Cognitir Webinar No. 1 – Data Science for Finance (Q&A)

Hi everyone,

We had over 1000 attendees during our very first webinar series. Data Science for Finance was a success – partly because we have received so many interesting questions before, during, and after the webinar sessions. Please find a selection of participant questions and our answers below. You can watch a recording of our Data Science for Finance webinar here.


During the webinar, you mentioned that traditional investment managers and quant firms are using data to craft investment management strategies. How exactly are they doing this if this is new and they don’t have data to work with?

This is an interesting question. They do have data in the investment management industry – in fact a great deal of data. The point we were making is that the level of data analytics on past data has been limited thus it is an underutilized investing asset. We are saying that investment managers, in today’s world, are starting to see the predictive power of data and are working hard to integrate it into current portfolio strategies. Also, keep in mind that in 2008, the UK set up Auto-Enrollment as part of the UK Pensions Act. The purpose of this legislation is to get workers to start investing in preparation for their retirements. As part of this scheme, the AE started collecting heaps of data that can be used by investment managers in construction of future portfolios as the data ranges from custodian to specific sentiment data on the investors themselves.

In your experience, what are the biggest hurdles for financial companies when it comes to big data?

Many (especially large) financial companies deal with scattered data lying in silos across various teams. One of the main obstacles is to remove such data silos and dependency on legacy systems to integrate information from different data sources more easily. In addition, analytics teams do not seem to be structured very efficiently within organizations. It has been shown that centralized data analytics teams and/or business units are significantly more effective than scattered pockets of teams or decentralized units spread across the organization. Lastly, organizations should further optimize internal workflows to support data-driven decision making, managers need to understand/study key concepts around data analytics, and the right analytical talent needs to be recruited and retained.

Do you view Data-Driven Decision Making as being adversarial or complementary to experiences and intuition? What is the role of hunches and/or creativity (envisioning something totally new) in the decision making process?

Data-Driven Decision Making (DDD) can supplement experience and intuition with objective information. Used together with experience and intuition, DDD helps decision makers become “data-informed”.

Hunches will always play a role in decision-making because models do not include every exogenous factor and some decisions need to account for “qualitative elements”. So, while a model might help illuminate a “better” decision based purely on quantitative features, is it the best decision for all stakeholders? It really depends. Humans possess the ability to reason and “see the bigger” picture. Even as artificial intelligence and machine learning continue to become more powerful and play more important roles in future technologies, it is unlikely that all decisions can be made purely by computer programs and algorithms in the near future. Experiences and intuition will always play some role in the decision-making process – to account for the factors these programs and algorithms are not taking into consideration or cannot be quantified.

Creativity is also a core component of decision-making because it is the catalyst for solution generation. Many times complex business problems need an “out of the box” solution that is more easily generated by creative approaches. This is why many corporations have retreats. Getting away from the office and brainstorming in new and relaxing environments can get the creative juices going. Thus, creativity is an essential part of the decision making process now and will continue to be in the future.

How do you think companies will handle issues around data privacy?

Data privacy is both an internal and external problem for corporations. Internally, many different teams want to use the same datasets because they can provide different insights that support various business functions. In the past, the privacy was less protected because there were only few technologies in place to limit access to these datasets. Now, data management systems are more sophisticated to give access to certain teams and individuals. This can be further supported by data mapping and having a centralized system managed by a Chief Data/Information Officer. By having a “gate keeper”, only those that need certain data types can obtain access thus preventing internal data privacy breaches.

Externally, corporations collect data from various mediums (e.g., websites, online applications, credit card purchases). This data is often used to assess what additional products and services to offer customers. It will be hard to prevent corporations from continuing this practice as the data is often necessary to complete purchases. However, governments are starting to impose stricter data privacy laws to ensure that consumer data are better protected.

Will this have the outcome that consumers want? Time will tell. But, it is important to realize that more powerful technology is a double-edged sword. With more powerful technologies comes increased data collection capabilities and responsibilities. It’s clearly a predicament.

What are the pros and cons of Python and R with regards to data analytics? From the perspective of somebody with no/minimal programming background, which would be better to learn first?

