In this post, we will talk about the career opportunities that PGDBA graduates have after completing their course.

Banking and Financial Services Institutions

In line with the industry trend, banking and financial services sector is one of biggest consumer of analytics and data science talent. This is reflected in the hiring trend of the course as well. Organizations such as D.E. Shaw, NPCI (National Payments Corporation of India), American Express, MasterCard Advisors, Yes Bank, HDFC Bank and many other been associated with the program since the beginning. The companies in this sector offer two kinds of roles – Business Analyst and Data Scientist.

Healthcare and Pharma Companies

The other prominent sector which consumes analytics in a big way is healthcare. Some of the world’s leading healthcare and pharma companies have hired from the program. Johnson & Johnson, United Health Group, Abbott and others have been active in the hiring process of students, for internship as well as final placements.

Corporate Houses

Large Indian corporate houses have also started setting up their internal analytics and data science teams. There could be separate teams for each subsidiary/function or a centralized “Center for Analytics” kind of a setup which caters to the entire group’s analytics needs. Mahindra and Mahindra, L&T and ITC are some of the key corporate houses which have visited the campus to hire talent from the program.

Consulting, Research and Analytics Service Providers

Gartner, PwC, Deloitte, McKinsey Knowledge Center, Alvarez & Marsal, EXL are some of the companies which have visited the campus in the past. These companies usually offer consulting roles wherein you have to deal with external clients and solve their analytics (and technology) related problems. Gartner could be an exception where work is done primarily to improve internal process. These roles are mostly business analyst kind of roles where you may have to work on different tools and technologies depending on the need of the project.

E-Commerce Companies

The likes of Flipkart, Myntra, Amazon, Walmart Labs and other e-commerce companies have visited the campus and recruited from the program for the roles of data scientists and business analysts. The roles offered by these companies are more of technical in nature, more inclined towards the data science side of the world.

Other Companies

There are many more companies operating in different sectors such as telecom, media and other sectors which have hired from the program. Some of the big names are Viacom, Jio, ACT (Atria Convergence Technologies Ltd.), NTPC, Ittiam Systems, among others. These companies again offered both kind of roles – business analyst and data scientist.

Disclaimer: This information is based on the research through primary and secondary sources that AI Monks has done. The information in this article should be used only for indicative purposes. AI Monks is not responsible for any information that may be different from the actual scenario.

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We have broken down this entire scenario into different questions which people have asked us.

If I apply for the course, would my age be a deterrent? 

In the application process, you will score less marks in certain areas (such as work ex) as compared to aspirants with less work experience. You will have to compensate this with higher marks in written test and interview. Otherwise, there is no restriction on the work experience to apply for the course.

Would I be considered an outlier in an otherwise homogeneous group?

In a way, yes. If your work experience is over 8 years, then chances are very high that you will be only person in the batch with that high work experience. The average work experience of students entering the course is 2-3 years, with only 4-5 students having 4-5 years or more experience.

In your experience, have you seen or heard of any student admitted in PGDBA program with over 8 years of experience? 

In 2015-17 batch, there were a couple of guys with around 5-6 years of experience. They both got placed with a decent package, but their expectations were higher than what they got. Remember 2015-17 was the first batch, so the placements were not as sky-high as they have been for the subsequent batches. In the batch 2016-18, there was a guy with around 10 years of experience (mid 30s). He got a very good placement (highest domestic salary) but I would take that with a pinch of salt.

If admitted, are the students with 8 years or above experience discriminated against during internship/placement season?

Companies, in a lot of cases, don’t prefer students with that high experience. And, if their prior experience is not in data analytics/data science, then it becomes even more challenging. They can’t offer you senior role because you don’t have hands-on experience in data analytics; they can’t offer you junior role because you have high experience. That becomes a challenge.

An important point during the placements/internships will be – how will you distinguish yourself from the students who are 4-5 years younger to you? What additional value will you bring to the table?

Is my worry related to high work experience for PGDBA correct? 

