“Data analytics is different from Data science“, is a thought that has stayed with me from our last Data Analytics (DA) class we had with Professor Bongi Adi. How is Data analytics different from Data science?
Data science is a more comprehensive field that comprises various ways of handling and managing complex and unstructured data. It is the umbrella of all things data, including data engineering, machine learning, data mining, data analytics and more. It is about extracting meaningful information and gaining insights by using algorithms and models to create and frame data. Data Analytics is more specific, it is focused more on interpreting and visualizing historical data to help in making strategic business decisions. Data Analytics involve using tools like Excel and PowerBI to analyse the data and present it in a way that communicates findings to business managers and stakeholders in an organisation.
Data science and Data analytics are both related but refer to different concepts and have different objectives. Data scientists aim to gather and retrieve data and develop algorithms to uncover hidden patterns within large datasets, while Data Analysts seek to query and visualize existing datasets to make informed decisions, identify opportunities and draw conclusions. It seems to me that Data Science is more about the How of data and Data Analytics is about the What of data. However, from my studies, I see that it is more nuanced than that. This distinction only provides a simple perspective on the matter, while the roles and responsibilities of a data scientist and analysts vary, it is important to understand there is often an overlap between the two. They are intertwined.
Data Scientists work with databases using tools like:
- PyTorch
- TensorFlow
- SQL
- Apache Spark
- Python
- KNIME
The software/tools Data Analysts use for interpreting data include:
- Microsoft Excel
- PowerBI
- R
- Python
- Microsoft PowerBI
- Tableau
So far in our DA class, we have had sessions on SQL, PgAdmin, Microsoft Excel and PowerBI, It has been more practical than theory, and a bit fast-paced if I should be honest. I guess it is because we have so much to cover. Our faculty has explained that the essence of these classes is to introduce us to these tools to a point where we can understand the basics and take it up from there. Emphasis has been laid on how to progress forward in mastery of these tools, we must put in more hours of practice beyond the class. This has been such a learning curve for me especially because my background is not in analytics or computer science.
To conclude, I am still learning to understand how some of these tools work and how to apply them to my business. I believe the key is to expand the mind and be familiar with the back end of many business models. Afterall, digitization and technology are vital for businesses in today’s modern world. The aim is not to become a Data Analyst or Data Scientist overnight but to gain sufficient knowledge to enable one to stand out and be relevant in any organization.
#MMBA5
