Data Science vs Data Analytics - How to Decide Which is Right for You?

Data Analytics and Data Science are the buzzwords of the year. For those looking for long-term career potential, big data and data science jobs have long been a safe bet. This trend is likely to continue as AI and machine learning become highly integrated into our daily lives and the economy. Today, data is the new oil for businesses that want to gather critical insights and improve business performance to grow in the market. But who will gather the insight? Who will process all the collected raw data? Everything is done either by a data analyst or a data scientist. These are two of the most popular job roles in this field as companies around the world try to make the most of data. Data science and data analytics are a mix of words that are interchangeable and overlapping with each other but still quite different.

Yes, professionals (both beginners and transitioners), we have our ear to the ground. Many of you starting a career in data analytics or data science are often confused and unsure which is the right career path for you. Well, the decision will depend on what your career goals are, as well as your skills and competencies. That said, to save you any confusion and give you a clear insight into these two innovative career paths, here we explore data analysis and data science. The purpose of this article is to help you better understand the difference between the two subjects so that you can decide which career path would be better suited for your career aspirations.

Read More : A Roadmap To Become A Data Scientist At A Big Tech Company!

Data Science vs Data Analytics — Understanding the Difference

Data Science vs. Data Analytics — Fundamental Goals

Data Analytics - Analyze and My Business Data

Data Science — Discover the right business questions and find answers.

Data analysis involves answering the questions asked in order to make better business decisions. It uses existing information to uncover actionable data. Data analytics focuses on specific areas with specific goals. Data science, on the other hand, focuses on discovering new questions that you may not have realized the answers needed to drive innovation. Unlike data analytics which involves checking a hypothesis, data science tries to build and shape connections to answer questions for the future. If data science is the home of all methods and tools, then data analytics is a small room in that house. Data Analytics is more specialized and focused than Data Science.

Data Analytics focuses more on looking at historical data in context whereas Data Science focuses more on Machine Learning and Predictive Modeling. Data science is a multidisciplinary mix involving algorithmic development, data estimation and predictive modeling to solve analytically complex business problems. On the other hand, data analytics includes broad statistics and a few different branches of analysis.

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Data Science vs. Data Analytics — The Skills

Data Analytics - with intermediate statistics knowledge and excellent problem-solving skills

Proficiency in Excel and SQL databases to slice and dice data.

Experience working with BI tools like Power BI for Reporting

Knowledge of statistics tools like Python, R or SAS

It is not necessary to come from an engineering background to become a data analyst, but having strong skills in statistics, databases, modeling and predictive analytics comes as an added advantage.

Data Science - with Maths, Advanced Statistics, Predictive Modeling, Machine Learning, Programming -

Proficiency in using big data tools like Hadoop and Spark

Expertise in SQL and NoSQL databases such as Cassandra and MongoDB

Experience with data visualization tools like QlikView, D3.js and Tableau.

Proficiency in programming languages ​​like Python, R and Scala.

Data Analyst vs Data Scientist - Job Role

The job roles of Data Analyst include –

exploratory data analysis

data cleansing

Discover new patterns using various statistical tools.

Develop visualizations and KPIs

Data Science vs Data Analytics – Which Should I Choose?

At Springboard, we have created data analytics and data science courses with the help of industry professionals to guide aspiring professionals to pursue lucrative careers in the world of big data. To understand the differences between data analytics and data science courses more effectively, we suggest that individuals consider some of the important dimensions, such as the tools and technologies to be mastered in each of these courses. Having practical practical knowledge and expertise of various analytical and database tools is the secret success mantra to excel in the data science and analytics industry.

Springboard's Data Analytics course provides comprehensive training on tools such as Excel and SQL to manipulate and analyze large amounts of data. In addition to learning Excel, SQL and Python, the Data Analytics course includes Power BI to create dashboards and visualizations to communicate analysis results