If you completed your schooling with Maths and Science, you are most likely to be looking for a technology career where you could use your knowledge of maths and science to maximum use.

While there is no shortage of engineering streams to choose from but working with data might be a good idea as this is in vogue and promises a bright outlook as the world is poised to consume even more data in the coming years owing to emerging technologies like IoT, 5G, Machine Learning, Artificial Learning to name a few.

This boundless raw data being produced through different technologies needs to be assimilated to make sense and improve business operations. This is where the role of data analysts and data scientists kick in as they are the people who dive-deep into the raw data and bring out substantial information to help companies grow organically. 

But what is Data Science, and how is it related to Data Analytics? Have they interconnected fields, or are they different? For many, the two terms might evoke confusion. While data scientists find ways to mine data, write algorithms to bring the raw data in a useful and readable form. In contrast, data analysts work on pre-existing data sets to find solutions to problems and produce desired improvements and positive business outcomes.

If you are interested in knowing about Data Science and Data Analytics, keep reading as we are going to deal with these topics in detail to know the difference between the two streams and find the one you wish to pursue.

What is Data Science? 

Data Science is a multidisciplinary discipline that focuses on finding solutions and creating predictive solutions to unforeseen problems. Data scientists create algorithms and statistical models to answer questions that help leverage opportunities and help businesses grow. 

Data scientists often use predictive analytics, machine learning, mathematical and statistical models to develop usable and relevant data that the organisation could use to improve business processes. 

What is Data Analytics?

Data Analytics emphases on processing available data sets and performing statistical analysis. Analysts focus on creating ways to capture, process, and synthesise data to discover actionable insights into current issues and to establish the best way to present this data. In short, the field of data analytics is aimed at solving problems for problems we do not know the answer to. 

Data analytics also includes a wide range of statistics and several other analysis branches that help you combine different data sources and find connections while simplifying your results. 

Data Science Vs. Data Analytics: What Are the Differences? 

It is often for people to use these terms interchangeably, but data science and big data analytics are unique disciplines. The two streams’ scope is different, and that is where the difference lies. Data science is a collective term for a group of fields used to mine large datasets. Data analytics is a more focused version of this and can even be considered part of a larger process. Analytics is dedicated to providing actionable insights that are ready to be applied to a given query.

Another important difference between the two areas is the question of research. Data Science has nothing to do with answering a particular query. Instead, it deals with data mining to expose insights, helping the businesses utilize the data for furthering the business processes.

Data Analytics works better when you focus on questions that require answers based on existing data. While Data Science creates broad insights that focus on which question to ask, big data analysis focuses on finding the answer to the question asked.

These fields can be considered two facets of the same coin, and their functionality is highly interconnected. Data Science lays an important foundation in analysing large datasets to create early observations, future trends, and potential insights that may be important.

This information can improve the way information is sorted and understood, which can help in some areas, especially modelling, improving machine learning, and enhancing AI algorithms. 

However, Data Science asks important questions that you have not noticed before but offers few difficult answers. By adding Data Analytics to your mix, you can turn what we don’t know into actionable insights and applications.

Choosing Between a Data Analytics and Data Science Career 

Now, when you have understood the difference between the two streams, it is pertinent to choose the right stream for you. To help you choose the best stream for you, you should consider the following three factors. Since these factors will defer with every individual, you should carefully assess these factors before deciding the stream.

1. Personal Background

If you are adept at programming, aka writing code, algorithms, and machine learning, you might want to go for Data Science. Although statistics and maths are not compulsory, you will be better off if you have an affinity for these subjects.

Whereas, Data Analysts deal with huge data sets to find trends and create visual presentations to help businesses making logical decisions. If you would be more interested in being a part of the decision-making team, you should consider pursuing your data analytics degree. Please keep in mind that you should have passed your 12th with Physics, Chemistry, and Maths.

2. Your Interest

The most important thing is that you should be interested in something you plan to pursue. For that, you need to introspect by asking pertaining questions to yourself honestly. 

Data analysts love numbers, statistics, and planning. Data Analytics is a full-time engineering stream that could help you build a career after finishing your course.

Data scientists should have a combination of math, statistics, computer science, and interest and knowledge of the business. If this description resonates with you, maybe a data scientist role is the right choice for you.

3. Your Career Path

Data Science can lead to decent jobs, but it has its limitations since it is mostly in the form of certificate/diploma courses. Whereas you can do B.Tech in Data Analytics, which would result in a full bloom career for you in India and worldwide.

Data Science Vs. Data Analytics: Which is right for you? 

Since it is a decision you would make once, you should consider all the aspects carefully, including your interest, background, and career goals. 

Something right for someone might not work for you. Everyone needs to chart his/her paths. Do not plan to study something just because some of your friends will pursue a particular course. Be honest with yourself, assess the Pros and Cons of data science and data analytics, and then decide.

Remember, only a handful of Indian institutions offer certificate courses after the 12th. You should be mindful that certificate/diploma courses after schooling in India have severe constraints as far as career is concerned. Only a few higher education institutions have recently started offering Post Graduate degrees in Data Science. 

So, even if you are keen to pursue a data science career, you should consider doing an undergraduate program and then go for a certificate/diploma program or a PG program.

As described above, if you would love to work with huge data sets to find trends and create visual presentations to help businesses making logical decisions, you could finalize sticking to a degree program in Data Analytics. Choose the program that ticks all the right boxes for you.

Wrapping up

We hope that the above-detailed post would have shed light on the differences between Data Science and Data Analytics. By now, you would have got the clarity about the program you would like to pursue. We also hope you would have learned the Pros and Cons of the two programs and the distinction between them.

There is no doubt that Big Data will play a big role in everyone’s life in the years to come as the technologies will integrate into homes, offices, and manufacturing plants in a bigger way. You stand an excellent chance to make it big and realize your career goals if you choose the right college, like MIT School of Computer Engineering & Technology (MIT SOE). 

MIT SOE is part of the famous MIT-ADT University is known for its sprawling peaceful campus, best-in-class facilities, top-notch labs, and superlative campus placement records. 

Take the right step; go ahead and get in touch with MIT SOE’s admission team today!