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"Business Analytics is a rapidly growing field that has a high demand for skilled professionals. With the increasing importance of data-driven decision-making in business, the need for experts in Business Analytics is becoming more critical. Graduates of the Post Graduate Program (PGP) in Business Analytics by Aegis School of Data Science are well-equipped to meet this demand and launch their careers in this exciting field. "


The job opportunities in Business Analytics are vast and diverse, ranging from data analysts, business analysts, HR analysts, marketing analysts, operations analysts, financial analysts, data analysts, data scientists, and more. These roles can be found in a variety of industries such as finance, healthcare, retail, e-commerce, marketing, and more


With a PGP in Business Analytics, graduates can expect to be highly sought after by companies looking to leverage the power of data to improve business operations, gain insights into customer behavior, and stay ahead of the competition. Graduates will be equipped with the skills to collect, analyze, and interpret data, create predictive models, and develop data-driven strategies

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Program Start Date

  • Mumbai: August 2023
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Our Course Curriculum

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What is Business analytics?

Busienss Analytics is the practice of using data, statistical and quantitative analysis, and computational methods to drive business decision-making, strategy, and performance. It involves collecting, processing, and analyzing data to extract insights, trends, and patterns that can help organizations make informed decisions and optimize business processes. Business analytics can be applied in various areas such as sales and marketing, operations, finance, human resources, and supply chain management. The goal is to identify opportunities for growth and improvement, reduce costs and risks, and gain a competitive edge in the market.

Applications of Business Analytics

Business Analytics has a wide range of applications across industries and functions. Here are a few examples:

  1. Marketing: In marketing, Business Analytics can be used to analyze customer behavior, identify market trends, and optimize marketing campaigns.
  2. Finance: In finance, Business Analytics can be used for risk management, fraud detection, financial forecasting, and portfolio optimization.
  3. Operations: In operations, Business Analytics can be used to optimize supply chain management, improve inventory management, and reduce operational costs.
  4. Human Resources: In HR, Business Analytics can be used for talent management, employee engagement, and workforce planning.
  5. Healthcare: In healthcare, Business Analytics can be used to improve patient outcomes, optimize resource allocation, and reduce healthcare costs.
  6. Retail: Business Analytics can help retailers optimize their supply chain management, improve inventory management, and enhance customer engagement through personalized marketing.
  7. Manufacturing: Business Analytics is used in manufacturing to optimize production processes, reduce downtime, and enhance product quality.
  8. Telecommunications: Business Analytics can help telecommunication companies analyze customer data to improve customer retention, identify new revenue streams, and optimize network capacity.
  9. Sports: Business Analytics is increasingly used in the sports industry to analyze player performance, develop game strategies, and optimize team management.
  10. Transportation: Business Analytics can help transportation companies optimize routes, reduce fuel consumption, and enhance customer experience through real-time tracking and personalized services.

These are just a few examples of the many applications of Business Analytics in various industries and functions.

What kind of jobs do students get after completing the PGP in Business Analytics?

Students who have studied PGP in Business Analytics can pursue a variety of job roles in different industries. Some of the job roles that are relevant to this program include:

  1. Business Analyst: This job role involves analyzing business operations and processes to identify areas for improvement, and recommending solutions to enhance efficiency and effectiveness.
  2. Data Analyst: This job role involves collecting, analyzing, and interpreting large data sets to identify patterns, trends, and insights that can inform business decisions.
  3. Data Scientist: This job role involves using advanced statistical and machine learning techniques to analyze and interpret complex data sets to develop predictive models, and to inform business decisions.
  4. Marketing Analyst: This job role involves using data analytics to analyze market trends and consumer behavior, and to identify opportunities for marketing campaigns and promotions.
  5. Financial Analyst: This job role involves using financial data analytics to analyze market trends, financial performance, and investment opportunities to inform investment decisions.
  6. Operations Analyst: This job role involves using data analytics to analyze business operations and processes, and to identify areas for improvement in terms of efficiency, productivity, and cost-effectiveness.
  7. Business Intelligence Analyst: This job role involves using data analytics to analyze business performance, and to develop reports and dashboards to inform strategic decision-making.