Both Python and R are amongst the most popular languages for data analysis, and both have their supporters and opponents. Python is a general-purpose programming language which means that, in addition to performing data analyses, you can use it to automate tasks with scripts or build powerful web applications. R has been designed for statisticians so its core functionalities focus on user friendly statistics. Both programming languages feature powerful libraries that extend their core capabilities and enable deep analytics with only little time investment.

Python code is known for readability and simplicity which makes it enjoyable to learn. Our course participants have told us that they find the learning curve relatively low and gradual, even without prior programming experience. R’s initial learning curve is known to be a bit steeper.

What are salary ranges for an experienced finance professional that adds solid data skills?

This is a good question which depends on several factors, including but not limited to relevant qualifications, geographic location, and the company you want to work for. This discussion may provide some ideas.

Are there any books you recommend about the field of data science?

Books that we have enjoyed reading include:

Python for Data Analysis by Wes McKinney
Data Science from Scratch by Joel Grus
The Signal and and the Noise by Nate Silver
Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman
Big data: The next frontier for innovation, competition, and productivity by McKinsey (report)

In addition to these, we recommend our Cognitorials, short videos that have been designed for busy professionals and students who want efficient ways to learn complex data science concepts.

On the last slide, you said that there will be a shortage of talent that is prepared to work with data. What can be done to ensure you are prepared to take advantage of job opportunities in the future?

Keep learning! We believe that individuals who want to seek such opportunities have to understand the possibilities of data analytics in a business/organizational context. This includes obtaining basic knowledge of computer programming principles and an understanding of how computer programming can be used to perform effective data analyses.

We are living in a knowledge world. Those who advance quickly do so because they are lifelong learners. It is important to never remain idle or “too comfortable.” Because technology is fundamentally changing many traditional roles, it is critical to recognize that technology will replace certain tasks and that roles will require new skill sets. Currently, data analytics skills are prized in the corporate world because of the dearth of talent. However, salaries are often a function of supply and demand which means that this dearth will go away and salaries will even out.

But, new technologies will emerge and professionals will need to learn skills to capitalize on them. So, the ability and desire to quickly learn is critical to remaining competitive now and in the future.

How can I launch a data-driven career in FinTech starting from scratch?

The key thing here is to develop knowledge in two areas: data science and finance. We believe the best way to build your data science knowledge base is to first take an intensive data science bootcamp (1-3 day options are best in our opinion) and then practice the skills on relevant datasets. The reason for this is because most learning will not happen in the bootcamp itself. It will happen when you practice applying the taught concepts on various datasets and problems. The age old adage – practice makes perfect – is completely true even for building data science and most other technical skills .

Taking into consideration time and monetary investment, we believe that CFA or CAIA programs are the best ways to quickly and economically build finance knowledge bases. The knowledge obtain should be ample for most data-driven FinTech careers.

What are the best places to start learning more about data science?

Check out the book resources above, our Cognitorials, and our courses. 🙂

How can organisations such as banks access data from social media?

All major social media platforms offer something called Application Programming Interfaces (APIs). Such APIs can be used to access data from social media using different programming languages (e.g., Python, Java, R). Think of them as bidirectional communication channels. For example, you can use the Twitter API to request tweets that contain a specific hashtag. Twitter will then return this data to you in a structured format which you can use for further processing.

Do you know successful companies that make scoring models for SME lending in Europe or US (or maybe Asia) based on big data? What kind of data/how many inputs  they use?

Sure! Below are just two companies:

Bigstone (based in Australia)
LendingKart (based in India)

There are quite a few players in the P2P and SME lending spaces. They all have different credit scoring algorithms with various amounts and types of data points. Ultimately, these data points are the foundation of each business’ IP. So, detailed breakdowns of these algorithms will generally not be made public. But, you can count on social media, employment history, academic history data points to be part of these algorithms.  

How useful do you think the skills that you taught us on the webinar will be to a student getting a full-time MBA?

Most of our MBA course participants find that obtaining these skills is extremely valuable for their careers.

