MCA Data Science opens up numerous employment avenues. Here are eleven of the highest paying job roles that you can opt for after completing your Data Science MCA degree.
A business intelligence (BI) analyst transforms data into valuable insights and, in turn, increases the business value by helping in decision-making. They use data analytics, data modeling, and data visualization techniques to find patterns and trends in the company data so that the area of potential revenue loss can be identified and worked on. The role of a BI analyst is growing increasingly important with all the companies becoming competitively eager to monetize their company data.
As per Glassdoor, the national average salary for a Business Intelligence Analyst is INR 6,54,267 in India.
Data analyst has become one of the most on-demand job roles in recent times. On a typical day, a data analyst extracts data from a company database, analyzes that data using programming skills, and communicates the analysis results to a larger audience. A data analyst deciphers the patterns hidden in the data and translates the numbers, statistics, and figures into plain English so that the data is comprehensive to all.
Unlike data scientists, a data analyst may not be proficient in machine learning. Here are the skills expected:
Glassdoor reports that the national average salary for a Data Analyst is INR 5,06,078 per annum in India.
Like data analysts, data scientists collect unruly data, analyze them, and present them in a more usable format. But, comparing the technical aspect, data scientists are a step ahead than data analysts in that they understand big data from a more informed perspective. Besides a strong grasp of statistics, a data scientist has an advanced staying on machine learning, deep learning, and text analytics.
As per a report by Glassdoor, the average national salary for a data scientist in India is INR 9,08,231 per annum.
Data Engineers are responsible for building and maintaining pipelines of data and creating one single, interconnected data ecosystem within an organization. They also build algorithms to enhance the accessibility to the company’s raw data. Data Engineers optimize the infrastructure around the data analytic process and collaborate with different teams to lay down strategies for future data architecture and discover various opportunities for data acquisition.
According to Glassdoor, the average salary of a Data Engineer in India is INR 8,56,643 per annum.
Machine learning is a subset of artificial intelligence that involves developing data science algorithms and machine learning solutions by analyzing large data sets. Besides developing the solutions, machine learning engineers are also responsible for monitoring the performance and optimizing the functionality of these systems. They work along with data scientists and ensure that the data science models are always functional. They require strong statistics and programming skills, besides knowledge in software development.
As per PayScale, the average salary for a Machine Learning Engineer in India is INR 6,89,588 per annum.
Quantitative analyst is a highly sought-after career option in financial firms. They are responsible for identifying potential investment opportunities for an organization using data analytics techniques. They need to analyze vast databases and discover patterns, to ensure that an organization makes a less risky and more profitable investment. To be a great quantitative analyst, one needs to be good in four fields – mathematics, data science, finance, and application development.
As per a Glassdoor report, the national average salary for a Quantitative Analyst is INR 13,52,340 per annum in India.
Operations analysts are mostly employed by larger companies, but they can work as consultants too. They ensure that the internal processes of a business – such as product manufacturing & distribution, internal reporting systems, and the streamlining of business operations – run smoothly. They must be business-savvy and have a strong technical knowledge of the system. From large grocery chains, to postal services and military, operations analysts are seen working in every sector, with a variance in remuneration of course.
As per PayScale, the average salary for an Operations Analyst in India is INR 3,74,563.
Data analytics has found huge application in the digital marketing sector. A marketing analyst’s primary responsibility is to analyze traffic from websites and social media campaigns. They undergo thorough market research comprising the study of market conditions, consumer behavior and competitors’ activities to help an organization decide which services should be sold, to whom and at how much. Market analysts are in great demand as they drive smart decisions on where to make investments and how to leverage the existing resources. No company wants to drive campaigns if the traffic and profit from them is not great.
PayScale reports that the average salary for a Marketing Analyst in India is INR 4,78,160 per annum.
Systems analysts are responsible for designing and developing new IT solutions; enhancing and modifying the already existing ones; analyzing systems requirement; conducting tests to improve the quality of the solutions; and integrating new features to improve the efficiency and productivity of a particular business. Along with high level of technical expertise, they must have clear knowledge of current business practices.
As per PayScale, the average salary for a Systems Analyst in India is INR 6,56,677 per annum.
The data science market has seen a steady growth over the years. Here are the future growth expectations and current market scenario of the sector.
Every career comes with its own pros and cons, and data science is no exception. A career in Data Science, though lucrative, is not for everyone. Here are the pros and con that you may face in the field:
|Programming is challenging
|Excellent career growth
|Data science is a blurry term
|Not every company knows how to best use data science skills
|In huge demand
|Data can be messy
|Abundance of positions
|Inconsistency in job titles
|A great deal of domain knowledge required
|Highly prestigious jobs
|Data Science is huge, mastering it can be difficult
Most of the cons are avoidable if you are careful enough while job hunting and the others are a matter of personal accomplishment. But, who wouldn’t want their job to be a bit challenging. After all, challenge is the opposite of boring.
We are sitting on a gigantic pile of data that keeps on increasing at a supersonic speed every minute. On an average day, 188 million emails, 18.1 million text messages, and 4.5 million videos are sent or watched across the entire world. In short, we generate more than 2.5 quintillion bytes of data on a single day. With such a consistency, it becomes more than just necessary to have someone who can analyze and tame the data and bring out invaluable information for an organization. Our point being, the need of a data science professional will always be there. It will only increase in the next five years. There are numerous career opportunities that you can easily fit into after completing a data science course. The only requirement will be that you must be a skilled professional who is keen to learn and share your learning.
What you need to be adept in the data Science field is guidance, which will come from a good degree. Check out the MCA Data Science degree from ADYPU that will polish your skills and prepare you for the cut-throat industry. It is a 2-year, full time program. To be eligible for this program, you must fulfill the following criteria:
You should pass a minimum three-year duration bachelor’s degree in any discipline (Science, Commerce, Arts, Engineering, or relevant) from any approved University. Also, you must have mathematics as a subject in your 10+2 or at any graduate level examination.