NTU NBS Specialisation Review – Business Analytics

It’s that time of the year again when decisions have to be made – by the year 1 business peeps regarding their specialisation.

Since my year, there has been 2 new specialisations, Business Analytics and Risk Management. I don’t really have much to offer in terms of information about the Risk Management spec but here’s a previous post about all the other specs including the new Business Analytics: CLICK HERE!

I think there’s a certain responsibility to shed light on what exactly is it like to be part of the pioneer (or caught in between :/) batch of Business Analytics graduates. Some parts of this review were done with advice from profs and my batch mates but note that it is still exclusively my opinion so take it with a pinch of salt yeah. Visit the BA Facebook page here too.

For the sake of the freshies reading this, here’s the most probable breakdown of the BA modules so far. Some core modules like Business Analytics Consulting have not been cleared for core status so there’s only 3 compulsory core modules so far.

 

BC2402 Designing and Developing Databases: 

Probably the module with the most relevance to what you learn in IT 1, with background knowledge of how public and private keys work in a public key infrastructure (basically how digital security works) and Entity-Relationship Modelling being a great help here.

Important concepts:

• Structure of databases
• Extracting data with SQL (Structured Query Language)
• How to model a database
• How to write use cases for defining the steps to achieve a task (like sending an email)

Project: In my time (sigh this is making me feel old) we had to find an actual client to model and build a database for. Not to worry as you will probably be grouped with Biz Computer Engineering (BCG) peeps who have more technical knowledge to develop the database. This baptism of fire will teach you almost all you need for the module.

Relevance: This is still a very relevant module and can lead to direct employment. Knowing how to write use cases is a key skill in being a Business Analyst, for example in banks where you need to interpret business requirements (eg. for a new App) into technical requirements for developers. Knowing how databases work is also crucial in manipulating and extracting the data that you need in future to do the actual analysis.

BC2406 Analytics I: Visual and Predictive Techniques:

Disclaimer: After feedback from the first batch, much has been changed in this module so I’ll touch on what I know but not experienced myself.

Here we started learning about the bread and butter of a Business Analyst: Defining and Tackling the business problem.

Important concepts:

• Data exploration
• Data cleaning
• Data preparation
• Data visualization
• Economic Analysis (profit loss)
• Predictive techniques (Decision Tree and Linear Regression)

Relevance: Not that much yet, but what is being taught here such as data cleaning and exploration are the bread and butter skills every data scientist or analyst will need to know. I heard that this module will be taught with R so that’s very good as R is one of the most demanded data analytics skill in the market (http://www.fastcompany.com/3030063/why-the-r-programming-language-is-good-for-business). Being able to experience using it will be a HUGE plus on your resume.

BC2407 Analytics II: Advanced Predictive Techniques:

It is going to be hard to remain neutral about this module but its perhaps the most important module I’ve taken in my 6 semesters here.

Most of the interviews I have been to (Banks, tech startups, eCommerce, Consulting firms, marketing roles) have revolved around content and skills I learnt from this module.

The prof (prof kim huat) is one of the most experienced practitioner in the field, provided you ask the right questions. The only gripe I have with this module is the use of SAS, which may not be used as much by corporate firms due to its cost. Still, its a platform that is quite friendly to use and learn from.

Important concepts:

• Cluster Analysis
• Association Rules
• Logistic Regression
• Neural Networks
• Text Mining

I don’t even need to go into details regarding these concepts because these are the meaty parts of the BA spec. It’s these content and hard skills that allow you to confidently tell your interviewer that you are able to generate whatever type of business insights with these analysis techniques.

These techniques are basically the basic version of what real world analytics such as how Google knows where you are going and what you are doing can do, and how banks are able to predict who will default on their bank loans or not. These are really powerful examples you can describe to a future employer that raises your employability.

Relevance: Very relevant. I have found myself using the content I learnt in this module in my technical interviews. Even for some of the technical interviews that requires Excel or VBA work, all the time spent playing around with data would have well prepared you for it.

Other modules:

BC3401 Enterprise Processes & Analytics: This module revolves around SAP’s Enterprise Resource Planning platform, basically allowing you to get a good idea of logistic systems and inventory planning etc. Some peeps go on to take the TERP10 certification after this module, which paves the way for a potential career as a SAP consultant. Quite a good module in my opinion as you learn more about high level processes in large MNCs. The project is a challenge and it will be wise to even collaborate with other teams.

BC3403 Social Media & Digital Analytics: Be warned, this module might fall on a Saturday morning as the prof may not be from NTU. Nevertheless, I honestly did not learn much from this module as most of the content can be picked up from a digital marketing module. Social media analytics concepts can also be picked up from a Google Analytics certification course (https://support.google.com/partners/answer/6089738?hl=en).

BA Culture:

I can’t speak much of the official new BA batch but I was part of the last batch of the Business IT cohort before it became Business Analytics. Being part of a small cohort (BA will have around 50, not including the Computer Engineering peeps and second spec) will definitely mean that most of you are going to know each other at some point of time. For me, that was something I only appreciated after getting into the spec. Some of the projects we faced were really hard and sometimes we resorted to collaborating with many other teams just to complete the project.

I remember one of the profs chiding us after a project submission, telling us that the greatest mistake we made was not collaborating with the other teams to solve the business problem. It spoke a lot about the culture among tech people, and how we can only go far if we go together.

If you think such airy fairy qualities such as “collaborative culture” ain’t any use for your career, please think again. Interviewers have asked again and again culture fit questions such as “What kind of working style do you adopt” and having a concrete example can be really convincing. There’s hardly any large firm out there that does not work on a collaborative basis.

There’s not much of a competitive streak among us but it’s more like a matter of professional pride as you either know your shit or you don’t. Add that to the collaborative culture and I find myself seeing a lot of examples of knowledge sharing going on. We share our notes and even our technical portfolios as we frequently point out points to improve on and level one another up.

Employability / Career Prospects:

I could not talk much of this a year ago but now that I see my batch mates graduating and finding jobs in this job climate, I daresay that being in BA gives you a competitive advantage.

Firstly, BA value adds to practically anything. Insights driven marketing is used for marketing (Advertising Tech, targeted digital marketing such as Google Adwords and Facebook Exchange). I have a friend launching a data analytics suite for HR using machine learning to determine succession planning. The last big thing in banking was the use of tools like R for financial modelling and the next big thing revolves around Fintech (blockchains and payments) and data analytics for fraud detection and targeted marketing.

Secondly, BA is a very dynamic field. Don’t expect to go far staying in your comfort zone learning only the things you have to to pass the modules. The best thing about BA is the availability and ease of picking up these other skills such as data visualization platforms like Qlikview and Tableu, and data storage platforms such as Hadoop, to programming languages such as R and Python. Adding these skills to your resume or LinkedIn profile really value adds a lot to you as a future employee, provided you really know your stuff.

Lastly, there’s already concrete proof in the prospects of being in BA. Our batch has gained employment in banks, FMCGs, consulting firms, public sector agencies and even a training program with Google.