As a company that trains Data Analysts and Data Scientists, we receive calls from customers every minute to inquire, “What’s the difference between Data Science and Data Analytics?” We hear from you! And If you ask us, we will tell you it’s an extremely relevant question – especially in the global economic landscape today. Interesting fact – Harvard Business Review awarded Data Science the “Sexiest Job of the 21st Century” and Data Analytics is among the top-skills to have in 2021. These two fields are presently among the most lucrative and well-paid jobs in the world, and it’s important to understand the differences and respective applications. The word “Data,” before Data Analytics and Data Science is not the only similarity these two fields share – just saying. One thing is for sure; these two roles primarily work with data for better understanding and decision making. To help understand these two categories, we are going to break them down and examine their goals and values.
DATA SCIENCE: Data Science is a multidisciplinary field focused on finding actionable insights from large sets of data. A data scientist’s main goal is to ask the right questions and unearth answers to questions or problems we didn’t know existed. Compared to a Data Analyst, a Data Scientist is less concerned with answering specific questions but rather asking the right questions. We will use our home country as an example. MTN is the leading provider of telecommunication services in Ghana and has over 17.83 million subscribers. At the end of March 2020, MTN had over 15.5 million registered Momo subscribers. These registered subscribers have information that will include their names, age, gender, transactions, et. al.. In this scenario, a Data Scientist gathers this data and builds a model that will help predict who should have access to a Momo loan and what amount they should most likely receive. Data Scientists use several techniques to obtain the right answers, including predictive analytics, statistics, computer science and machine learning techniques to understand data and establish solutions to data-driven problems.
Let’s take a look at a Data Scientist’s role description:
· Identify valuable data sources and automate collection processes
· Undertake to preprocess of structured and unstructured data
· Analyze large amounts of information to discover trends and patterns
· Build predictive models and machine-learning algorithms
· Combine models through ensemble modelling
· Present information using data visualization techniques
· Propose solutions and strategies to business challenges
· Collaborate with engineering and product development teams
DATA ANALYTICS: Data Analytics is the process of analyzing existing datasets to find trends and draw conclusions about the information they contain. Data Analytics’ main goal is to solve problems we know to exist and it deals with finding immediate solutions to an existing problem. Quick question: How does a company know what region or market it’s making the most sales off? Easy answer – Data Analytics. Presently, MTN has recorded an uptick in data subscribers with contribution of voice calls revenue dropping from 47.1% to 45.6% whilst data revenue grew to 19.4%. Per this information, imagine MTN wants to understand the increase behind data bundle purchases and which age group is actually driving this increase in order to target them better.In this scenario, the Data Analyst will try to answer the question, “What age group is driving the increase in data revenue?” by analysing the already existing data in order to draw the needed conclusion.
Let’s take a look at a Data Analysts’ role description:
· Interpret data, analyze results using statistical techniques and provide ongoing reports.
· Develop and implement databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality.
· Acquire data from primary or secondary data sources and maintain databases/data systems.
· Identify, analyze, and interpret trends or patterns in complex data setsFilter and “clean” data by reviewing computer reports, printouts, and performance indicators to locate and correct code problems.
· Work with management to prioritize business and information needs.
· Locate and define new process improvement opportunities
Even people who have basic knowledge of Data Science and Data Analytics use the two interchangeably. The actual point is this: whilst they’re different, they’re just opposite sides of the same coin and are interconnected in the process of understanding data. So instead of thinking of it as Data Science vs Data Analytics, think of it as Big Guy Data Science and his younger sibling Data Analytics. In the long run, most Data Analysts decide to build on their skills to become Data Scientists.
Kickstart your data analytics or data science career by joining the next Blossom Academy bootcamp. Express interest at www.blossom.africa or email us at [email protected]