It is completely okay to be worried when you have chances of becoming an outlier in a batch where you have to spend 2 years. There are a few students with 5+ years of experience in every batch but 8-9 years is a little on the higher side. It could be a little challenging but certainly, not impossible.

Read about Gautam Kumar’s experience of doing PGDBA. He joined the PGDBA 2015-17 batch with 6 years of work experience.

In conclusion, I would say:

• Be very sure that you want to do this course because it will come with its own risks of not getting desired career opportunities. You may get, you may not get. Are you ready to take that risk?
• If you want to learn analytics for your own interest, you may opt for multiple other options which are more of distance learning or mix of classroom+online courses. You may refer to these links for few courses that we have listed on our website. Weekend Classroom Courses | Online Courses | Online+Classroom Courses
• Another option is that you may talk to your current employer. If they have an option of sabbatical or something similar, you may negotiate with them that you will join them after the course completion with agreed upon hike and designation (this could act as a backup option in case you don’t get desired opportunity).

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Our take on this –

No, we don’t think having experience in coaching industry or academics is going to be a deal breaker. The PGDBA eligibility criteria doesn’t mention the type of industry where you should have prior work experience to gain work-ex score.

Answer ‘THREE’ important questions:

Essentially, for any interview you should have clarity on below 3 questions:

1. Why did you choose the particular industry as your desired profession?
2. Why can’t you up skill (or learn the skills that you are going to learn in the course) yourself in your current industry?
3. How are you going to leverage your prior work experience in your course/post-course professional experience?

If you can justify the reasons for choosing academics/coaching as your professional industry, it’s perfectly fine. There is nothing negative about being a teacher/mentor at any coaching institute or a college or a school. I have tons of friends who used to teach at coaching institutes/schools; currently, they are either studying at one of the IIMs or graduated from there. That gives me some data as well that IIMs don’t discriminate against teachers/academicians.

Additionally, you should be clear on how your experience in education industry will be helpful to you in PGDBA course and how are you going to leverage that going forward.

You may also be interested in PGDBA Cut-Off, Number of Applicants, Wait list Movement and Everything Else You Need to Know

You may also be asked why can’t you learn analytics and data science along with your job? Since you are already into teaching industry, you can learn yourself. And, why do you even need to learn analytics at the first place? Why do you need analytics in the teaching profession? You should be prepared with answers to all these questions. We suggest you read applications of analytics and data science in the education/teaching industry. Learn how educators are using data science and analytics in improving their teaching methods? Study about applications of data science in making education more personalized and adaptive.

In conclusion, we would say that having experience in education/academics/teaching industry is not negative per se; not able to justify your decisions is the biggest negative.

“Do you know AI Monks provides exclusive test series for PGDBA entrance exam preparation? Visit here to know more.”

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The reason I am writing this post is that people have asked me this question numerous times and since I have been through this entire journey myself, I am adequately qualified to write this post.

I must say that the entire PGDBA course curriculum has been designed in a very thoughtful and meticulous manner. The entire transition of courses from pre-semester to the final semester is very smooth and structured.

The overall curriculum, which includes classroom courses, diverse projects, tools, competitions, guest lectures and of course end semester exams, offered in the program is very application based and very relevant to the current industry needs. A lot of emphases is given on projects, case studies, interaction with industry people, along with theoretical concepts that are an inevitable part of the journey.

Below are the details about the course structure, curriculum and details on how the entire course unfolds in the two years.

The program session usually starts in the third week of June with a pre-semester module at IIM Calcutta. This pre-semester module is like a crash course (4-5 weeks) wherein the focus is on building the foundation for the next 4 semesters. Subjects taught in this semester gives you a sense of analytics and other basic areas of management such as marketing, financial statements and organizational behaviour. The subjects offered throughout the course have a strong focus on case studies and/or projects, and the same is true for the pre-semester module. A lot of case studies in the area of analytics, marketing and organizational behaviour are covered in the pre-semester.

