give a miss call on
please call on
Chitkara Business School believes that data analytics is a managerial problem, not primarily a data science or technology problem. We argue that business leaders need a working knowledge of data science to reap the rewards of big data and analytics. Analytics requires managerial judgment, demands process and incentive changes, and is most effective when problem-driven; as a result, managers play a crucial role in the success of its implementation.
The influence of big data and analytics is only growing stronger. Companies are finding new ways to leverage data as a source of competitive advantage. Analytics is becoming increasingly integral to the way organizations understand and market to customers, and the way they structure their internal systems and practices.
To meet these demands, Chitkara Business School has introduced 2-Year MBA Program in Business Analytics this year, which will look at developing the following skill sets in our students-
This track is appropriate for MBA students interested in transforming large amounts of data into better decisions. Possible careers include consulting in data-rich environments, analytical marketing, information technology and financial data analysis.
Students interested in the track should have a strong interest in analytical approaches to management, as shown by aptitude in courses such as optimization, probability, statistics and statistical decision making.
Chitkara Business School’s data analytics curriculum is built around the observation that managers do not always have a sense of what analytics can do for them and data scientists do not always understand enough about a manager’s problem to be helpful. What is missing are analytics-savvy MBAs who have a passion for business problems and who are so fluent in data analytics that they can easily converse with and manage teams of data scientists. As a result, our teaching philosophy in data analytics is to be relentlessly problem driven while taking a deep dive into methods and applications.
These courses provide the statistical and methodological foundations for data analytics. Some of the courses are Business Analytics and Marketing Research.
These courses teach students how to apply data analytics to different business problems. Students learn new methods as needed to solve the business problems at hand and are required to apply these methods to large real-world datasets. Some of the courses under competitive advantage are Digital Marketing Analytics and Customer Analytics.
These courses provide depth in selected areas. In contrast to “Competitive Advantage” course, they can be methods as opposed to problem-focused. Visualization for Persuasion and Technology for Analytics are some of the courses under this.
These courses allow students to apply their skills from “Competitive Advantage” and Deep Dive” courses to real company situations. Mentorship, internship / project work will part of the experiential learning.
The capstone course comprises a project which exposes students to a real business problem which they will solve using visualization, data mining and optimization techniques. The problems are solicited from businesses, although students are welcome to propose projects as well. Some projects will be taken from financial services organizations, technology companies, retailers, marketers, manufacturers and distribution services to solve these types of problems such as
Analytics has become an attractive career destination for MBA students. The field offers exciting and challenging work that leverages business as well as technical knowledge in a largely merit-based environment. The financial rewards are great and growth opportunities aplenty.
There was a time when businesses looking for analytic professionals would primarily focus on candidates with a master’s degree in quantitative fields such as Maths, Statistics, Economics and Computer Science. PhDs were in great demand too. A couple of things have happened in the last few years.
Firstly, the analytic tools have become more user-friendly. A lot of the latest tools are GUI-based. Instead of learning a coding language one now needs to just master the graphical user interface of the tool, which is a lot easier.
Secondly, experience has taught businesses that effective analytics exists only in the context of a business and a business problem. Those who have strong technical and quantitative skills but a poor understanding of the application of analytics in a business context, are likely be weak performers.
Even experienced statisticians sometimes get caught up in the technique and accuracy of their predictive models and forget to pay sufficient attention to common sense and business logic.
So a combination of more user-friendly analytic tools and a need for higher business understanding to leverage the analytic insights better, has forced businesses to reverse their hiring philosophy. Instead of trying to find business-savvy mathematicians and statisticians, companies (like Genpact, WNS, Infosys, Accenture and Target) are now looking to hire data-savvy, business professionals with starting salaries ranging from 4 lacs to 12 lacs for deserving candidates.