First, MBA students can improve their business problem solving and creativity skills. Data science in business is applied problem solving in the context of business problems. It allows course graduates to see problems from different angles given the ocean of available data therefore enhancing creative-problem solving strategies.

Furthermore, MBAs with fundamental data science skills can understand what types of data are actually useful for companies, what good data analytics looks like, where data analytics can specifically add value to businesses, and which business questions data science can and cannot answer.

MBAs can benefit from learning how to “speak tech” to be able to better communicate with technical teams and work collaboratively with them on achieving company goals. Having practical data science and programming experiences will allow business managers to have more productive and meaningful conversations with their company’s technical teams.

In conclusion, learning data science skills is a high ROI investment for individuals given the plethora of benefits received versus the low monetary and time costs of obtaining such skills.

The 2016 Summer Olympics May Have Ended, But The Cognitir Webinar Series Is Just Starting!


Cognitir just started a new initiative – Cognitir Webinar Series. These one-hour, free webinars will cover a range of technical topics including data science, computer programming, and web development. Business, finance, and nonbusiness professionals as well as students will benefit from these webinars.

Cognitir held its first webinar in the series – Data Science for Finance – earlier this month. It was a massive success! Over 1,500 people signed up from more than 36 different countries! The company held this webinar in partnership with over 40 CFA Societies around the world.

The webinar highlights novel data science use cases in finance, economics, and fintech. You can access the webinar video recording here.

If you would like to stay informed of future webinars, please follow us on Facebook, Twitter, and LinkedIn.

Many thanks!

Interview with Mai-Gee Hum, Director of Career Management Services at the John Molson School of Business

Import Classes interviewed Mai-Gee Hum, Director of Career Management Services at Corcordia University’s John Molson School of Business (JMSB). After speaking with Mai, we learned first-hand about recent hiring trends and specific skills that employers are looking for during resume selection and recruiting. We hope that other Career Center Directors and students enjoy this interview!

Tell us about yourself. What is your background and role at JMSB – Concordia? How did you choose to join JMSB’s Career Management Services?

My role at JMSB is Director of Career Management Services. My job is to help students find jobs. I provide them with the tools and preparation to become the most competitive applicants that they can be in recruiting processes.

JMSB works exclusively with business school students and alumni. We offer training solutions, job postings, networking opportunities, and industry resources so students and alumni can craft their best applications for employers.

I have been at the business school for nine years now. I first started out in graduate school recruitment and was responsible for recruiting prospective students into our MBA program. I then went on to work for the Dean as Communications Officer. In this role, I produced content that was used by students, in faculty research, and by our media relations team. I got to witness great things happening at school. After this, I entered my current role as Director of Career Management Services.

My career post grad school has always been in education. I first started out at Kumon, which is a math and reading tutoring company. I did business development for them. At some point in my career there, I reached a plateau, and it happened to be that JMSB was looking for a Graduate Student Recruiter. Once I met my colleagues and the students, I knew immediately that the job was a great fit, and I decided to join JMSB. Working at JMSB has been very rewarding – there is not a day that I do not want to go to work. It is a stimulating and motivating experience, and given the steady growth and diversity in employment placements over the years, I recognize there is so much potential here.

What are the main types of recruiters at JMSB? What types of jobs are JMSB students most interested in?

Really all industries recruit at JMSB because they all need business students to some degree. The main types of recruiters are financial institutions and accounting firms. Most of our students go into accounting, so accounting firms are by and large the most active recruiters. But, we have quite a few companies that hire for marketing, management, business technology, and human resources roles too.

JMSB students are very motivated and usually go after the most coveted jobs – think investment banking. Given there are lots of students interested and so few positions available, only a very few select students get in. So, we also place lots of students in corporate finance roles – not necessarily investment banking but finance roles within companies, etc.

In terms of other functional roles, JMSB students are interested in human resource coordinator/technician jobs, marketing/account manager and analyst roles, etc.

How do you determine priorities for the JMSB Career Center each year? What drives these priorities? Do you find that these priorities change in a given year?

