Data is at the core of businesses and industries today. With the rise of Big Data, companies and organisations around the world are harnessing the potential of the data at their disposal to drive growth, scale profitability, enhance customer satisfaction, and improve the overall operations process among other things. And behind all of this lies one major secret – Data Science.
What is Data Science?
Data science is an amalgamation of multiple aspects of data such as data inference, algorithm development, and technology to help analyse the date and find innovative solutions to complex problems. In other words, Data science is all about analysing data and finding creative ways to drive business growth.
In order to fully understand the question “what is Data Science,” we need to start from the basics. At the primary level, data science seeks to reveal the hidden patterns within data sets. With the help of these useful data insights, companies can enhance their decision-making process, streamline their sales and marketing strategies, scale the revenue, and so much more. For instance, Netflix mines user data to understand the customer movie viewing patterns, what boosts their interests and towards what particular genre to determine what kind of shows and movies they should produce.
Furthermore, data science also involves the development of data product. By a ‘data product’ we mean a technical asset that uses and processes data to generate algorithm-oriented results. A recommendation engine is the most excellent example of a data product. For instance, Amazon’s recommendation engine ingests user data to provide personalised recommendation list based on your previous purchases or viewed items. Similarly, Spotify tracks consumer behaviour to understand their preference in music, thereby providing customised music lists for individual users.
Another important thing to tackle while answering “What is data science” is the components of data science. Let’s look at the same.
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What is Data Science Comprised of?
Data science essentially involves the blend of three core areas of expertise – Mathematics, Technology, and Business Acumen.
Mathematics and statistics lie at the heart of data science. To be able to mine data successfully, one needs to view the data from the quantitative perspective. The correlations in data, finding hidden patterns and complex trends within demands a certain degree of expertise is Mathematical concepts such as classical statistics, Bayesian statistics, and Linear Algebra, to name a few.
Technology and Hacking
When dealing with large amounts of data, it is a given that you must have a knack for technology. A large part of a data scientist’s job is to leverage technological tools in uncovering valuable patterns within the data. He/she must be well-versed with programming languages such as Java, Scala, Python, R, and SQL. A data scientist needs to be a staunch algorithmic hacker, and by that, we do not mean hacking into computers illegally. It means that they should be able to hack into complex codes and break it down into more understandable and readable formats.
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To excel in the field of data science, one needs to have a strong business mindset. Since data science aims to drive value generation of businesses, apart from being comfortable with working on large data sets, a data scientist also needs to bear a tactical business mindset. He/she needs to dive into data, extract useful information from it, and advice business organisations about how they can use that information to their benefit.
Remove the above three, and the answer to “what is data science” will be reduced to “nothing”.
Who is a Data Scientist?
Data Scientists are inherently analytical data experts equipped with the requisite skills to solve complex problems complemented with the unquenching thirst for exploring a wide array of issues that need to be addressed. They are highly skilled individuals combining the best of both worlds – IT and business. Hence, data scientists are part computer scientists, part mathematicians, and part trend-analysers.
The primary goal of a data scientist is to gather, analyse, and organise massive amounts of data, and in the process discover valuable insights that aid in shaping the business strategies of organisations. Over the years, the job of data scientist has been in high demand as businesses today are relying on Big Data and data analytics more than ever. In fact, Harvard Business Review declared that the job of a Data Scientist is “The sexiest job of the 21st century!”
Now let us look at the job responsibilities of data scientists.
In the book Doing Data Science, authors C. O’Neil and R. Schutt elaborate on the duties of data scientist as such:
“…a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills… Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualisation and data sense.”
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Here are the most fundamental duties of a data scientist:
- Gathering vast amounts of structured and unstructured data and converting them into actionable insights.
- Identifying the data-analytics solutions that hold the most significant potential to drive the growth of organisations.
- Using analytical techniques like text analytics, machine learning, and deep learning to analyse data, thereby unravelling hidden patterns and trends.
- Encouraging data-driven approach to solving complex business problems.
- Cleansing and validating data to optimise data accuracy and efficacy.
- Communicating all the productive observations and findings to the company stakeholders via data visualisation.
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What is Analytics?
By now you are aware that analytics forms a vital aspect of data science. Data scientists have to rely on analytics to uncover meaningful patterns within the raw data. But, what exactly does Analytics mean?
Analytics is the process of gathering data from multiple sources and processing, examining, and interpreting the data to gain meaningful insights. It is a field that utilises multidimensional tools like Mathematics, Statistics, predictive modelling and ML to uncover useful patterns in data. Analytics can be classified into two categories:
- Quantitative data analysis – This type of analysis analyses numerical data with quantifiable variables that can be statistically measured.
- Qualitative data analysis – This analysis takes on a more interpretive approach to data, that is it aims to ‘understand’ the patterns in non-numerical data (text, images, audio, video).
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As more and more data keeps piling up across various industries around the world, analytics is increasingly becoming an integral part of businesses. To survive in the cut-throat competitive market, companies need to harness the data at their disposal to find ways in which they can stay ahead of the competition. And the answer to this is, of course, data analytics. From the healthcare and education sector to sports and smart homes, analytics is rapidly taking the business by storm.
I hope this article helped answer your queries regarding “what is data science.” and more!
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How do Data Scientists mine out insights from the available data?
The first and foremost step towards mining out insights is to review and understand the data. When confronted with a complex problem, data scientists take on the role of investigators. They look into leads and try to figure out whether there are any patterns or qualities in the data. This necessitates a significant amount of analytical inventiveness. A data-driven understanding is critical for strategic planning. Data scientists serve as advisors in this regard, advising business stakeholders on how to act on discoveries. The second step is the data product. A data product is a piece of technical functionality that encapsulates an algorithm and is intended for direct integration into core applications.
What is the requisite skill set required to become a Data Scientist?
To become a data scientist, one requires two types of important skill sets:
-Technical skills – Technical skills include:
1. Math – Statistics, Probability, Linear Algebra and Multivariate calculus.
2. Programming – Python, R, SAS, etc
3. Analytical Tools – SQL, Spark, Hoop, Hive and Pig.
4. Machine Learning
5. Data Visualization – Power BI, Tableau
6. Data Wrangling
-Non-technical Skills – These skills include
1. Business acumen
3. Business Awareness
What is the difference between Data Science and Analytics?
Many individuals mix up the phrases Data Science and Data Analytics. But they are unique fields. Let’s know how are these different
1. Scope – Data Science refers to a variety of disciplines that are used to analyse massive databases. On the other hand, Data analytics software is a more specialised form of this, and it can even be regarded a component of the overall process. The goal of analytics is to produce actionable insights that can be implemented right away based on existing inquiries.
2. Exploration – Data science isn’t about answering specific questions; rather, it’s about sifting through huge databases in often ad hoc methods to uncover insights. Data analysis is more effective when it is targeted, with specific questions in mind that require solutions based on available data.