It won’t be an exaggeration to state that data science and machine learning are excellent career choices for those interested in building algorithms and crunching numbers. As big data, data mining, and data analytics become more relevant and drive more impact in the mainstream IT industry, machine learning, and data science gain unprecedented traction in the global job market. While organizations all over the world are going over the board for skilled data professionals, the annual remunerations for data scientist and machine learning experts are going into overdrive.
According to research by McKinsey, in 2018 the U.S will face an extreme shortage of data scientists (close to 190,000), and analysts and managers (estimated at 1.5 million) capable of deriving actionable insights from the deluge of big data. This demand for data experts is not just limited to the U.S., several Indian companies have also started hunting for reliable data science and machine learning professionals who can add value to their business. However, there is still a demand-supply mismatch in the field of machine learning and data science. This has led to many data science courses, and students and professionals alike are readily enrolling in them to make themselves relevant to the industry.
As every business is identifying the importance of data analytics to stay ahead of the competition, the demand for data professionals and machine learning experts is bound to be on the rise. If you’re making up your mind to enter into these fields, here is an analysis of the prospects of a career in data science and machine learning:
Machine learning implementation
The next tier of demand in the field of machine learning is for professionals who can build practical deployments, particularly in collaboration with a cutting-edge research team. Machine learning implementation experts have strong software development skills and substantial work experience.
They make production quality systems for industrial big data, and also have a thorough knowledge of R or Python to put together a model on a sample dataset. However, much effort is already being directed towards training “machine learning engineers” who would have a high level of expertise in the field.
The tasks that utilize machine learning solve problems related to manipulating and classifying large numbers of vectors in high-dimensional spaces. While classical algorithms that are currently in use take time, quantum computers are likely to be extremely good at manipulating the high-dimensional vectors. It is expected that the development of both supervised and unsupervised quantum machine learning algorithms will exponentially increase the number of vectors as compared to classical algorithms. This would result in a distinctive increase in the speed at which machine learning algorithms would run.
As quantum computing gains more recognition, its usage is expected to grow equally into machine learning and finance spaces. By analyzing thousands of datasets, quantum computing can prove effective in determining the best investment options. Further, its techniques will also be proficient in mapping key fraud indicators in their early stages.
The Hercule Poirot of a data science team, a data analyst, is responsible for looking into the data and extracting meaningful insights. Data analysts are expected to collect numerical information and present the ideas in a meaningful way – usually in the form of graphs, charts, dashboards, and reports. They are also responsible for identifying trends and creating predictive models to provide businesses with meaningful insights. A career as a data analyst requires a working knowledge of database systems and spreadsheets, and an in-depth understanding of programming languages like Python, R with machine learning. It is crucial for data analysts to have excellent analytical skills coupled with proficiency in mathematical and statistical models. Since they deal with data representation, they also need to have an ability to work with different reporting, business intelligence, and data visualization tools.
One of the most lucrative prospects of data science is the application of statistics to collect, analyse, and interpret quantitative data. Statisticians implement statistical theories and methodologies to determine data collection methods and to decide which data is required to answer specific questions or problems. This field of data science demands a strong background in statistical methodologies, and a logical and stats-oriented mindset. Statisticians also need to have hands-on knowledge of distributive systems like MapReduce and Hadoop, and a proficiency in data mining and machine learning is a must.
One of the most attractive and upcoming job titles that a data professional can put on his/her curriculum vitae -data architects are responsible for developing the underlying architecture to process and analyze data in the way demanded by the organization. Data architects design and create the basic blueprint of a data science project by centralizing, protecting, integrating, and maintaining the source of data from a wide range of data management technologies and systems. Data architects are responsible for making sure that the entire data environment is always available, stable, and secure. Since the role of data architects is application driven, it is essential for them to have a thorough understanding of data manipulation programming languages like Hive, SQL, Spark, XML, etc. They need to have detailed knowledge of database architecture and should demonstrate proven expertise in data mining and warehousing solutions.
While statistical expertise and programming skills are regarded as the foundation of data science and machine learning, a strong business acumen is always helpful for professionals to navigate their career on the right track. However, as technological improvements like advanced analytics, deep learning, and cognitive computing gain relevance, the industry requirement is slowly shifting from traditional data scientists to machine data scientists.
A study by Burtch Works titled ‘Salaries of Data Scientists’ reports that for both individual contributors and managers alike, the median base salaries of data and machine learning experts increase with job levels. Irrespective of the trends, the opportunities in the field of data science and machine learning are unlimited as these are bound to become one of the highest paid and most sought-after professions in the next five years.
Author Bio : Vivek is the President of Consumer Revenue at UpGrad, an online education platform providing industry oriented programs like Big Data, Digital Marketing or Data science courses etc in collaboration with world-class institutes, some of which are MICA, IIIT BANGALORE, BITS and various industry leaders which include MakeMyTrip, Ola, Flipkart to name a few. He has 19 years of experience in diversified industries like Consumer goods, Media, Technology Products and Education Services. He has been leading businesses & multi-cultural teams with a consistent record of market-beating performance and building brand leadership. His previous engagement has been with Manipal Global Education services as Sr General Manager, Education Services (Digital Transformation Strategy & Global Expansion).”