Full Time Post Graduate Program - SACE

Post-Graduate Program in Business Analytics & Data Science

SSN SACE’s full-time Post Graduate Program in Business Analytics and Data Science is among the most preferred advanced career programs by graduates and professionals. It is delivered in collaboration with leading industry partners. The program duration includes 5 months of an in-depth in-class learning experience and a 1-month corporate internship.

Business Analytics is among the most sought after skills in the industry. Retail, healthcare, insurance, education, finance, supply chain & logistics, banking – every growing industry is using business analytics and data science for better decision making. This program has been designed in collaboration with industry leaders to provide that competitive edge and industry relevance to graduate students and working professionals aspiring for change in IT career.

The business analytics and data science program combines both theoretical and practical knowledge on industry-relevant tools and phases of analytics including descriptive analytics, predictive analytics and prescriptive analytics. The techniques such as business statistics, data visualization, machine learning, deep learning, artificial intelligence, optimization techniques and big data technology are covered in these three phases in two sections. The program imparts practical knowledge through implementation of concepts with Python, SQL, Excel and other industry relevant tools.

Upon completion of the program, SSN SACE students are qualified for various roles including business analyst, data analyst, data scientist, data engineer, machine learning engineer, statistician and data architect with a high degree of employability. Our placement cells provide them 100% placement assistance with fortune 500 companies in India.

Program Structure

Post-Graduate Program in Business Analytics & Data Science

Business Analytics

Foundation Courses
Business Statistics
Data Visualization and Business Intelligence
Analytics in Business Management
Machine Learning - I

Data Science

Machine Learning - II
Artificial Intelligence
Deep Learning
Optimization Techniques
Active Learning Project

Business Analytics Lab

SACE has a business analytics lab with the following support tools

excel

MS Excel

sql

MySQL

tablue

Tableau

power

Power BI

python

Python

pytorch

PyTorch

tensor

TensorFlow

mat lab

MATLAB

Pedagogy

Technology Enabled Learning

Students are given the course material in advance. They come prepared to the class by going through the learning material. The classroom sessions are learner driven and interactive. The students and the faculty have ongoing interactions through Learning Management Systems (LMS).

Real-Life Case Studies

Case studies are included to help the students to analyze business issues from variety of perspective, and apply critical thinking and problem solving skills

Industry Speakers

An integral part of the PGP-BA&DS is interactions with industry speakers and their regular visits. These sessions are part of the module, and are designed to enhance the understanding of the concepts of the module from a practitioner’s perspective, making it easier to apply the learning immediately.

Project Based Learning

Project based learning involves working in teams, identifying the requirements and design solutions to meet requirements. The active learning project helps students to appropriately apply techniques and skills in real-time world because the projects are proposed by external clients. The principles of the active learning projects are professionalism, right judgement, and reflective practices.

Benefits of Business Analytics

Organizations are increasingly making use of Business Analytics in their operations.

  1. To identify patterns in data to analyze customer behavior and tailor products to them – Telecom companies develop custom plans based on individual customer usage.
  2. To analyze data generated from business transactions and use it to offer products to customers – Retailers identify customers’ buying patterns and offer discounts and bundling based on their preferences.
  3. To detect fraud – Financial firms process data to identify inconsistencies and detect irregular transactions.
  4. To predict future based on data analysis – To identify new segments of customers.
  5. To gain competitive advantage and prepare strategy

For corporate enquiries, please feel free to contact us: +91-94459 72530 | sace.admission@ssn.edu.in