Data opportunity that spans sectors

Data opportunity that spans sectors

Futuristic A good foundation in maths, machine learning and data visualisation are essential for a data miner.

We are living in an age of information overload with technological advancements leading to ever-improving connectivity and communication. Organisations and institutions use the data generated to understand their customers and audience. Data Science has been gradually creeping into every sector – be it technology, arts or politics. The recent trend of quantifying everything has made Data Science an inevitable part of our everyday routine.  

From casting votes to uploading pictures on Instagram, everything constitutes data. Reports predict that by 2020, about 1.7 MB of digital data per second will be created for every single person on this planet. Organisations these days are focusing more on using data to evaluate progress, build solutions and make decisions. A data scientist should be capable enough to undergo open-ended research and deduce simple algorithms from complex mathematical data. He or she should also have a holistic approach on unstructured data and spot hidden patterns.

There are many ways to be a data scientist, which include degree and graduate certificates and self-guided courses or workshops. To improve one’s skill in data mining, it is necessary to have a strong foundation in mathematics, machine learning and data visualisation.

Skills required

Here are some skills that are necessary for a good grounding in the field.  

Basic use of statistical tools and fast mathematical calculations. Ability to work with large data sets and examining them from different perspectives.

Good grasp of programming languages like Java, Perl, C or C++, Python and SQL clubbed with statistical analysis.

Extensive knowledge of data analytics software like SAS, R, Hadoop and Tableau.

Critical reasoning skills and problem-solving ability

Data science offers different types of roles which are determined by the exploration and analysis of massive data. Some professions like data engineer are more into cleaning data sets and implementing data solutions. Others are into market research of converting unstructured data into organised ones with the help of SQL database and presenting them in different management tools. The most popular roles include:

Data scientist: This forms the core analytics part of big data. Data scientists are involved in understanding and exploring data patterns, to analyse the impact on businesses. They apply statistical and mathematical models to simplify data. Along with analysing data, they also devise solutions for various data complexities.

Data engineer: This role comprises all software engineers, who are involved in the non-analytical part of big data. Their role is more focused on coding, cleaning up data sets and implementing suggestions as well as data solutions that come from data scientists.

Business intelligence: A business intelligence specialist is involved in the market research of various structured and unstructured data and generates reports to analyse business trends. They are trained to work on SQL and other statistical tools. They send these reports to the management and update the data models as and when required.

Data manager: Also known as database administrators, they are involved in the structuring of data and management of unstructured data. They are responsible for creating the infrastructure and database systems that meet the needs of research and data science teams for the information gathered. They also review data for inconsistencies and conduct maintenance of data.

Data analyst: Data analysts help make sense of large amounts of data, specifically for use by businesses. They work with SQL databases, Excel, Tableau and other software to analyse various kinds of data, such as website traffic, sales figures, operational costs, etc. They then develop reports to be used to create solutions and make strategic decisions. Apart from these, specialised roles in Machine Learning, Artificial Intelligence and Big Data are also coming up. All in all, a data scientist can be a programmer, product developer, analyst and statistician, all rolled into one.

Courses and institutes: Due to its diverse application and multidirectional nature, Data Science requires a plethora of skills including Mathematics, Statistics, Computer Science and Hacking/Coding, coupled with substantial expertise in business or a field of science. Knowledge of the concepts of Artificial Intelligence and Machine Learning are also beneficial.

Some leading institutes that offer courses in Data Science and Data Analytics in India are Indian Statistical Institute; Indian Institutes of Science Education and Research; Indian School of Business; Indian Institute of Management; SP Jain School of Global Management; Aegis School of Business; Great Lakes Institute of Management.

While degrees can help you enter the fields or form a base, there are many good online learning platforms that offer certifications in data science, as well as specific skills required for it. 

(The author is chief executive officer, Mindler)