Praxis Business School: Empowering the Next Generation of Data Scientists to Redefine Industries

Praxis Business School encapsulates the ethos of the translation of theory into practice which has been the guiding philosophy of the institution and has enabled its evolution into a contemporary, industry-facing business school.

Praxis runs a one-year full-time Data Science program from its Kolkata and Bangalore campuses and online programs in data science for working professionals. In addition, Praxis offers a 2-year Full-Time AICTE Approved Residential Post Graduate Diploma Management Program from its Kolkata campus.

Praxis’ programs have been well received by the industry and the one-year Data Science program has been consistently ranked as one of the top 5 programs in data science in India. The college is well known for the quality of the faculty team it has been able to build. Faculty members with impeccable academic pedigree and enormous industry experience design and deliver programs that are relevant and effective in today’s analytical space.

The Andhra Pradesh Government has chosen Praxis as its academic partner for delivering the analytics program at the International Institute of Digital Technologies (IIDT, Tirupati). Praxis has also partnered with Savitribai Phule Pune University to deliver the PG program in Business Analytics at the University.


Promising Career in Data Science

Praxis offers a Post Graduate Program in Data Science with ML and AI (Formerly known as PGP in Business Analytics) which aims at equipping students with the tools, techniques and skills to enable a seamless absorption into the domain of Analytics and grow into the roles of Data Scientists:

•  Technology Skills: An understanding of a wide range of tools that are commonly used to extract, analyze and visualize data.

•  Analytical Skills: Knowledge of statistics, data mining, machine learning and deep learning techniques and the ability to create predictive models.

•  Domain Knowledge: A fair understanding of business functions like marketing, finance, operations and the application of Analytics in various verticals.

•  Communication and Visualization Skills: The ability to tell an effective story.


Agile Leadership with Diverse Experience

Charanpreet Singh is the Founder and Director of Praxis Business School Foundation. Has been a part of the corporate world for 20 years and has a rich experience in industries as varied as Cryogenics, Steel, International Trade, Consulting and IT with organizations such as British Oxygen, Tata Steel, PwC and Compaq-HP. At HP, Charanpreet was the Country Manager, Marketing for SMB when he decided to switch to his first passion, academics, and embarked on a mission to set up a business school of the highest quality in the country. A winner of the Chevening Scholarship for Young Managers awarded by the British Government, Charanpreet has strong professional interests in the areas of information technology, analytics and business communication. He has taught at the University of Iowa and has been a visiting faculty at IIM Lucknow, IIM Raipur and IIM Shillong. While his courses are rooted in academic theory and fundamentals, they never let the student lose sight of the real business value of each concept.


The Praxis Advantage

1. Full-time rigorous program

Full-Time program allows the candidates to commit all their time to learn the concepts, applying them to practical real-world problems and engaging in a strong, campus-based analytics ecosystem.

2. First-mover advantage

Praxis started full-time Analytics courses in 2011, and its flagship course in Business Analytics was the first full-time analytics program in the country. Today, Praxis offers the advantages of a deep understanding of the analytics education domain, industry expectations and what it takes to create an effective analytics professional. The institute has forged strong relationships across industry segments that require analytics resources.

3. Robust industry association

Practitioners at Praxis are involved in the design and delivery of the latest in Analytics Education. Industrial tie-ups with ICICI Bank and PwC offers Praxis with specific knowledge support for an updated and relevant course curriculum. On the demand side, Praxis has created strong relationships with organizations seeking trained resources.

4. Comprehensive and topical curriculum delivered by top-class faculty

An experienced, well-qualified and committed faculty team helps Praxis deliver relevant content in collaboration with industry practitioners.

5. The Praxis selection process and placement program

Praxis has put in place a comprehensive placement program that matches candidate’s competence to industry opportunity. A well-above 90% placement rate bears testimony to the success of the program. A corollary of this is the realization that as a responsible institute, Praxis needs to create a fair selection process and select only those aspirants who demonstrate the ability to think analytically and have a passion for problem-solving.

6. Strong and upwardly mobile alumni base

In the last 7 years, Praxis alumni have performed with distinction in the corporate sector and, given the young age of this industry, are already holding pretty senior positions in their respective analytics teams. This network provides immense strength and guidance to succeeding batches, in addition to opening up significant career opportunities for them.


Promoting a Hands-on Environment

Business Analytics & Data Science lie at the intersection of three key disciplines, namely Statistics & Machine Learning, Programming and the targeted Business Domain and the 9-month program at Praxis is designed to address all three in significant depth.

The backbone of analytics is the theory of statistics in general and machine learning in particular and these two key areas are offered as two different subjects. Both these subjects have ‘lab’ sessions where the students use Python to apply the theory. In parallel, students are also taught SAS and R for the implementation of key concepts from statistics and machine learning.

Students are introduced to the principles of Big Data using Hadoop/ Spark and Python where they learn how to migrate and port machine learning applications from their laptops to the Amazon AWS platform so as to understand how to leverage the power of large clusters.

Students are exposed to a set of near real-world projects obtained from a variety of sources. Faculty use data available from Kaggle competitions to create assignments and student solutions are benchmarked against global leaderboards.

Case studies borrowed from the Business Management program of Praxis give students a basic grounding in the principles of horizontal domains like finance and marketing and in vertical areas like retail and telecom so that they can relate to the “business end” of business analytics.

Finally, a comprehensive, practice-based course on business communication enhances the story-boarding skills of the students and prepares them to ‘tell the story’ of the business problem and the consequent analytics solution which is an essential skill all data scientists need today.