After the pre-semester, the first semester starts at ISI Kolkata. ISI Kolkata – as the institute is famous for its strong foundations in statistics – focuses on building statistical knowledge which is used in the subsequent semesters. A few of the topics that you study in the first semester are linear algebra, database management, hypothesis testing, probability, Markov chains, distribution curves, inferential analysis, regression models, random forest, missing value imputation, graphical representation of data and few aspects of computing. Courses are laden with projects, theoretical as well as coding assignments, tutorial classes, etc.

At the end of the semester, you develop a good understanding of statistical techniques, a fair bit of computing and machine learning knowledge. Students usually work on SQL, R and Python in this semester. By the time you finish the first semester, you will start appreciating the applications of analytics and data science in real-world. You will develop an understanding of the level wherein you will be able to identify (at least at a high level) how a particular data science model must have been developed.

The second semester starts at IIT Kharagpur in the month of January when preparations for Spring Fest (IIT Kgp’s Cultural Fest) are at peak. The campus has altogether a different vibe at that time – full of activities and chirping of people coming from different campuses all over India. Now, let’s get back to our main topic of discussion – PGDBA structure and curriculum. This semester offers you a plethora of electives to choose from. A few of the electives are offered by Computer Science department, a few other electives by management school of IIT Kharagpur (Vinod Gupta School of Management) and other electives by other schools and departments such Rajendra Mishra School of Engineering Entrepreneurship, Mathematics department, etc. at IIT Kharagpur.

Algorithms, Machine Learning, Regression and Time series analysis, Operations modelling are some of the mandatory courses. The electives offered in this semester are supply chain analytics, optimization theory, marketing analytics, healthcare analytics, product analytics, banking and financial analytics, information retrieval, network theory, among others. Depending on how many students opt for a particular elective, professors take a call whether to float the course. Here again, students usually work on R, Python and some start with Hadoop and other things if required. Some of the students also use SPSS, Minitab (in case they are able to get the license, else learn from the trial version).

The third semester at IIM Calcutta starts in June and focuses mainly on the application part of whatever you study in the first two semesters. Though the number of courses that you cover in this semester is higher, the duration of each course is less. Major analytics courses offered in this semester are categorical data analysis, business data mining, high-frequency trading, financial risk management, customer analytics, predictive modelling, forecasting, among others. Some of the management courses offered are strategy management, business communication, project management, among others. Almost all the courses taught in this semester are either case-based or project-based or both.

Please note that the electives offered every year depending on the availability of faculty and preference of students. All electives may not be offered all the time.

During the third semester (around August-September), placements for internships take place. Companies start coming in for pre-placement talks from June last-week or July first week.

From October-November, starts the final semester. This semester is the internship period where you go to a company and intern for 5–6 months. You can also opt to go for an academic intern as per your choice. The semester ends with a presentation to a panel of 3–4 faculty members telling them about your internship projects. This usually happens in the month of May after you complete your internship. Students not offered PPO (pre-placement offer) by their interning company come back to campus for their final placements. The final placements take place in the month of February-March. These students then go back and resume their internship.

That’s how the entire journey unfolds in two years for PGDBA students.

If you are interested in the program, we suggest you go through the interview experiences of students and alumni of this program.

Hope this was helpful and gave you insights about the program and the entire journey. Please post your questions or comments in the comments section below.

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Below is a detailed break-up of the type of questions asked in the previous years’ papers.

Check our PGDBA 2020 exam analysis. 

Every year, the quant section of the entrance exam has 25 questions out of a total of 50 questions. The topics are based out of class XI and class XII Mathematics. About 37% of questions are based on Calculus – the majority of them around continuity/differentiability, application of derivatives and integrals, limits and determinants. About 23% of questions are related to Algebra – Complex Numbers, Quadratic Equations, Permutation Combination and Series. Coordinate geometry and geometry account for 20% of questions – covering circles, locus of a point, distances, equation of conic sections, formula related to triangles. Other topics include sets, functions, relations and functions and they cover the remainder of the section.

A single question might test you on multiple topics and is necessarily not limited to just a single topic. The above categorization is only indicative of the pattern of Quant section and aims to help you prepare for the entrance accordingly in conjunction with Verbal, Logical Reasoning and Data Interpretation.