These are just a few examples of the job roles that students who have studied PGP in Business Analytics can pursue. The skills and knowledge gained in this program are highly versatile and applicable to a wide range of industries and job roles.

Apply Now! Application Rounds for August Batch

  • Round 1:The first application deadline is 30 May 2023 and the fee is INR 500

  • Round 2:The second application deadline is 20 August 2023 and the fee is INR 1000

Faculties

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Dr. Abhijit Gangopadhyay

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Dean and Prof. Human Resource and HR Analytics at Aegis, Founding Dean IIM Indore,Former Dean & Director, Tata Institute of Social Sciences, Mumbai (TISS).
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Dr. Vinay Kulkarni

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Director of Aegis School of Data Science. Ph.D. Indian Institute of Technology,Bombay.
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Dr. Shamsuddin Ladha

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Ph.D. in Computer Science from IIT Bombay and Monash University MS in Computer Science,Loyola University of Chicago.
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Dr. Sougata Mukherjea

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Program Director, Cloud Center of Excellence at IBM Global Technology Services Ph.D. in Computer Science, Georgia Institute of Technology MS in Computer Science, Northeastern University.
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Bhavik Gandhi

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Director of Data Sciences and Analytics at People Interactive (Shaadi.com).Masters in Computer Engineering from the University of Florida. Specialties: Data Mining, Machine Learning, Big Data, Evolutionary Algorithms, Data Analytics, Bio-informatics, Business Intelligence, Artificial Intelligence, Data Warehousing, Algorithms, Data Structures, Databases, Data Modelling, Cubes, Analysis Services, MDX, SQL query tuning, Java, Python, R, C#, C++, SQL, MySQL, SQLServer
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Amar Nayak

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Ph.D. in Econometrics and Quantitative Economics from Robert Gordon University.
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Dr. Kodliuk Tetiana

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Data Scientist, Active Wizards Ph.D., Mathematics, Institute of Mathematics of the National Academy of Sciences of Ukraine.
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Dr.Oliver Westerwinter

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Faculty For Machine Learning Foundations with Practice Lab at Aegis School of Data Science A freelance data scientist and consultant working in the fields of data analysis, machine learning, and artificial intelligence. He has also a strong interest in network and text analysis. Before working as a data scientist, Oliver was an Assistant Professor of Political Science at the Department of Political Science at the University of St. Gallen, Switzerland, and held research fellowships at the European University Institute in Florence and the University of California, San Diego. His research has been funded by several funding agencies and universities, including the Swiss National Science Foundation and the Swiss Network for International Studies, and has been published in the Journal of Peace Research, Review of International Organizations, Journal of European Integration, International Theory, and with Oxford and Cambridge University Press
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Eligibility requirements for most programs include:

  1. Bachelor's degree in any discipline from a recognized university

  2. Strong quantitative skills, including mathematics and statistics

  3. Basic knowledge of programming languages such as Python or R

  4. Good communication skills in English

  5. Relevant work experience in data analysis or related fields may be preferred but not always required

Business Analytics is a field that is useful for individuals from a variety of backgrounds who are interested in using data-driven insights to make informed business decisions. Some of the ideal candidates for a career in Business Analytics include:

  1. Individuals with a background in business, finance, economics, or management who are looking to expand their skill set and become more data-driven in their decision-making.
  2. Professionals in the IT or technology industries who want to specialize in data analysis and data science.
  3. Those with a strong background in mathematics, statistics, or computer science who are interested in applying their skills to real-world business problems.
  4. Anyone interested in pursuing a career in data science or business intelligence.
  5. Individuals who are interested in the latest technologies and want to learn how to use them to improve business outcomes.

Overall, Business Analytics is an ideal field for individuals who are interested in using data to drive decision-making and improve business outcomes.

 

Origin of Business Analytics

The origins of Business Analytics can be traced back to the 19th century when statistical methods were first applied to data analysis in order to make better business decisions. However, the field really began to take off in the mid-20th century with the advent of computers and the ability to process large amounts of data quickly. In the 1950s and 1960s, early pioneers in the field such as George Box and W. Edwards Deming developed statistical tools and methodologies to help businesses improve quality control and make better decisions.

