Data Analytics: Prospective student focus for career prospects
Data Analytics involves aiding better decision-making by inspecting, cleansing, transforming and modeling data. Data analytics techniques are used across all industries to enable people to make better decisions. These are also used by scientists and researchers to prove or disprove scientific models, theories and hypotheses.
Sarita Digumarti, COO & Co-Founder, Jigsaw Academy, The Online School of Analytics, talks to Careers360 about trends and career prospects of the field.
Read the full interview below:
Q1. What are the emerging trends in Data Analytics?
A. The demand for analytical skills is at its peak. In fact, it has become essential for everyone looking at career growth, to be analytics savvy. In 2017, analytics skills will not be exclusive to just the Data Analysts, for these skills have indeed become a mandate.
Some of the top trends you need to be thinking of as you work towards career growth in the analytics are:
Democratisation of Data – Data will be generated, collected more easily than earlier and analysed for various purposes at a much easier scale than earlier with the help of different analytical tools.
Takeaway: Data-driven decision-making is the need of the hour
Visual Analytics – Visual Analytics has become the common language for both data science professionals and non-data guys alike.
Takeaway: You need to build a collection of visual analytic tools into your broader analytics skill set.
Data Skills will not be Exclusive to just the Data Analysts
It is important to note that data is a mainstream story and every manager today has access to data. As such we are seeing a big shift in who management is being delegated to, in that it is more and more the people who are data savvy, that are taking on leadership positions.
The Internet of Things (IoT) will take on lives
Sensors in every consumer durable device will begin to become a norm and connectivity between devices will lead to enormous amount of data generation which must be analysed and made sense of. IoT will find its use and place in our everyday lives. Smart homes are already a reality.
Takeaway: Upskill in IoT, The next Big Thing!
Q.2. What are the factors to consider before opting for the program?
While the analytics field is an open playground for anyone to make the most of, students or professionals with quantitative aptitude and cognitive skills find themselves at a better place to take up analytics as a career, as they are well-equipped to deal with numbers.
Analytics is a profile where a student/professional needs to be consistent and can learn and upskill himself with the changing analytics trends. Since Analytics is evolving as a domain and finding its applications in various functions from Supply Chain to Artificial Intelligence, technology is also leading to the creation of new tools and technologies. The ever-evolving industry demands a Data Scientist to be constantly “learning and evolving” in the changing times. And this very skill keeps an analytics professional going in the long run.
Q3. What are the subjects a student should focus on before pursuing Data Analytics?
In general, good introductory courses in Data Science do not assume any prior knowledge of analytics and will cover statistics, analytics techniques, and tools. It does help to brush up on basic statistics (simple descriptive statistics and basic probability concepts) before embarking on a Data Science course.
Q4. What are the top universities around the world to pursue a course in Data Analytics?
There are still very few specialized degree or certificate programs available in this field. While some online courses offer analytics training for a fraction of the cost of a full-time programme, some of the universities that provide analytics related courses are:
Multiple IIMs including IIM-Bangalore and IIM Calcutta, as well as other universities like NMIMS offer courses in business analytics for professionals with at least 2 years of experience – these typically will be long term (9 months to 2 years) in a blended model – a combination of in person and online training programs aimed at working professionals that do not want a break in their careers
Globally many well-known universities are NCSU, UT-Austin, UChicago, NYU and MIT which offer Master of Business Analytics
Q5. What are the career prospects of a Data Analyst?
The need of the hour is specialized learning paths adapting the ever-evolving roles in data science, Big Data, machine learning and data visualization.
The roles as mentioned below are new and still evolving. As the analytics industry will mature, so will the acceptance of these classifications.
Business Analyst – Someone with domain knowledge as well as technical expertise
Skills: Domain Experience, Basic Statistics, Simple Analytics; Predictive Modeling, Excel
Data Scientist – Someone with an extensive analytics background
Skills: Statistics, Advanced Modelling, SAS, R, Python, SQL, VBA
Machine Learning Specialist – Someone with expertise in machine learning and experience working with large data sets
Skills – R or Python, Neutral Networks, Support Vector Machines, Random Forests
Big Data Specialist – Someone who analyses Big Data to derive insights
Skills – Hadoop, MapReduce, Hive, Pig, Sqoop, Spark
Data Visualizer – Someone with good analytics knowledge and expertise in creating reports
Skills – Ability to create reports and be a visualizer, Excel, R, Tableau, etc.
Q6. What the various research prospects available for a student pursuing an advanced degree in Data Analytics?
There are multiple research opportunities for students pursuing advanced degrees in Data Science and Analytics. If a student is part of a certification program, then he is expected to work on a Capstone project that involves live data from a client company. If a student is part of a more intensive and rigorous Master’s programs, typically they will be expected to work with companies on larger projects that involve extensive research and application.
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