The top 3 categories are Calculus, Algebra and Co-ordinate geometry which together comprises almost 70-80% of the quant section every time. This should be the starting point of preparation and the remaining topics can be covered in succession. Though Numbers aren’t covered as part of NCERT syllabus, a few questions in this category can be expected as can be seen in 2019. Do watch out for PGDBA 2021 Test Series + Preparation Package for more info.

To begin your preparation for the Quant section, you can start with NCERT books. You can also view the entire list of topics in the table below.

Check out PGDBA 2021 Test Series + Preparation Package only at INR 2,499/-. The test series and package has been designed by AI Monks, a PGDBA alumni initiative.

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Let’s first understand the background of students entering the PGDBA program and then answer this question.

Based on our estimates and research, hardly 10-15 per cent of students, i.e., 5-8 students, out of 50-55 students, have considerable background in statistics. Does that put you at a disadvantage in the course? At prima facie, it does look so. But, in reality, it isn’t the case because the amount of statistics that is part of the course can be easily learned during the course. Professors start statistics (or any other topic, for that matter) from the very basics and then gradually build. So, if we talk about the course, you won’t be at any disadvantage when compared to other students who have a background in statistics. The only thing is that you will have to put in a little extra effort to come up to speed, nothing else.

If you were to talk about the interviews during the selection process, then our suggestion will be to spend some time and learn the basic concepts of statistics. This should not take more than a few hours to prepare.

Now, let’s talk about students who come with a coding background. The percentage of this category of students is a little higher. I would say, “they do have some advantage because they can code well, and they can code fast.” Having said that, this does not mean you won’t be able to compete with them. You can easily compete with them and maybe, beat them in their own game – all that with little extra effort.

When I joined the PGDBA program, I neither had experience in coding nor in statistics but I had the will to learn. I had to put in extra hours to learn and practice coding. Eventually, I reached a level where I wouldn’t hesitate to pick up my system and start writing a code whenever required.

I would end this post by saying that you don’t need to worry about your performance in the PGDBA program if you don’t have coding/statistics knowledge. The only thing is you should be willing to work hard, rest everything can be managed.You may also want to read Low marks in Class 10/12/Undergraduate – Should I go for PGDBA?

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1. The first category – this category of students is preparing for CAT (as their first focus) or any other entrance exam for management courses
2. The second category – this category of students has not prepared for CAT; hence, they are starting their preparation from scratch.

There is a slight difference in the PGDBA 2021 strategy for the above two categories; hence, I am covering them separately.

Let’s talk about the first category.

If you are preparing for CAT, my assumption is that you already have prepared for Verbal, Logical Reasoning and Data Interpretation sections pretty well. Since these sections are almost same for both the exams (rather, I find PGDBA difficulty level for such questions lower than CAT.), you don’t need to prepare too much in addition to what you are preparing for CAT. The verbal section of PGDBA is essentially a subset of the verbal section of the CAT exam. Logical reasoning and data interpretation are more or less same, with PGDBA having a lower difficulty level.

Now, coming on to the Quant section – there exists a good difference in the syllabus for both the exams. Key additional topics in the PGDBA quant syllabus are functions, limits, continuity, derivative, maxima & minima, methods of integration, evaluation of areas using integration, conic sections, matrices and determinants, among few others. So, if you are preparing for CAT exam, you would not have covered these topics. My suggestion to you guys would be to focus on CAT exam right now and give your best shot. Once you are done with the CAT exam, you would have 2-3 months (mid-November to mid-February) to prepare for these additional topics. Focus on these additional topics during that time and revise/brush up other topics before the exam.

Now, let’s talk about the second category that is not preparing for CAT, but is focusing on PGDBA.

You guys need to pull up your socks and start preparation right away. You need to focus on 3 sections for the exam:

1. Verbal
2. Logical Reasoning and Data Interpretation
3. Quantitative Aptitude

Let’s talk about each of the sections one by one.