In the 1970s, the concept of decision support systems (DSS) emerged, which provided managers with computer-based tools for making strategic decisions. By the 1990s, the field had evolved into what we now call Business Analytics, with a focus on using data, statistical and quantitative analysis, and predictive modeling to drive business decision-making and strategy. With the explosion of digital data in recent years, Business Analytics has become an increasingly important and in-demand field.

Curriculum for a PGP in Business Analytics

Term 1:

  • Business Analytics Fundamentals

  • Business Fundamentals

  • Statistics for Business Analytics

  • Spreadsheet Modeling for Analytics

  • Visualization with Power BI/ Tableau

  • Python for Data Analysis 

Term 2:

  • Applied Regression Analysis

  • Machine Learning for Business Analytics

  • Marketing Analytics

  • Time Series Analysis and Forecasting

  • Financial Analytics

Term 3:

  • Operations Analytics

  • Customer Analytics

  • Fraud Analytics

  • Capstone Project

Elective Courses

  • SQL

  • Predictive Analytics

  • Forecasting Methods for Business

  • Deep Learning

  • Social Media and Web Analytics

  • Healthcare Analytics

  • NLP for Text Analytics

  • Supply Chain Analytics

  • Data Management and Warehousing

  • Financial Accounting for Managers

  • Marketing Management

  • Operations Management

  • Decision Analysis and Optimization

  • Big Data Analytics and Hadoop

  • Data Warehousing and ETL Process

  • Project management

The focus of this curriculum is to provide students with a strong foundation in both the technical and business aspects of analytics, while also allowing them to specialize in specific areas of interest through the elective courses and capstone project.

 

Brief on each course module 

 

  1. Business Analytics Fundamentals: This course provides an overview of business analytics and its various applications in different industries. Students will learn about the importance of data in decision-making, the different types of data, and how to interpret data to gain insights.

  2. Business Fundamentals: This course covers the basic principles of business management and strategy. Topics include accounting, finance, marketing, operations management, and organizational behavior.

  3. Statistics for Business Analytics: This course covers the fundamentals of statistics, including probability, hypothesis testing, and regression analysis. Students will learn how to apply statistical techniques to business problems and interpret statistical results.

  4. Spreadsheet Modeling for Analytics: This course covers advanced Excel techniques for modeling and analysis. Students will learn how to use Excel to build decision models and perform data analysis, including scenario analysis, optimization, and simulation.

  5. Visualization with Power BI/ Tableau: This course covers the principles of data visualization and the use of visualization tools such as Power BI or Tableau. Students will learn how to create effective data visualizations that communicate insights and support decision-making.

  6. Python for Data Analysis: This course provides an introduction to the Python programming language and its use in data analysis. Students will learn how to use Python libraries such as Pandas and NumPy to manipulate data and perform basic data analysis tasks.

  7. Applied Regression Analysis: In this course, students will learn how to build regression models to analyze the relationships between variables in business data. The course will cover topics such as simple and multiple linear regression, model diagnostics, and model selection. Students will also learn how to use regression analysis to make predictions and solve business problems.

  8. Machine Learning for Business Analytics: This course introduces students to machine learning techniques such as clustering, decision trees, and random forests, and how they can be applied to solve business problems. Students will also learn about deep learning techniques such as neural networks and how they can be used for image and text analysis.

  9. Marketing Analytics: This course focuses on how analytics can be used to solve marketing problems. Students will learn how to measure marketing effectiveness, segment customers, and optimize marketing campaigns. The course will also cover topics such as customer lifetime value and customer churn.

  10. Time Series Analysis and Forecasting: This course module covers the fundamental concepts and techniques for analyzing time series data and making forecasts. Students will learn how to model different types of time series, identify patterns, and apply various forecasting methods.

  11. Financial Analytics: In this course, students will learn how to use analytics to solve financial problems such as portfolio optimization, risk management, and fraud detection. The course will cover topics such as time series analysis, volatility modeling, and value at risk. Students will also learn how to use software tools such as R and Python to analyze financial data.

  12. Operations Analytics: This course module covers the use of analytics in operations management. Topics include process analysis, queuing theory, inventory management, and supply chain management. Students will learn how to use data analytics tools and techniques to optimize operations.

