– Dr. D Srikanth Rao, director, Manipal Institute of Technology, Manipal Academy of Higher Education. Data science is nothing but a blend of mathematics, good business decisions and tools, algorithm and machine learning used to bring out knowledge and insights from both structured and unstructured data. With data becoming the fastest and largest growing commodity companies are in dire need for data analysts making data analysis one of the top, fastest growing careers. Data analysis helps decode hidden patterns and insights from raw data helping companies in faster and better decision-making process. Pursuing a course on data science and engineering helps students prepare for various fields of data science. It provides insights into various business tools such as: SAS Python Apache Spark BigML MATLAB Excel Tableau R programming and so on. Studying these in depth as part of the curriculum helps students explore software and come up with new, innovative ideas that will guide them on their usage of these tools. Courses not only teach them software but also how to interpret data, collect information and analyze it to produce fruitful results that will help the company. Apart from software learning, these courses also teach them to tap into their creativity, curiosity, strategic perspectives, communication skills, continuous learning and their statistical and technical expertise. A data scientist must be able to explore data, formulate problem statements in line with business models and engineer effective end to end solutions. There is a surging demand for data scientists in the fields of: Healthcare and pharmaceuticals Telecommunications Internet-based businesses Energy sector Automobile sectors and so on. Some of the top careers in the field are data scientists, machine learning engineers, machine learning scientists, applications architects, enterprise architects, data architects, infrastructure architect, data engineer, business intelligence (BI) developer, statisticians and data analysts. Some of the major skills a data scientist must acquire are programming skills; this is nothing but knowledge of the trade tools. Statistics and Machine Learning are other skills that a data analyst must have. Statistical knowledge will help in deriving solutions using various approaches in designing or evaluating experiments while machine learning comes in handy while working for completely data driven companies like Netflix, Uber and Google maps. Multivariable Calculus and Algebra are skills that must be learnt as they can help a data science team to build their own implementations in house if ever necessary. Data wrangling, data visualization and communication come in as handy skills to have because data is often messy and difficult to work with. These skills help effectively sort data and deal with its imperfections, they also help in data driven decisions and describing both technical and non-technical findings. Software engineering and data intuition are a couple more skills that have proven essential in smaller companies and companies looking for data-driven problem solvers. Before the digital revolution, the amount of data available was relatively small, structured and at our disposal. So traditional BI tools were more than sufficient to analyse these small structured sets of data.…