Verbal

In the past four years of examination, there have been five different types of questions which have been asked. The breakup of these five question-types has been given in the below table.

Looking at the trend of questions asked, it is fair to assume that the admission committee will continue with reading comprehension and jumbled sentences. The other 2 question types (Identify Incorrect Sentence and Incorrect Word Usage) are more based on grammatical usage of words.

Though there is no fixed number of questions or passages that you should prepare, we believe you should aim to practice the bare minimum of 15-20 reading comprehension passages and 20-25 questions of other types, each. If you have time to practice more questions, please do.

Logical Reasoning and Data Interpretation

Though most of the times this section has consisted of a set of 5 logical reasoning questions and one set of 5 data interpretation questions, 2018 showed a slight deviation in the trend for logical reasoning questions. The admission committee might play with this section and a variety of questions by breaking one set of 5 questions into 2-3 sets of 2 or 3 questions each.

We would suggest you practice at least 15-20 sets of each of logical reasoning and data interpretation for the exam. If you have time to practice more questions, please do.

Check our PGDBA 2020 exam analysis.

Quantitative Aptitude

This is the most important section for all the aspirants since this forms 50 per cent of the overall exam score. Quite a few of the topics covered in this section are not part of any other management course entrance exam, so both categories of aspirants should focus on this equally.

You can also get detailed insights and analysis on how to go about developing a PGDBA 2021 strategy for the quant section.

In case you have not been in touch with mathematics for a long time, we suggest you begin with class 11/12 NCERT books, and revise your basic concepts and practice a few questions. Once you are done with NCERT books and feel confident about the concepts and your understanding, you should move to difficult questions.

AI Monks provides best-in-class test series and preparation package to prepare for PGDBA 2021. Click here to know more about the test series.

For the topics that are also a part of the CAT syllabus (such as combinatorics, algebra, basic geometry, linear equations, quadratic equations, among others), you may pick up any CAT preparation book and practice questions from there. For the topics that are not a part of CAT syllabus, you may pick up class 11/12 mathematics reference books such as RD Sharma or others. Additionally, you may pick up IIT JEE entrance exam preparation books and practice questions from there.

There’s no specific number of questions as to how much you should practice. Whatever time you have till the written exam, practice enough so that you don’t feel that you should have practised harder later on.

The post How to build PGDBA strategy? Know from PGDBA Alumni appeared first on AI Monks | Counsel.

Criteria and weights for final selection to PGDBA

A candidate’s final score for selection is calculated on the basis of Table1, which indicates the criteria and their weights to be used for the final selection to PGDBA for the batch 2020-22.

While on the work experience side:

They have nowhere mentioned the qualifying marks for Class 10/12, even though there is a criterion on the qualifying marks for the graduate degree. Minimum qualifying marks during the graduate degree are either 60% or CGPA 6.5 on a scale of 10.

This answers the question from the qualification perspective.

Next part is compensating for the low points you will get due to low marks in Class 12. This is something which can’t be changed, so the best option is to work hard and improve on the areas that can be improved – written test and interview. You should in extra effort for the written exam and interview in order to compensate for it.

You may also want to check the experiences of students who have appeared for PGDBA exam over the years. Here’s the link for your reference.

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Both the courses have a very clear distinction in terms of the course content, academic rigor, pedagogy, opportunities after course completion, among many other aspects. Let’s understand the different aspects of PGDBA and PGDM one by one.

Course Content 

The content that these courses offer is nowhere similar to the other one. PGDBA is more inclined towards topics like statistics, machine learning, algorithms, coding, followed by the application of different techniques and models in different areas of management. To elaborate further, you will be studying marketing, finance, supply chain, operations but from the analytics perspective. In PGDBA, you will find answers to questions like what are the different analytics techniques that are used in these areas, or how can you make sense of data that is being generated in these fields. While in PGDM, your focus will be mainly on applying marketing or finance concepts in the business world to drive sales or improve portfolio yield.

