Data Science and Data Analysis
The recent boom in the data industry has driven the demand for data science professionals at enterprise-level, across all industry verticals. There are job openings for data scientists, data engineers, and data analysts. And there seems to be a lot of confusion and varying opinions among people regarding the roles and skillsets driving this field. Although all these job titles sound similar and are related to data the devil is in the details.
What Distinguishes a Data Scientist from a Data Analyst?
Unfortunately, there are no defined skill-sets that can distinguish between the role of a ‘Data Scientist and Data Analyst. In fact, different companies have different definitions for both these roles, and there is a lot of grey area in between the two job titles.
Broadly analyzing, a Data Scientist is a professional who combines data handling and data visualization with sound business understanding to make smart business decisions. A data scientist is expected to deliver business impact and take insights from the raw, chaotic data thereby uncovering answers to the problems we did not know existed. Data science as a job profile demands skills such as data structuring, data mining, data visualization, analytical skills, programming skills, machine learning skills, and customer insights
The role of a data analyst, on the other hand, is to summarize data and provide futuristic inputs by identifying consistent patterns from the past and the current data. The primary role of a data analyst is to collect, curate, process, and arrange data from different sources. They are responsible for presenting data in the form of charts, graphs, and tables and use this structured data to build relational databases for companies.
Data Science and Data Analysis – Two Wheels of the Same Barrel
Although there is a difference in the job responsibility of a data scientist and a data analyst, these two fields are exceptionally interconnected. They often work in close coordination to achieve the same goals i.e. of growth and development. For someone who aspires to become a data analyst, it is essential to understand the nuances of data science.
Advice from a Data Scientist for Data Analysts
Learn the Art of ‘Data Storytelling’
Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” –Stephen Few
Too often data storytelling is understood as effectively presenting data with visually-appealing data charts. However, data storytelling is much more than that. It is the art of weaving a rational story with clear logic that can strike the right chord with the stakeholders and give them enough insights to drive a decision.
More than the data presented, it depends on how the data is presented to a non-technical audience. Data storytelling follows a structured approach that involves a combination of 3 crucial elements, which are data, visuals, and narration.
As a data analysts, it is important that you learn the art of storytelling. The key skills required in a great storyteller are:
Knowing the audience and weaving the story to their understanding
Clearly understanding the business problem and the solution derived
Getting the right data at hand
Strong presentation skills
Analyzing probable questions and preparing answers for them