  13. Customer Analytics: This course module covers the use of customer data to understand customer behavior and preferences. Topics include customer segmentation, lifetime value analysis, and churn analysis. Students will learn how to use customer data to develop effective marketing strategies.

  14. Fraud Analytics: This course module covers the use of analytics to detect and prevent fraud. Topics include fraud detection techniques, fraud risk assessment, and fraud prevention strategies. Students will learn how to use data analytics tools and techniques to identify and prevent fraud.

  15. Capstone Project: The capstone project is a culminating experience that provides students with an opportunity to apply the knowledge and skills they have acquired throughout the program. Working in teams, students will identify a business problem and use data analytics tools and techniques to develop a solution. The project will involve data collection, analysis, and interpretation, and will culminate in a final presentation to a panel of industry experts.

Frequently Asked Questions

Aegis, a premier higher education institute, was established in 2002 with the support of Bharti Airtel to develop the next generation of cross-functional techno-business leaders. The institute has a strong tradition of innovation and was a pioneer in launching a Post Graduate program in Data Science in India in 2015. Since then, hundreds of participants have successfully launched their careers in the rapidly growing and highly rewarding fields of Artificial Intelligence, Data Science, Machine Learning, Deep Learning, Big Data, Business Analytics, and Data Engineering. Aegis also offers specialized programs in Cyber Security, Applied AI, and Full Stack Development. Additionally, the institute provides executive education, corporate training and consulting in the field of exponential technologies. Aegis also runs India's most prestigious innovation award, the Aegis Graham Bell Awards and hosts various conferences such as the Data Science Congress, People Analytics Conference, and Deep Learning Summit.

Business Analytics is the practice of using data analysis to make data-driven decisions in business. It involves the collection, processing, and analysis of data to extract insights that inform business decisions.

Anyone who is interested in data-driven decision-making and wants to pursue a career in business analytics can benefit from a PGP in Business Analytics program. This includes professionals with a background in business, engineering, mathematics, or computer science.

While requirements can vary between programs, typically students are expected to have a bachelor's degree in a related field such as business, engineering, mathematics, or computer science. Additionally, some programs may require prior coursework or experience in statistics, programming, or data analysis.

Graduates of a PGP in Business Analytics can pursue a variety of careers in the data analytics field, including roles such as business analyst, data analyst, data scientist, marketing analyst, and operations analyst.

Students in a PGP in Business Analytics program will develop skills in statistical analysis, data visualization, predictive modeling, machine learning, decision analysis, and optimization, as well as programming languages such as Python and R.

Many PGP in Business Analytics programs are designed to be hands-on, with a focus on applied learning through case studies, projects, and internships. However, some programs may also include theoretical coursework.

Yes, Aegis offers online PGP in Business Analytics programs that can be completed remotely.

PGP in Business Analytics programs is 11-month duration with 45 credit unit

The admission process for the Aegis PGP in Business Analytics typically involves the following steps:

  1. Online Application: Candidates are required to fill out an online application form available on the Aegis School of Data Science website.
  2. Application Review: Once the application is submitted, it is reviewed by the admissions committee.
  3. Entrance Exam: Candidates are required to take an entrance exam, such as CAT/GMAT/XAT/CMAT/ATMA/MH-CET, or they can take the Aegis Online Entrance Exam.
  4. Personal Interview: Shortlisted candidates are invited for a personal interview.
  5. Offer of Admission: Successful candidates are offered admission to the program.

The tuition fee of a PGP in Business Analytics program is 3.5 lacs the on-campus program and 1.5 lacs for the online program.

MBA (Master of Business Administration) and PGP (Post Graduate Program) in Business Analytics are two different types of programs that offer different specializations.

MBA programs typically cover a broader range of business topics, including finance, marketing, human resources, operations, and strategy. The focus of an MBA program is on developing a well-rounded understanding of business and management principles.

On the other hand, PGP in Business Analytics is a specialized program that focuses specifically on the application of analytics in business decision-making. The curriculum includes courses in statistical analysis, data mining, machine learning, and other data- related topics. The focus of a PGP in a Business Analytics program is on developing skills in data analysis and interpretation.

