Have you ever thought of the difference between Data Analytics vs Data Science? Both are booming in the market right now. Since the past decade, due to an exceptional rate of growth in data, organizations around the world have begun to recognize the importance of data, its scope, and management. Ever since then, the demand for Data Analyst and Data Scientist have begun to explode. The evolution of big data has transformed the world in many areas from healthcare, online shopping, finance, and insurance, etc., To comprehend big data better, the fields of data science and data analytics have gone from largely being relegated as part of academic studies to being an integral part of Business Intelligence for the complex strategic decision-making process. Technological advancements and the emergence of social media, smartphone, the Internet of Things (IoT), etc., has led to the proliferation of data to a great extent and has made entrepreneurs realize the need to leverage data to further their business prospects.
When you apply data analytics tools and methodologies in a business setting, we call it business analytics. The insights from data can be used for budgeting and forecasting, risk management, marketing and sales, and R&D on product development, etc., Meanwhile, the career prospect in data analytics vs data science are only continuing to grow, and people have started to develop their skills and knowledge in these areas to advance their career by getting placed in a good organization.
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The four major types of analysis can be classified into:
- Predictive Analysis: Makes use of historical data and past trends and projections to make future predictions and projections.
- Descriptive Analysis: Examines data and tries to understand and describe something that has already occurred.
- Diagnostic Analysis: Goes deeper into Descriptive Analysis and makes a diagnosis and tries to ascertain why such an event occurred.
- Prescriptive Analysis: Provide remedial actions to be taken by management to achieve the target and goals.
Dan Ariely, a well-known behavioral economics expert, once mentioned big data as “Everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it.”. The reality here is, though many people toss around the words like “data analytics”, “data science”, “data mining”, “big data”, “data forest” etc, even the experts are unable to figure out what exactly these terms actually mean. All of them are behind data, trying to analyze it, gain insights, and make predictions from it.
The role of data analysts and data scientists differ according to the domain in which they are working. While data scientists are expected to forecast the future based on past patterns or trends, a data analyst’s role is to extract meaningful insights from various data sources, which also include historical data. Therefore, though the terms are often interchangeably used, “Data plays a crucial role in all these cases. We bring to you the often-muddled difference between Data Analytics and Data Science.
If you take Data Analysts vs Data Scientists, both work on data, the key difference lies in what they do with it.
1) Data Analysts have a more narrow and specialist role while Data Scientists have a broader role: The Data Analyst is involved in examining large datasets, modifying them and making analysis, understanding the trend and making predictions, and finally providing reports and strategic insights enabling management to take strategic decisions. This is done using various tools, techniques, and frameworks that vary on the type of analysis being done. Eg., suggest which particular product features customers prefer most or uncover how marketing spending improves the conversion rates to help target better. Data Scientists, on the other hand, is involved in constructing design and processes for data modeling using prototypes, algorithms, predictive analysis, etc for making predictions for solving complex business problems. A data scientist gathers data from multiple sources and applies machine learning code and libraries to data, to make predictions from the data that can be used by management to make critical decisions. For eg., let’s take the example of Facebook, which uses a machine learning algorithm to gather the behavioral pattern of each registered user on the platform. Now, based on the user’s previous behavioral pattern, the algorithm can predict the behavioral pattern and interest of that user and can recommend articles that are of interest to them. Similarly, Amazon recommends products to users based on their past behavioral patterns.
2) Data Analytics is a subset of Data Science:Data Analytics is a single discipline within the umbrella of data science.
3). Data Analytics is a standalone field (ie., Micro based) in its own right, focused on specific questions and deriving actionable insights while Data Science is broad-based (ie., Macro based ) and focuses on answering broad strategic questions and driving innovation.
4). Data Science explores Unstructured Data using machine learning algorithms, neural networks, and Deep Learning with Artificial Intelligence to identify complex data patterns and correlate them, whereas Data Analytics explores structured data using tools such as SQL, data visualization software like Power BI and Tableau, etc
5) For Data Analysts the basic proficiency required is in Database Management and Data Visualization for industries with the immediate requirements to provide actionable insights whereas for Data Scientists the basic proficiency required is in Database Management, data wrangling, machine learning, programming languages Python and R, Mathematical and Statistical Modelling, etc to correlate data and make predictions.
6) The scope of Data Science is large compared to Data Analytics.
7) Data Analytics are used to generate actionable insights and make data-driven decisions whereas Data Science is used for risk evaluation and prediction of future outcomes.
8) In Data Analytics, the outcome is based on customer requirements which may or may not be predictive whereas, in Data Science, the outcome is a predictive model generated by drawing inferences from testing the hypothesis of data.
To make things simpler, let’s imagine a school (we will assume students to be a business for a moment). We have a principal who is also a teacher of that school (let’s assume the principal is a Data Scientist), and a class teacher (let’s assume the teacher is a Data Analyst). The main role of the principal and class teacher is to ensure quality education to the students (ie progress of business). The principal needs to have a holistic view of the overall functioning (both academic and administrative) of the institution. Broadly, he must know how elements interact at work, the impact that external factors can have on students, new technologies required for their growth, monitoring mechanisms, etc which can provide knowledge to the principal about the students well being. We have teachers for specialized subjects who conduct classes, conduct tests at regular intervals and monitor the performance of students based on the results. The class teacher will have a good idea of the student’s performance in the class and is the best person capable of answering specific queries about the student’s performance, and they can also suggest remedial measures for improvement. The principal will seek feedback from class teachers at regular intervals about students’ performance. In short, When we take Data Analysts vs Data Scientists, both play a crucial role in the healthy functioning of the business. Despite their overlapping skills at work, both pursue different approaches and interact with each other regularly, ensuring the smooth running of the business.
The good news for those aspiring to a career in Data Analytics and Data Science is that both professions are in great demand today, and the trend is not going to die down anytime soon at least in the near future. So get ready to dive into the field of Data Analytics and Data Science. with eduJournal.
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