Data Science and Analytics detailed syllabus for Information Technology (IT) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the IT students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2019 regulation scheme. The detailed syllabus of data science and analytics is as follows.
Course Objective:
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Unit I
Introduction To Data Science and Big Data
Data Science – Fundamentals and Components – Data Scientist – Terminologies Used in Big Data Environments – Types of Digital Data – Classification of Digital Data – Introduction to Big Data – Characteristics of Data – Evolution of Big Data – Big Data Analytics -Classification of Analytics – Top Challenges Facing Big Data – Importance of Big Data Analytics – Data Analytics Tools.
Suggested Activities:
- Case studies on big data application domain.
- Real world domain specific problems involving big data and listing out the challenges.
- Demonstration on data analytics tools.
Suggested Evaluation Methods:
- Student assignment on case studies related to healthcare, climate change, ecommerce, retail business, manufacturing etc.
- Group presentation on big data applications with societal need.
- Quizzes on topics like big data terminologies, big data applications, etc.
Unit II
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Unit III
Predictive Modeling and Machine Learning
Linear Regression – Polynomial Regression – Multivariate Regression – Multi Level Models – Data Warehousing Overview – Bias/Variance Trade Off – K Fold Cross Validation – Data Cleaning and Normalization – Cleaning Web Log Data – Normalizing Numerical Data – Detecting Outliers – Introduction to Supervised And Unsupervised Learning – Reinforcement Learning – Dealing with Real World Data – Machine Learning Algorithms -Clustering -Python Based Application.
Suggested Activities:
- Solve numerical problem solving using linear regression models.
- Demonstrate data cleaning using WEKA tool.
- Demonstration of data preprocessing and machine learning features in Python.
Suggested Evaluation Methods:
- Simple lab based activities for machine learning in Python using small benchmark datasets.
- Tool based assignments on linear, polynomial and multivariate regression using real world case studies.
- Assignment on comparative analysis of two or more data sets using their features.
Unit IV
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Unit V
Data Science Using Python
Introduction to Essential Data Science Packages: Numpy, Scipy, Jupyter, Statsmodels and Pandas Package – Data Munging: Introduction to Data Munging, Data Pipeline and Machine Learning in Python – Data Visualization Using Matplotlib – Interactive Visualization with Advanced Data Learning Representation in Python.
Suggested Activities:
- Demonstration of simple Python scripts using NumPy and SciPy Package.
- Demonstration on NumPy arrays and matrix operations.
- Simple lab activities on dimensionality reduction and feature selection using Python.
- Demonstration of experiments on data visualization using matplotlib functions.
Suggested Evaluation Methods:
- Mini Project using Python for data analytics with benchmark datasets.
- Quiz on data visualization functions.
Course Outcome:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Text Books:
- Frank Pane, “Hands On Data Science and Python Machine Learning”, Packt Publishers, 2017.
- Seema Acharya, Subhashini Chellapan, “Big Data and Analytics”, Wiley, 2015.
References:
- Alberto Boschetti, Luca Massaron, “Python Data Science Essentials”, Packt Publications, 2nd Edition, 2016.
- DT Editorial Services, Big Data, Black Book, Dream Tech Press, 2015.
- Yuxi (Hayden) Liu, “Python Machine Learning”, Packt Publication, 2017.
For detailed syllabus of all other subjects of Information Technology, 2019 regulation curriculum do visit IT 6th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.