While there is some overlap between the two, business analytics and data science are distinct fields.

Business analytics generally involves analyzing business data to make informed decisions and improve business performance. This may involve using statistical and quantitative analysis techniques to extract insights from large datasets, with a focus on applying these insights to real-world business problems.

Data science, on the other hand, involves working with large and complex datasets to identify patterns and extract insights using a range of quantitative and computational techniques, including machine learning and artificial intelligence. Data science is often focused on developing new algorithms and models to extract insights from data, with applications in a wide range of fields beyond business.

While both fields involve working with data and applying quantitative analysis techniques, business analytics is generally more focused on solving specific business problems and improving business performance, while data science is focused on developing new models and algorithms to extract insights from data in a more general sense.

Here are the top 5 reasons to join the Aegis PGP in Business Analytics program:

  1. Comprehensive curriculum: The program covers a wide range of topics in business analytics, from fundamentals to advanced techniques, providing a comprehensive and practical understanding of the field.
  2. Industry-aligned program: The program is designed in collaboration with industry experts and leading organizations, ensuring that students receive relevant and up-to- date knowledge and skills required by the industry.
  3. Experienced faculty: The faculty consists of experienced professionals with extensive industry and academic expertise, providing students with the opportunity to learn from experts and gain valuable insights.
  4. Hands-on learning: The program provides hands-on learning opportunities through various case studies, projects, and internships, enabling students to apply the concepts and techniques they learn in real-world scenarios.
  5. Career opportunities: The program opens up a plethora of career opportunities in the field of business analytics, including roles such as data analyst, business analyst, data scientist, data engineer, and more. Aegis has a strong placement record, with students being placed in top companies across various industries.

While there is some overlap between the two, business analytics and data science are distinct fields.

Business analytics generally involves analyzing business data to make informed decisions and improve business performance. This may involve using statistical and quantitative analysis techniques to extract insights from large datasets, with a focus on applying these insights to real-world business problems.

Data science, on the other hand, involves working with large and complex datasets to identify patterns and extract insights using a range of quantitative and computational techniques, including machine learning and artificial intelligence. Data science is often focused on developing new algorithms and models to extract insights from data, with applications in a wide range of fields beyond business.

While both fields involve working with data and applying quantitative analysis techniques, business analytics is generally more focused on solving specific business problems and improving business performance, while data science is focused on developing new models and algorithms to extract insights from data in a more general sense.

Business Analytics and Business Analysis are two related but distinct fields.

Business Analytics is the practice of using data, statistical analysis, and quantitative methods to gain insights and drive decision-making in business. Business Analytics involves working with large datasets, data mining, data visualization, predictive modeling, and other techniques to identify trends and patterns, and to make informed decisions based on data.

Business Analysis, on the other hand, is a broader term that refers to the process of identifying business needs and determining solutions to business problems. Business Analysis involves working with stakeholders to identify business goals, requirements, and constraints, and then using various techniques to define and prioritize business requirements, design solutions, and ensure that the final product meets business needs.

While both fields involve working with data and making informed decisions, Business Analytics is more focused on using data to drive insights and decisions, while Business Analysis is more focused on the overall process of identifying business needs and designing solutions to meet those needs.

To be successful in business analytics, there are several key skills that are important:

  1. Analytical and critical thinking: The ability to think logically, break down complex problems into smaller components, and identify patterns and trends is crucial.
  2. Data analysis: Business analysts must be proficient in data analysis techniques and tools, including statistical analysis, data visualization, and data mining.
  3. Communication: Business analysts must be able to communicate their findings to a wide range of stakeholders, including executives, managers, and technical teams. This requires excellent verbal and written communication skills.
  4. Business acumen: It is important for business analysts to understand the business they are working for, including its goals, objectives, and operations. This enables them to identify opportunities for improvement and make strategic recommendations.
  5. Technical skills: While not always required, having a strong foundation in programming languages like Python, R, or SQL can be helpful for data wrangling and analysis.
  6. Project management: Business analysts often work on projects, so having strong project management skills, including the ability to manage timelines and budgets, is important.
  7. Adaptability: Business analytics is a rapidly evolving field, so the ability to adapt to new technologies, techniques, and methodologies is crucial.