The data analytics vs data science argument has been around for long and it’s still one that needs clarification. If you’re pursuing a big data analytics career, you have two main options – data analytics and data science. Both entail analyzing data to extract useful insight. More importantly, the two are interconnected.

However, the two aren’t the same thing. While data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make informed decisions, data scientists are more concerned with data modeling.

Data Analytics vs Data Science: Which Career is Better?

Below we provide a breakdown on data analytics vs data science, focusing on roles and responsibilities, educational background, and required skill sets to help you make the right decision.

A Career in Data Analytics

Data analytics focuses on processing and performing statistical analysis of existing datasets. A data analyst concentrates on creating methods to capture, process, and organize data sets to uncover potential solutions to current problems. The other part entails presenting these possible solutions in the right format.

Therefore, you can think of data analytics as a Big Data career path directed at finding solutions to everyday problems.

Roles and Responsibilities

The roles and responsibilities of a data analyst vary depending on industry and even across companies. Fundamentally, however, data analysts analyze well-defined datasets using advanced tools to answer pertinent questions. This is a key factor in understanding the relationship in data analytics vs data science discussion.

For instance, at a clothing company, a data analyst can analyze existing data to determine why sales dropped in a particular year, why some regions performed better, and why some ad campaigns failed to deliver the desired ROI.

Other key roles of the data analyst include;

• Processing, cleaning and verifying data integrity
• Exploratory data analysis
• Gleaning business insight using machine learning
• Identifying new trends in data to predict future trends

Typical Background

The majority of data analysts have a background in mathematics and statistics. However, others enter the field from non-quantitative backgrounds by taking professional courses and learning the tools and methods necessary to make decisions with numbers. Many employers, however, want a degree (undergraduate or sometimes post-graduate) in data analytics.

Required Skills

Aside from getting a degree in data analytics, professionals in this field need to arm themselves with skills in mining/data warehouse, data modeling, SAS or R, SQL, database management, statistical analysis, and data analysis. Management and reporting is an added advantage.

Dexterity in Excel and experience working with Business Intelligence (BI) tools such as Power BI and Python knowledge are other added advantages. Most times, the skills discussed in data analytics vs data science debate relate in great extents.

A Career in Data Science

The main difference between data analytics and data science is scope. Data science is an umbrella term for a group of fields involved in mining large data sets. Meanwhile, analytics is a more focused process and may even be considered part of the data science family.

Another critical difference is exploration. Data science is more explorative than definitive. Unlike the data analyst, a data scientist isn’t concerned with answering specific questions. Instead, they explore massive data sets to see if they can find something useful. By contrast, the data analyst seeks specific answers to specific questions.

Finally, unlike data analytics that focuses on historical data, data science focuses more on machine learning and predictive modeling. The result is that the data scientist does a lot more coding than the analyst and works with a broader, more undefined data pool than the analyst.

Roles and Responsibilities

The data scientist is tasked with designing data modeling processes and creating algorithms and predictive models to extract information.

Other key roles of the data scientist include;

• Mining vast amounts of data for the analytics team
• Using advanced tools to mine data from multiple sources
• Using insight to influence how an organization approaches business challenges
• Making recommendations to adapt existing business strategies

Typical Background

A data scientist needs a background in mathematical and statistical knowledge, hacking skills, and substantive skills. Therefore, a degree in data science is often required. A degree in math or computer science is also sufficient.

Many data scientists also choose to pursue post-graduate degrees. A Master’s or Ph.D. gives you a competitive advantage.

Required Skills

In addition to a math/statistics degree, the data scientist is proficient in using Big Data tools such as Hadoop and Spark and can comfortably work with SQL and NoSQL databases such as Cassandra and MongoDB. Experience with visualization tools such as QlikView, Tableau, and D3.js are other advantages. A master of Python, R, and Scala are also considered vital.

Data Analytics Vs Data Science: Making The Right Decision

Ultimately, the decision is up to you. If you prefer a data analytics career and have the requisite background and skills, then go for it. The same applies to data science.

The only other thing we want to mention is salaries. Both data analysts and data scientists are paid very well. Data analysts take home between $83,750 and $142,500 per year, according to 2020 salary guides. You can even command a higher salary as your skillset improves. Data scientists take home even more money, ranging from $105,750 to $180,250 per year. If you’re ready to take the next step, sign up with plumlogixu.com and set off your career.