Make a career in Analytics

Print and social media as abuzz with advertisements about courses in Analytics. All professional colleges have updated their curricula to include courses on Analytics.

Every software product that is out in the market claims they have built in analytics. Analytics is here to stay. It is not a specific field but a capability that extends the function of any organization, product and process.

But when viewed from the eyes of students of technical education such as engineering, Analytics is an area of career and is booming hot with career opportunities.

Let's explore how one can fully understand the landscape and make a career choice to enter this area after their college.

While data is defined as the "facts and statistics collected together for reference or analysis", Analytics is information resulting from the systematic analysis of data or statistics.

Data represents the raw facts and Analytics represents the insights derived from these raw facts using various methods. Due to advances in technology, capturing of raw data multiplied exponentially resulting in mines of data.

This data when analyzed using various mathematical and statistical analyses produce several insights based on which efficient decisions can be made that are backed by data. To illustrate this in a simple example, a list of names and demographic information gathered during census is raw data.

Analyzing data and then quoting that certain state has five million female population between the age groups of 18-25 is information. When such information is further analyzed to answer something like how many of this cluster are likely to opt for higher education is applying analytics on data.

Analytics basically come in three forms. Descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analytics are insights depicting what has already happened. Such as, revenue of a company in current year versus that of last year by region is an example of descriptive analytics.

While predictive analytics are insights that give indications of what is likely to happen in future given the current data and past history. When a state goes into election, everyone around us start to make predictions on who is likely to win based on gut or intuition.

If the same thing is done based on scientific analysis of data, then it is an application of Predictive analytics. In this example, given historical data and demographic make-up of a certain constituency, an algorithm predicts with certain accuracy how a constituency will behave.

Once there are some actionable insights provided by employing both descriptive and predictive, a decision can be made based on these decisions. However, based on several parameters, if a system makes recommendations, that is prescriptive analytics. While descriptive represents what has happened, predictive represents what is likely to happen and prescriptive provides what can be done about it.

Employing analytics can aid almost every decision that is made in organizations. Applications vary broadly depending on the domain of the organization and a given context. If there is a significant drop in revenue, analytics if used for diagnosis can yield a reason.

The possibility of a particular financial transaction being fraudulent can be predicted. But one must understand that Analytics is not a tool that gets employed just before decision making.

To build this capability, a company sets out on an Analytics maturity journey where all points of data ingestion are analyzed and data that is required for decision making is tagged for further processing.

This is an ongoing process and cannot be done on an ad-hoc basis and discarded when not required. These same concepts when applied on data is voluminous, varied and getting recorded rapidly can be referred to as the Big data Analytics.

Data when it meets these three criterion is classified as big data and analytics applied on these are referred to as Big data Analytics.

Big data analytics landscape offers several career opportunities for students of professional technical education. Ranging from infrastructure to product development there are hundreds of skills employed in implementing an analytics solution.

Students should first assess their strengths and choose a track that is programming oriented or data analysis oriented. Irrespective of which track one chooses, some basic skills required for a career in analytics are mathematics, statistics, basic programming and data analysis, with emphasis on either programming or data analysis depending on one's strength. All these four basic skills required can be acquired while at college and more advanced skills can be acquired through external sources.

Apart from popular MOOCs offered by Udemy and Coursera, several top management institutes such as ISB and other IIMs also offer in class training programs spanning from few months up to year. Along with these, there are several online training providers who have come up with certificate programs in this area.

While the choices are many, one needs to note that, most of these courses can be pursued while working full time.

Although, getting trained and certified is a laborious task, companies value those who have applied and produced certain results than people who just got trained.

This is where, one can innovate to gain some experience in applying what they have learnt and build a more professional profile. Explore analytics challenges posted in sites like Kaggle and participate in hackathons.

Leverage open data published by several government bodies and derive insights that might interest someone or that might just present a whole different perspective of the underlying data.

All in all, there are as many options to build a successful profile in analytics area as the opportunities are and it is just your resolve and initiative to crack these opportunities.

By: Sharat Konatham

(The writer is an entrepreneur and ISB alumni who runs Thoughtwise, a career counselling service. He can be reached at [email protected])