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Career Opportunities in Data Science & Advanced Analytics in an Enterprise

The world has been changing so dramatically in the 21st century that it’s difficult to keep up with the latest buzz. But, more important than the buzz, especially for young people who have to make key decisions that would affect one for the rest of one’s life, is, what to study, which institutions to go to, how much can I earn, will my strengths be utilised, will I be happier than everyone else, when will I be able to afford a Ferrari? Will I be able to do that European Contiki Tour before I marry?

And the questions get trickier by the day. It’s good to dream and have a vision of where you would like to be in 10, 20, 30, 40 years from now! Yup, the decisions we make now affect our lives even 40 years later guys!!! Just 30 years ago many young people learnt to be fast and accurate typists in order to get the best jobs; can you believe now that people thought that it was so important then?? Ask your folks, they would remember this! The world, friends, has changed.

In this constantly changing world, one thing is starkly apparent; technology is ruling the roost in life. Almost our entire life is permeated by technology – online shopping, fitness watches, apps for every conceivable use on our smartphones, online higher education as well as K12 courses, availability of instant information on search engines, terabytes of music and videos for entertainment, specifically tailored instant news feeds, prepare food by following a video, driverless cars, maths solutions online, gaming, the ubiquitous social media, drones that deliver our pizzas, google maps, e-books, on-demand tv, app-based taxi services, live streaming of puja being done at home and family members living overseas able to join the live webcast, GPS, instant global communications, tele-medicine, hyperloop distance travel

Do you know ? : 

Virgin Hyperloop has already concluded the initial agreement to build India’s first hyperloop system between Mumbai and Pune

(Mumbai-Pune hyperloop project)
Even in rural parts of the world, technology has made big strides, one can even get global positioning or real-time communications with satellite phones from the remotest parts of the world. Stands to reason, then, that technology is here to stay – perhaps there may be greater advances in technologies down the line (how far away is 6G connectivity?) but technology is the inevitable way of life of future generations guys. On 22 April 2020 Facebook and other A-List American investors, subsequently over the last month, invested $9 Billion (USD) in JioPlatform these investment sharpies know a bit more than most of us about where to invest – they obviously think that Mr Ambani and his JioPlatform is going to be the next Amazon or Alibaba – a world giant e-commerce platform! I think you know that Data Science is the very essence of e-commerce and Indian Hypermarket; I cannot imagine the number of Data related jobs that will be created by JioPlatform in the next few years!

So, back to my first question: Can we use Data Science and/or advanced analytics to predict the future? While the smart tools contain very good predictive and prescriptive models to facilitate cogent decision making for companies and organisations, however, there are no known algorithms or tools to predict the future itself. One thing though, if you are not looking at a technology related career, who knows, you could end up as the typists of old did!

Industry 4.0 and Data Science

They say that the 4th industrial revolution is underway; this means that in the next 10 to 20 years manufacturing workplaces will be a fully automated, smart environment; there will be very few manual workers, but an army of technologists and engineers walking around the factory floor; and an army of IT professionals and Data Analysts controlling everything from operations, to logistics, to maintenance schedules, to planning, to financial management, etc. Yes, you read right, even accounting functions would largely be a function of IT and Data specialists. In industry jargon we speak of ‘domain expertise’, which refers to the need for Data Scientists to have consummate knowledge of the company’s business (or the clients’ business in the case of specialist Data consultants like Aptus Data Labs; refer to – this writer boldly predicts that professional Data specialists with good knowledge in functional domains such as Accounting, Operations, Quality, Planning, Marketing, etc, will form the bulk of the workforce in most organisations in the not so distant future, such will be the reach and importance of technology and Data Science in EVERY organisation.

Data Science is a relatively new branch of IT. American business strategists led the way in discovering the value of old data and the mountains of unstructured data from servers, emails, social media and other sources to provide new insights if looked at using different mathematical and statistical algorithms, but it wasn’t called data science then, the early gurus spoke of data mining. They diced and sliced this data using an array of operations and statistical techniques to process the various combinations of data, and, in the process, uncovered thrilling new opportunities for businesses to grow. The new insights uncovered quickly became a specialist field and businesses wanted more of this new specialist people. IT guys dusted their maths and stats textbooks to re-learn many of the quantitative techniques that they hadn’t used after university1. In no time at all OEM software manufacturers began developing specialised tools to carry out all the work from deep mining of data from different sources, sifting salient data from ‘noise’, and included the mathematical and statistical functions to do the calculations, produce visual views of the resultant insights, and several other functions that were the bread and butter of data gurus. Over the last 6 to 7 years data mining and analyses have evolved into what we now call Big Data Analytics or Data Science. Data Science came into existence as a niche branch of Information Technologies. Separately other new technologies like Artificial Intelligence, Cloud Computing, IoT etc, also came into existence. These days, the combined use of Data Science with these new technologies is transforming organisational growth, effectiveness, efficiency, and reach, such that no organisation can afford to carry on without this service; even governments are now using Big Data Analytics! Speaking of governments, elections have also become the ‘playground’ of Data Scientists; political parties employ an army of data science specialists these days to try and eke out a win at the hustings! The 4th Industrial Revolution? It was the German National Academy of Science and Engineering that used the term Industrie 4.0, in 2011, to describe operational workplaces of the future; it has become the mantra of industrial strategists and gurus all over the world.