Academic Rigor

While both the courses are equally rigorous and require you to be on your toes, the rigor at PGDBA may vary from campus to campus. Since students spend time in three different campuses and each of the campuses has its own legacy of functioning, the rigor required in each of these courses may vary.

Pedagogy

While the focus in both the courses is to make the students ready to face the business world, there is a subtle difference between the pedagogy of two courses. In PGDBA, the focus is to do as many data-intensive projects as possible. There could be a few theoretical subjects, but in most of the subjects, depending on the electives you choose, you will be required to do a project, either individual or group-based. There are a few management subjects also where the case-based approach is followed; however, such subjects are limited in number. In PGDM, the case-based approach is the most preferred approach.

Industry Opportunities

The most common job roles that you may get after completing a course in PGDBA are those of Data Scientist and Business Analyst. The difference between both the roles is a question of debate; however, one may assume that the person involved in more technical issues with data is a Data Scientist; while the person involved with visualization or solutioning is a Business Analyst. There are also other roles that may involve consulting in the analytics team of a management consulting firm or even working as a Consultant in a pure-play analytics firm. In the finance domain, a lot of opportunities are available in high-frequency trading. There are companies like Walmart Labs which offer technical jobs such as one of Data Science. On the other hand, the students of PGDM are offered work profiles like Management Consultant, or the ones that include core marketing.

Before you start to set your target to pursue either of these courses, it is important that you understand the details of each. You must have a clear idea of what subjects you would be focusing on, what job roles may be offered to you, and what expectations you should hold from the course you are entering into.  PGDBA and PGDM are two very different courses and one should not treat them as a substitute for each other.

To learn more and be assured of your admission in PGDBA, read more about the assistance we offer.

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While receiving queries regarding the admission to PGDBA, topics to be studied for the interview had been the most common. Aspirants are concerned about the level of Mathematics or the level of Statistics that they should study for the interview. Here, we are listing down a few topics that you should prepare for your interview.

Probability

1. Concept of Probability, Sample Space, Independent Events, Disjoint Events, etc.
2. Bayes Theorem
3. Random Variables,  for example- Discrete/Continuous Random Variables; Expectation and Variance of Random Variables; Probability Density Function and Probability Distribution Function for various common distribution like Uniform, Binomial, Bernoulli, Normal (Gaussian)
4. Concept of Sample Mean, Law of Large Numbers and Central Limit Theorem

Statistics

1. Different types of Data/Variables, for example- Numeric, Interval, Categorical, Nominal, etc.
2. Measures of Central Tendency and Variance like Mean, Median, Mode, Quantiles, Standard Deviation and Variance; Difference between these summaries and which of these is more representative of the central tendency for which kind of data and for what application; What’s a skewed distribution, for example- Left v/s Right Skewed and how is Median and Mean different for these distribution
3. Concept and Definition of Covariance and Correlation (for example- Pearson’s Correlation); the difference between Correlation and Causality
4. What’s Regression Analysis and the difference between Linear and Logistic Regression
5. Concept of Statistical Inference, Confidence Interval, Hypothesis Testing and some
6. Statistical Tests, for example- Z-Test and t-Test

Linear Algebra

1. System of Linear Equations: Conditions for Independence & Consistency, Conditions for No Solution, Unique Solution & Infinite Solutions
2. Matrices & Determinants
3. Basics of Vector Spaces: Linear Combination, Linear Span, Linear Independence, Basis Function, Linear Subspace

Machine Learning

1. Types of Learning: Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning
2. Examples of Supervised and Unsupervised learning: Regression, Classification, and Clustering
3. Basic Idea about some fundamental methods for each of these learning methods like Linear Regression, Logistic Regression, and K-Means Clustering

Calculus

1. Basics of Limits and Differentiation
2. Critical or Stationary Points like Minima, Maxima and Inflexion Points
3. Basics of Integration

It must be noted that the comprehensibility of these topics cannot be assured due to the personal nature of the interviews. For instance, if you have highlighted exposure to any particular domain in your application, you should be well prepared for those too.

You may also learn from the real experiences of students from the course here.

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