Emerging career opportunities in machine learning & data science

The terms machine learning and data science are often used interchangeably. However, they are not the same. machine learning is a subset of data science which specifically deals in finding trends and patterns in vast volumes of structured and unstructured data using statistical techniques to teach machines and systems to act implicitly on stimulus rather than extensively coding all the business logics.

Both machine Learning and data science are emerging fields of study with LinkedIn job trends claiming these two to be the most lucrative and prospective fields amongst different professional fields practised across the globe.

Therefore, if you want to make your career as a data scientist then sign up for a data science course to get a head start in the field of data science.

What is Machine Learning?

Traditionally, the business layer of applications was programmed using different coding frameworks and languages which used to explicitly define the working principles of machines and systems. The scalability of such models has been questioned in current times due to the rapid outbreak of petabytes of data in transactional and storage systems of business. More so, the increasing complexity of the business environment, has made it exceedingly difficult to simulate all variables and situations via software codes. This is how the concept of machine learning spawned.

Machine Learning is a technology by which systems and machines are trained to take self-action basis statistical and mathematical algorithms to find out valuable trends and patterns in the training data – which are then applied on the future inputs to generate self-invoked response.

Impact of Machine Learning on Indian businesses

“Artificial Intelligence, Machine learning can be as big as $177 billion IT services industry” (Economic Times) speaks volume on the massive impact and future prospects of machine learning in today’s business. The figure below shows the increasing interest in three of the most trending field of research – artificial intelligence, machine learning & data science with machine leading the three.

The industry-wise segregation of usage of Machine Learning and Artificial Intelligence in various industries are as shown below.

Demand & salaries of machine learning engineers

As shown in the figure above, the banking and financial services sector are the highest proponents of machine learning concepts and technologies, followed by other industries such as e-commerce, healthcare, telecom etc. A general trend which has been observed in industries is that more leveraged the data structures and information system of a particular industry, more the inclination towards involving machine learning and data science projects and implementations into the delivery mechanism.

The demand for machine learning is majorly fuelled by the increasingly positive sentiments of these concepts and implementations in businesses from across the globe. More the investment that businesses make in the field of data science and machine learning; higher would be the demand of experienced professionals; and the same trend would hold true for salaries as well.

The above chart clearly shows that job opportunities in the field of machine learning and data science has grown the maximum; with machine learning growing by 9.8 times and data science increased by 6.5 times.

The typical salary structure of a machine learning engineer ranges somewhere around $200,000; with the base salary extending to $116K while the remaining amount is attributed to annual equity and signing bonus.

However, despite the positive prospects and promising future in the field of machine learning and data science; freshers and starters in this field must be cognizant of the fact that there are fair amount of drop-outs in the field as the road to becoming an established and successful machine learning engineer is a long one. Hence, aspiring candidates must be self-motivated and patient enough in this field to reach the top.

How machine learning is contributing to business growth

The different facets or yardsticks wherein machine learning is contributing to the overall business growth are mentioned below:

Driving simplicity & automation – Automation & intelligent execution is the most important contribution of machine learning to business which leads to operational efficiency and removal of manual intervention in business operations.
Better and more customised customer relationship management – Machine learning plays an important part in customising customer service portals specifically in creating the recommendation engine for customers wherein learning systems are extensively used.
Finance & Marketing Management – Machine learning can be extensively used to manage finance & marketing functions of a business; without the need of maintaining an extensive manual workforce around it.

Therefore, overall, machine learning tends to eliminate human intervention from decision making – thereby equipping systems to self-learn from data, and take intelligent decisions.

Insights on how and why freshers should join the data science/machine learning revolution

Before taking a decision to build a career in machine learning; students must ensure that they are genuinely interested in the stream; and are not simply getting attracted looking at the lucrativeness of the job description. The steps which freshers can follow for building a career in the field of machine learning are mentioned below.

Step 1 – Firstly, students need to have a basic know-how on statistical concepts, data engineering as well as software engineering.

Step 2 – Freshers aspiring to build a career in machine learning invariably starts with data engineering – which involves cleaning and massaging large volumes of data in form on which statistical and other analysis can be performed. In this stage, students need to focus on database management systems (such as SQL etc.) to ensure that they an establish logical relationships between unstructured data points; and devise an entity relationship model around it.

Step 3 – Once this is done, the next step is to learn any of the scripting language (preferably Python as it’s the most frequently referred to by developers) so that statistical and mathematical models can be run on the massaged and harmonised data.

Step 4 – The final step of becoming a machine learning engineer is to understand the supervision part of things i.e. how to train existing systems and applications using the training set; and then extrapolate it to enable machines to self-operate without the requirement of explicit information or instruction via software programming.

At each of the above stages, knowledge on business is an important addendum as all solutions at the end, correspond to particular business problems only.