Career Opportunities in Data Science and Bid Data Analytics

As the amount of work grew for the analytics people, Big Data Analytics quickly devolved into several sub-specialisation fields; it became efficient to break up the large range of work into cogent sub-functions – which has now become several NEW jobs within Data Science – jobs and careers that suit one’s temperament and unique abilities.

The following data related careers are now offered by enterprises :

Brief Job Description : Exploratory data analyses; use diff tools; initial visualisation reports. Many entry-level professionals in data-related jobs start off as Data Analysts. Good knowledge of query languages like SQL, some knowledge of leading BI tools, understand data handling, modeling and reporting techniques, a strong understanding of the business along with some technical skills (Python and R) gives one an edge to become a Data Analyst.

Brief Job Description : Before ‘playing’ the data to give new insights, data has to be properly organised. Gathering of data from various sources, including social media and HDDs, videos, and other unstructured data is the work of the Data Engineer; Creation of a Data Lake – structuring a DWH; use of ETL, Hadoop, SQL, NoSQL, and other skills including important programming languages like R and Python. A good knowledge of Cloud platforms like AWS, Azure etc is also expected of the incumbent. Usually, a Data Engineer acquires a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

Brief Job Description : This is a senior post within the Analytics environment. A data scientist will have a mix of software engineering, strong statistical analysis, strong machine learning and business analytics skills. This post requires one to have the ability to create and optimise models; apply machine learning and / or deep learning models; apply other smart technologies like AI where warranted; use different sets of algorithms and quantitative techniques for alternate outcomes; the Data Scientist is expected to have some idea about the deployment of ML models.

Brief Job Description : Machine learning engineers normally focus on the implementation and deployment of the models. They are also expected to be good with Data Structures and Algorithms. Expertise in statistics is not expected from ML engineers but should have a moderate idea about statistical analysis. ML engineers are less focused on the insights you get from the models but more on the productionalization and application of it.

Brief Job Description : Deep Learning engineers are similar to Machine Learning Engineers but with strong expertise in Deep Learning modeling, the building of training and inference pipeline on distributed GPU environment and deployment into production.

Brief Job Description : Domain expertise is often mentioned in Data Analytics circles, this is becoming more important in order to maximize the value of data intelligence. The BI Analyst uses the insights obtained to assist other departments and functions within an organisation to benefit from the data exercises, however the BI Analyst has to be very familiar with the complete range of business activities of the organisation to carry out this function successfully. The cross pollination of duties, activities and roles within the broad Analytics environment is crystallizing into roles with sharp focus on modular concepts

Brief Job Description : This specialisation within Data Analytics is getting more important as marketing efforts, especially, relies more on data insights. The ability to present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence has become as important as applying sciences to obtain insights from data

It is important to note that Data Science students at AptusLearn are taught the full array of skills and knowledge to become fully-fledged Data Scientists. It is common knowledge, however, that not every fresh graduate will be employed at the highest levels immediately upon graduation. Even if one starts at a lower end in an Analytics environment, one does have peace of mind that the AptusLearn ( courses train one properly to quickly take on higher-level roles in a company. As one exhibits sharpness in handling different quantitative techniques, smart skills, the ability to produce eye-catching work, and the knowledge of handling the various smart data tools and languages, decent upward mobility is assured in the corporate world! Have a look at the following graphic produced by AptusLearn to show a typical data analytics environment and the various jobs and functions in a progressive Data Analytics Department (this schematic is actually characteristic of the Aptus Data Labs workplace):

The above schematic demonstrates the cross-functional nature of a typical Analytics environment. Dear reader, if time does not allow you to properly understand this schematic, don’t worry I will unpack it in a future blog shortly – watch this space!

The million dollar question is:

Are you brave enough to walk away from technology that defines future workplaces ?

Okay, so you have just completed your accounting or engineering or management degree; why don’t you do yourself a great favour, for your own future, mind you, and add some flavour to your degree with a Data Science qualification – after all, it’s your life, success, and future prosperity and security that’s at stake! AptusLearn offers various courses to both full time and working professionals, in the form of professional certificates, PG Diploma courses and corporate training in Data Science and Big Data Analytics. Arm yourself with the best weapons to take on this unpredictable world.

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