The Journey To Business Mastery: Entry 5

Are data science and data analytics the same thing?

I had not given this question any thought until the facilitator in my Data Analytics class mentioned that they were different. So naturally, I got curious and did a little digging. Here’s everything I found:

Although data analytics and data science are closely related, they are ultimately different. Both involve working with data to gain insights from it, but data analytics involves and focuses on the analysis of past data to inform decisions to be made in the future. Data analysts would employ the use of tools so they can prepare, combine and analyze existing datasets, identify patterns in them and develop actionable steps. On the other hand, data science involves the use of data to build models that can predict future outcomes. Data scientists employ the use of tools to examine and then prepare large datasets. Then they use programing, machine learning and algorithms to develop new data models. Fascinating, yes? I agree but it all sounds very complex.

Through my perusal of the internet, aside from their definitions, I also came across two more ways they can be differentiated. Let’s have a look.

In their approach to data: Data analysts prepare, manage, and analyze well defined datasets to identify patterns and create visual (and usually visually appealing) presentations to help businesses make proper data-driven decisions. Data scientists also prepare, manage, and analyze large datasets and then create custom data models and algorithms to produce the needed business insights. They also work closely with the business stakeholders, to be able to identify trends and spot issues. This helps them in offering the right solutions for the company in question.

Skills required: Included in the broad field of data analytics are Data analysis, data presentation and data integration. But data science is a multi-disciplinary field that includes machine learning, data engineering, predictive analysis, statistics and a few more.  

Some of the tools needed in the field of data analytics include Tableau, Microsoft Excel and Microsoft Power BI. Tools needed for data science on the other hand include a few programming languages such as Python, R and SQL. Its tools also include a few open-source data processing engines and even uses Excel, which was earlier listed as an analytics tool. This is because ultimately it deals with the analysis of data and quite a number of the functionalities present in Excel will be applicable. I’m currently of the belief that Excel is a way more powerful tool than I earlier believed it to be.

Looking into this topic has led me to realize just how important it is to have records as a business. Not keeping records would be detrimental to any business that was looking to survive beyond the week. Now, having records is one thing but it is also key that there’s someone on board who can interpret all that data, sift through the relevant and irrelevant, then develop solutions to wading the business tides when necessary. And trust me, it will be necessary.

Till next time, adios.