Organizations all over the world are investing heavily in capturing data and gathering insights using analytics. The challenge is doing so, however, is that some organizations acquire the tools without updating their culture, which ultimately leads to abandoning big data projects. Overcoming this challenge has resulted in the rise in demand for data experts who bring a deep understanding of the technical aspects of analytics, data, and their business implications.
With these skilled professionals, organizations can make smarter investments and achieve success in analytics-driven projects.
This demand has made the roles of data scientists and data analysts the most coveted jobs in the world. About 40% of these professionals have a master’s degree like an MBA in Data Analytics and Data Sciences, which give them an edge over their competition.
Let us explore the meanings and differences between data analytics and data science, so that you can make the best choice about your career path in this field.
Data science and data analytics are terms that are often used interchangeably. While both these terms sound similar, they refer to different things and have significantly different implications for businesses. It is vital to know the distinctions between data science and data analytics to use them in the successful running of a business. This is particularly true today as the amount of data available to us is growing and becoming an essential part of our daily lives.
What is Data Science?
Data science is the broad term that describes a number of models and methods to obtain information. Math, statistics, and other tools, processes used to analyze and manipulate data are under the aegis of data science. The science focuses on finding actionable insights using large sets of raw data.
Uncovering answers to problems that haven’t been conceived yet, might sound rather vague, but the field of data science makes this possible using a number of advanced techniques. These techniques incorporate computer science, predictive analytics, statistics, and machine learning to comb through massive data sets.
The main goal of data science is to locate potential avenues of study with less concern for particular answers and greater emphasis on finding the right questions to ask.
Connecting information and data points to make useful connections for businesses is done with the help of data science. It delves into the vast world of the unknown in an attempt to find new patterns and insights. Data science differs from data analytics in that; it involves building connections to plan for the future. With the help of data science, organizations can move from inquiry to insights, as they have new perspectives and understanding of data and the way it is connected.
What is Data Analytics?
If we think of data science as the tool house, data analytics can be understood as a room in that tool house. Data analytics is more specific and concentrated compared to data science. It not only looks for connections between data, but data is sifted through with a specific goal in mind, which supports mining of that data. Practising data analytics involves combing through data to find nuggets that support companies in reaching their goals. Data analytics is often automated to offer insights in certain areas.
Using data analytics, organizations can categorize data into what is known to them and what is still unknown. Data analytics is useful in measuring events from the past, present, and future.
The focus of this discipline is on processing and performing statistical analysis on existing data sets. Using data analytics, data can be taken from insights to impact by connecting trends and patterns with the company’s true goals. This process tends to be more business and strategy focused.
Data analytics focuses on processing and performing statistical analysis of existing data sets. Analysts create methods to help capture, process, and organize data that can be used to uncover actionable insights for current problems and establish the best way to present this data. In other words, the field of data analytics can be directed to solve problems for questions we do not have the answers to. Data analytics is valued for producing results that can lead to immediate improvements.
How do data science and data analytics differ?
The nuanced differences between data science and data analytics can actually have a significant impact on a company. For instance, data scientists and data analysts perform different duties and come from differing backgrounds. This makes it critical to be able to use these terms correctly for companies to hire the best people to carry out tasks they have in mind.
Where and how data analytics and data science also varies with the industry type. Data analytics finds extensive usage in the healthcare, travel, and gaming industries. Data science, on the other hand, plays a vital role in the development of artificial intelligence, machine learning, and digital advertising.
A large number of companies today are turning to systems that allow them to use computers to sift through large amounts of data. An example of such systems is the enterprise flash system which uses algorithms to find the connections that will most help their organizations reach their goals.
The potential of machine learning across a number of industries is growing and is touted to play a critical role in how businesses are run in the future.
Owing to the undeniable value and merits data science and analytics bring to any organization, the demand for professionals with these skills is through the roof. In recent years, there has been a hike in the education institutes offering master’s programs in data science and business analytics.
With an MBA in data science and analytics, students obtain an in-depth understanding of topics like machine learning by exploring their historical and theoretical applications to business problems alongside hands-on learning. Equipped with the cutting-edge big data skills students develop, they are industry ready to aid organizations in producing actionable insights that are aligned with business goals in some of the following roles:
- Data Scientist
- Data Architect
- Business Analyst
- Business Intelligence Manager
- Predictive Modeller
- Quantitative Analyst
- Data Mining Expert
We wish you the best for your dynamic career in data sciences and data analytics!