Glorious Achievements

•  Ranked 2nd Best Institute by Analytics India Magazine in Full-Time Analytics Courses in India – Ranking 2017

•  Ranked 2nd Best Institute by Analytics Vidhya in Top Business Analytics Programs in India (2015 -2016)

•  Praxis is listed as one of the Top 5 institutes to study analytics by India Today. Dr. Prithwis Mukerjee, the Director of the institute has been featured in the list of top 10 academicians in analytics for 4 consecutive years by Analytics India Magazine

•  Praxis students have performed with distinction at national level hackathons, including

  1. Top 3 finish at MachineHack by Analytics India Magazine among 979 teams.
  2. Top 3 finish in the NASSCOM National Analytics Challenge.
  3. Second among 261 teams in Data Tales – Annual Machine Learning competition of Great Lakes Institute of Management.
  4. 6th position among 1503 teams in Felicity, the annual techno-cultural fest of IIIT Hyderabad.
  5. Praxis students presented their Capstone Project- “What if computers invigilate examinations? “at CYPHER 2018 – the biggest analytics conference in India.


Tackling Industry Challenges

Through regular interactions with recruiters, the institute is of the opinion that the analytics industry is still nascent where the term ‘industry ready’ is not clearly defined. In fact, industries are also not very clear about what they want to do in terms of building an analytics team or outsourcing it from institutes, what skills they seek in their analytics team etc. There’s a lot of uncertainty, and with time it will be stabilized.

Talking about the supply side, the supply side is saddled with a serious shortage of trainers equipped with the knowledge, skills and industry experience required to build the next generation of analytics professionals. Owing to the hype around analytics, a lot of people want to join this industry and build their career without knowing about the skill sets. Even the expectations of the companies are huge. However, there is a need of a filter to pull out the candidates who have the right skill set or training and are capable of adding value to the analytics industry.

Charanpreet thinks that the prospect of the job that is driving so much interest in analytics rather than matching of capabilities and skills is creating little chaos. From the educator’s perspective, Praxis has all kinds of training program, starting from online to classroom programs to equip students with necessary skills, but there’s flux in terms of requirement. Some companies want to create hardcore analytics professionals and some want to create managers who can interpret data well. Expectations are varying, and with time most of it will get steady.


Trends that will Shape the Big Data Industry

The full-stack data scientist that organizations require is superman batman and iron man rolled into one. Today’s rock-star data scientist understands and defines business problems, accesses the right data, subjects it to the right treatment, uses the right techniques, builds amazing models and tells the story. The founder and director of Praxis Business School Foundation, Charanpreet Singh pick four trends that will define the future of a data scientist and analytics industry.

1. The Citizen Data Scientist – Data Science will move from the geek stage to the business stage where the tools become more user-friendly and offer proven algorithms that work in pre-defined use cases. It will steadily become easier to use, with more turnkey solutions for small and medium business, more point and click access, greater automation and more domain expertise embedded in the software. What is seen is the commoditization of the field and further commoditization will happen as the tools get more accessible and easier to use. More and more people and companies will be able to use them and be able to create quite advanced models for analysis and prediction without being experts in statistics or machine learning. Ultimately, the central data science organization will go away and each business unit will have large dedicated data science teams.

2. Specialization – Charanpreet proposes a loose kind of analogy here. Data Science is to statistics and math what engineering is to the physical sciences. And just like the invention of the steam engine and the subsequent industrial revolution applied to the knowledge of sciences to create engineering as a sexy discipline, the computer revolution has created the data scientist, a statistician who can apply the science in a technology ecosystem that enables him/her to do things that were hitherto impossible. Taking the analogy forward, as technologies grew broader and deeper, engineering becomes more and more specialized – today there are not engineers but mechanical engineers, chemical engineers and electrical engineers. Staying with the analogy, Charanpreet expects that the superman data scientist will give life to several forms and specializations across techniques and/ or domains. The data world is adept at coining intelligent names to roles like fraud data scientists, risk data scientists and cybersecurity data scientists just to name a few. There will a data visualizer, the machine learning expert, the AI star in the times to come.

3. Domain – Charanpreet feels that a good number of the algorithms and the platforms would have been done by 2020; the data scientist will move far closer to business than s/he is today. The present-day data scientist is working on tools and techniques that others are not aware of or proficient in. This keeps the typical techie turned data scientist a little distance away from the business. Once some of these algorithms get templatized and menu driven, the focus of the data scientist will shift to the business side of things. The quality of questions asked will determine the quality and relevance of insights drawn and the quality of questions asked will be a function of how deep his/her knowledge of the domain is. Consequently, the world will enter an era of sharp, function or vertical focused applications – for example, developing an application for micro-financing farmers to predict not only capability to repay but also collections over a period of time once the loan is disbursed will be a reality. This will require deep knowledge of farming across crops and geographies and an understanding of all the environmental factors or variables that may impact a farmer’s ability to pay.

4. Data – Charanpreet said, “Deeper domain knowledge and the availability of a much wider source base for data will drive the next round of differentiation. As DJ Patil said in a recent presentation that the internet of things is actually the data of things. The data scientist of 2020 will have to be a data guru not from a technology perspective but from the perspective of understating, identifying and accessing the right sources of data and bringing it all together to enrich the analysis. In the farmer financing example, data on weather and even climate over a period of time will be critical to assess the financial health of a farmer in a particular geographical region, given the crops he grows and the soil he uses.