance, economics, and more
Paper 10 : Capstone Project in Data Science
o Real-world data science project from problem formulation to deployment
o Integration of skills learned throughout the program
o Collaboration with industry partners (if possible)
o Presentation and documentation of the project
STUDY MATERIALS AND TEACHING METHODOLOGY
Study Materials and Teaching Methodology
The entire study material will be made available online and the
teaching/training will be done through online zoom based instructions.
You can pay the fee online. Fee can be transferred electronically in the Institute's Bank Account through ONLINE or RTGS etc. also. Click here to Pay Online
FEE
The details of the fee to be paid to The Global Open University Nagaland are
the following :
The Admission Registration Fee : Rs. 3,500
Tuition Fee @ Rs. 2000 per month (for 12 months) : Rs. 24,000
Examination and Evaluation Fee @ Rs. 300 per paper (for 10 papers) :
Rs. 3,000
Total Fee : Rs. 30,500 (Rupees Thirty
Thousand Five Hundred only)
The payment to The Global Open University Nagaland may be made as per the
following details :
TOTAL FEE STRUCTURE FOR 1 YEAR IS INDIAN RUPEES (INR.) 30,500 ONLY (US$ 400 only)
This amount is to be transferred to the Institute's Bank Account having
the following details :
Name of the Account: Indian Institute of Data Science
Bank Name: AU Small Finance Bank
Account Number: 2301259852472977
IFS Code: AUBL0002598
Branch: Saket, New Delhi