St Claret College

B.B.A. with Data Science

Data Science is a blend of interdisciplinary fields like statistics, data management and technology which can be applied to almost every domain or industry to extract knowledge from data in various forms. Over the last decade, there has been an enormous explosion in the data, often called as Big Data, generated and retained by various organizations. Data Scientists make sense out of all this Big Data by analysing it and figure out immediately what can be done with it for the progress of the organization. A Data Scientist knows how to drive the value of a large amount of data that already exists inside an organization by defining goals, by empowering management and officers to make better decisions, by understanding and refining target audiences, by identifying opportunities, by recruiting right talent for the organization and many more. Data Scientist is one of the fastest-growing and highest paid jobs which cannot be affected by recession.

Realizing the growing demand of Data Science and Analytics industry in the present world with manifold increase in career opportunities, St. Claret College, Bengaluru offers a three- year programme (200 hours) along with B.B.A., titled as Post Graduate Diploma in Data Science (PGD Data Science), in association with HumaLitix Solutions Pvt. Ltd., Bengaluru. Skilled and experienced experts from HumaLitix Solutions train the students on Data Science and Analytics.

The following is the syllabus of the PGD Data Science programme that ExcelR Edtech Pvt. Ltd. offers at SCC.

YEAR I

Semester	Theme	Core Content	Technology Tools
I	Data Science Foundation	Overview of Statistics Overview of Computer Science Data Science Lifecycle Overview of Data Science Use Cases Overview of Tools and Datasets Hands-On Learning with Excel	Excel DB / SQL
II		Data Collection & Extraction Data Science Constraints Data Mining, Sampling & Patterns Primitive Inferences from Datasets Hands-On Learning with DB / SQL
Note: 70 Hours of Classroom Learning & Assessment (Approx. 35 Hours In-Class, 35 Hours Virtual Classroom) 20+ Hours of Self-Paced Learning, Assignments and Assessments (E-Learning) Mini Project (Individual)
Specific Requirements: Microsoft Excel: Participants are expected to have access to a Microsoft Excel installation on their Personal Computer / Laptop, or a subscription to Microsoft Office 365 Education edition supported by the College. SQL: Hands-on practice on SQL will be enabled with an opensource database such as MySQL Community Edition. Installation instructions will be provided as part of the learning program.

YEAR II

Semester	Theme	Core Content	Technology Tools
III	Exploratory Data Analysis	Overview of Descriptive Statistics Data Visualization & Reporting Univariate Analytics & Multivariate Analytics Descriptive Analytics in Data Science Hands-on Learning with PowerBI Hands-on Learning with Tableau	PowerBI Tableau
IV		Challenges of Dataset Size and Scale Case for Programming Languages in Data Science Introduction to Python (Basics) Data Structures and Algorithms Exploratory Data Analysis using Python	Python
Note: 70 Hours of Classroom Learning & Assessment (Approx. 35 Hours In-Class, 35 Hours Virtual Classroom) 20+ Hours of Self-Paced Learning, Assignments and Assessments (E-Learning) Mini Project (Group)
Specific Requirements: Microsoft PowerBI: Hands-on practice on PowerBI will be enabled using the Desktop Edition (free). Installation instructions will be provided as part of the learning program. Tableau: Hands-on practice on Tableau will be enabled using the Tableau Public Edition (free), which the participants can access online via the Tableau web URL. Python: Hands-on practice on Python will be enabled using the Anaconda open- source distribution. Installation instructions will be provided as part of the learning program.

YEAR III

Semester	Theme	Core Content	Technology Tools
V	Data Modeling and Machine Learning	Overview of Data Modelling & Predictive Analytics Introduction to Machine Learning Fundamental Machine Learning Algorithms: Regression and Classification using Python Time Series Analytics: ETS, ARIMA etc. using Python	Python
VI		Additional Algorithms: Random Forest, Decision Trees, Neural Networks etc. Applications with real world data Overview of Data Science applications: • Scientific Analytics • Marketing Analytics • Financial Analytics • HR Analytics etc. Capstone Project	All the Technology /Tools learnt from the course
Note: 60 Hours of Classroom Learning (Approx. 30 Hours In-Person Classroom and 30 Hours Virtual Classroom) 20+ Hours of Self-Paced Learning, Assignments and Assessments (E-Learning) Capstone Project (Group)
Specific Requirements: Machine Learning with Python: Hands-on practice on Data Modelling and Machine Learning in Python will be enabled using the statsmodels and scikit-learn libraries. Setup instructions will be provided as part of the learning program. In the Capstone project, participants are encouraged to use several or all the technology/tools learnt through-out the course.

Department of
Commerce

Department of Commerce - Tourism

Department of
Humanities

Department of
Computer Science

Department
of Sciences

Department of
Management

PG Department of
Commerce

PG Department of
Social Work

PG Department of Psychology

PG Department of
Management

Department of
Languages

B.B.A. with Data Science

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