Our Career Center priorities are very cyclical. For consulting firms, banks, and CPG companies, hiring cycles are cyclical which is why our process tends to be cyclical. Our top priority is maintaining a high level of standard of company coming to campus. We are always making sure that posted opportunities are suitable for undergrads. And, for our graduate students, we try to find opportunities that can utilize their MBA learnings and pre-MBA experiences.

The main driver of this top priority is forging and building meaningful relationships with employers. We want them to think: “Ah, I want to come to JMSB to recruit talent because JMSB is a talent destination.” In order to do this, we must make sure our students are as prepared as they can be so they can perform at their highest levels during the competitive recruitment and selection processes. This helps facilitate the relationship building.

In terms of priorities changing, I would say sometimes, but more often than not – no, the priorities do not change drastically. Business school hiring patterns are generally the same. However, an emerging trend is that students are getting and needing more technical skills to successfully land top jobs. Implementing new course content takes a great deal of time – lots of bureaucracy within the school and even at the government level, so getting external training helps close the skills gap quickly and effectively while not costing much.

Given your detailed conversations with HR and business/finance professionals at top companies worldwide, what have you learned about the types of skills that employers are looking for in students for internships and full time jobs in the recruitment process? Why?

Employers are looking for students with technical skills such as programming and other semi-technical skills such as SEO proficiency for marketing students. I have noticed that there is a sudden hunger for students with technical proficiency – even just a basic understanding of programming.

Other critical skills that employers are looking for include communication skills and high degrees of emotional intelligence. Employers want students who can effectively convey findings and information to customers and senior managers. This is true for both internships and full time positions.

To summarize, I would say that employers are looking for a balanced blend of soft and hard skills. They want the whole package, and given how competitive recruiting is becoming, they can demand this.

What do you think will be the most important skills for students in the next five years or so? Why?

Technical skills. Coding, programming, SEO, just to name a few of them.  Communication skills (written and spoken) will also continue to be very important.

I also think that employers will place an emphasis on finding students who are truly global thinkers. They want students who are adaptable and malleable in different contexts given that the economy is global. Students should be able to convey information to other parts of the world with no problems. If students have a chance to do a term abroad, I would highly recommend it as I think the experience would be a huge asset to their careers. Awareness and mindfulness of how other people do things are very valuable skills to employers.

Why did you decide to retain Import Classes for data science training for JMSB students?

The instructors!  I knew Neal as I had worked with him before, and I had complete confidence in him and his cofounder to create a great course and supply knowledgeable and patient instructors.

My expectations were exceeded in terms of the teaching and content.

I also knew, having talked to numerous HR and business professionals, that the learnings from the course would be very relevant; they would help students become more competitive in the job market.

Given that you decided to take the two-day Introduction to Data Science class, what did you enjoy most about the course?

I had no experience in programming or coding, but the course was broken down effectively into manageable chunks. We were together for two days, but it was structured in a way that the learning was completely manageable. It was challenging and intense, but I really enjoyed the entire experience.

Did any students share feedback on their experience taking Introduction to Data Science?

The student feedback was very positive. We had a sixteen year old son of a faculty member in Supply Chain Management. This professor saw the value of his son taking the course even at age sixteen!

And, having discussed the course with employers, the feedback has been super positive too in terms of their desire to recruit students with these skills.

Essentially, the course has both the support of the Career Center and employers.

Is there anything else that you would like to add that you think could be helpful to other Career Center Directors out there?

I would recommend my Career Center colleagues at different schools to always pulse check their students – student feedback is usually spot on. They are the most in tune with what recruiters want as they themselves are in recruiting processes. If you are unsure about a specific training or development opportunity, ask them: “if you had this course or opportunity do you think it would help you? Why?” kinds of questions.

In addition, I would constantly interact with recruiters and employers to find out about their specific needs and confirm this with job postings of similar positions as these positions will often list required skills. Eventually, you will start to see recurring patterns, which can help drive how you can best support your students from a resources standpoint.

If you are a professional working in a career center, what have you learned about current hiring needs?  Do you have any advice to give your colleagues at other schools?

We LOVE debates! We recognize that many data science and programming topics draw a myriad of opinions. Please share any experiences, comments, or questions you may have below!