Data Science Vs. Data Analytics: What’s The Difference

The world runs on data. Every day, users generate colossal amounts of data. This has led to Big Data becoming a major component in the tech world. The creation of large datasets also requires the proper and necessary tools to go through them and uncover the right and relevant information. The fields of data science and analytics have shifted from previously being related to only academia to becoming an integral part of big data analytics tools and Business Intelligence.

With so much information and so many technical terms, it has become difficult and confusing to differentiate between data science and data analytics. Though the two are interconnected, they pursue different approaches and provide different results. For accurately studying and analysing business data, it’s important to grasp what each of them brings to the table and how each is unique in a distinct way.

To help you optimise your big data analytics, we will break down for you both data science and data analytics, their differences, and the value they deliver.

What is Data Analytics?

There is a lot of raw data collected by businesses for analysis to drive profits, expand market reach, enhance customer experience, and so on. Data analytics seeks to find hidden trends within data and use the insights to solve complex business challenges.

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Several techniques and tools are used in data analytics to analyse colossal amounts of data which is not possible through manual organisation of data.

Data analytics can be better understood by going through the steps it involves:

• Understanding the data requirements and grouping them accordingly. This can be done based on the business problem at hand or the target audience of the company. Data can be grouped into many appropriate subsets, for example, age, gender, lifestyle, location, interests, habits, etc.
• Gathering data from several sources, both offline and online — physical surveys, computers, social media, online searches, etc.
• Then the next step is the organisation of data for analysis. The most common way to organise data is through spreadsheets. You can also use other platforms like Apache, Hadoop and Spark.
• The last step is to remove inconsistent, incomplete, and duplicate data sets and clean the data before analysis. This step eliminates any error in the data, and the data finally becomes ready to be analysed.

Data analytics is rapidly becoming essential in many major domains like finance, tourism, retail, healthcare, education, and hospitality industries. To learn more about data analytics, you can apply for online courses at upGrad and enhance your knowledge.

What is Data Science?

Compared to data analytics, data science has a much broader scope. In fact, data analytics is one of the many branches of data science. What happens before, during, and after analysing data is all included in data science, and data analytics is a part of that process.

In data science, statistical knowledge and domain knowledge are also needed besides knowing several programming languages like SQL, Python, etc. Data science experts use machine learning (ML) algorithms to process and analyse data — image text, video, and audio — to assemble AI systems that have the potential of thinking like a human.

Data Science has the following main components-

• Statistics: Collection, interpretation, analysis, and presentation of data through different mathematical methods are what statistics deal with.
• Data Visualization: The outcomes of data science are exhibited in the form of visually appealing diagrams, graphs, and charts which makes it extremely easy to view and understand the results. This helps by highlighting the key takeaways, facilitating quicker and more efficient decision making.
• Machine Learning: This component is essential as it uses intelligent algorithms that learn on their own and predict human behaviour with huge accuracy.

A data science expert defines and identifies possible business problems from several unrelated sources and collects data from these sources. Once data analysis is complete using data analytics tools, a model is formed and checked for accuracy iteratively. If you are interested in getting a thorough knowledge of data science, then there are courses online that you can pursue.

Data Science Vs. Data Analytics: Comparison

• Data Science is a multidisciplinary field, including machine learning(ML), data analytics, statistical research, domain expertise, mathematics, and computer science. In contrast, Data Analytics is an essential part of data science where data is analysed, processed, and organised to solve various business problems.
• The scope of data science is macro, whereas the scope of data analytics is micro.
• Data Science is one of the highest-paid fields in computer science, whereas data analytics is not as highly paid as a data science job (but it still pays higher than the market average).
• Data science requires a lot of knowledge, including advanced statistics, machine learning, data modelling, and a basic understanding of almost all programming languages like Python/R, SAS, SQL, etc. On the other hand, data analysis requires knowledge of business intelligence tools and a medium level of understanding of statistics, with solid knowledge of programming languages like Python/R and databases like SQL.
• In data science, the input is mostly unstructured and raw, which is further cleaned and organised for analytics. In data analytics, usually, the input is structured data, on which data visualisation and design principle approaches are applied.
• Data Science involves artificial intelligence, machine learning, and search engine exploration, whereas data analytics’ scope is not that extensive and is limited to using statistical tools and analytical techniques.
• Data science finds and defines emerging business problems that eventually lead to innovations. In data analytics, the problem is already known, and the analyst just tries to find the most optimal solutions to those problems.
• Data Science involves finding new and earlier unknown problems and then providing a solution to them by converting the data into business stories and use cases. Data Analytics only does in-depth interpretation and analysis without creating a roadmap.

Data Science is a vast domain with multiple specialisation branches emanating from it, and data analytics is but only a part of the larger canvas. Data science includes the entire business market and all its processes, from stakeholders, data analysis, storytelling, model building, testing, and deployment. Analytics is an important stage of data science where big data is reviewed and results are prepared as graphs, charts, and diagrams. However, one thing is clear, both fields are growing rapidly, creating numerous employment opportunities for passionate aspirants who wish to enter this field. With this, there is also a need to work extensively with data to understand the big picture and prove your worth.

Whether you choose a career in Data Science or Data Analytics, there’s plenty of room to improve, learn and grow your expertise.