Career as a Business Analyst vs Data Scientist

Career Coaching
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#1

With the complexity and evolving use cases, the dynamics of both the jobs will diverge year on year. Yet there’s a common point. Let me begin my opinions on common points:

  1. Ability to visualise raw data, explore raw data
  2. Strong foundation in stats, analytics domains
  3. Strong domain understanding ability

Where they differ:
While business analyst would be more concerned with framing the right sets of problem that will bring out the full picture view, a data scientist would be focused on analysing the data the right way.

Which path you should chose?
That’s a personal opinion and choice. See what excites you the most, painting the story out of the data or getting hands on to the analysis. Both are equally valued, however that also depends company to company.

#2

Understanding company requirements, sketching up plans, and providing actionable insights are all duties that business analysts are responsible for. Data scientists, on the other hand, are experts in the fields of data analysis, preparation, formatting, and maintenance. Data science entails computer science, mathematics, and statistics skills, whereas business analysis integrates integrative skills such as analytics, business acumen, and domain expertise.

Business Analyst:

  1. Client and business requirements are investigated by business analysts.
  2. Clients are communicated with by business analysts in order to have a better understanding of their business
  3. Only structured data is used by business analysts.
  4. Statistics expertise, great interpersonal skills, and problem-solving strategies are all required by business analysts.
  5. SQL, R, Tableau, and Excel are all skills required of business analysts.
  6. Schema on load models are used by business analysts.

Data Scientist:

  1. Data scientists are largely responsible for modelling and analyzing data.
  2. Data scientists sift through business-generated data in order to glean useful information.
  3. Both structured and unstructured data are used by data analysts
  4. Mathematical skills, knowledge of machine learning methods, and statistics are all required of data scientists.
  5. Python, R, SAS, Spark, TensorFlow, Hadoop, and other programming languages are required of data scientists. On query, data scientists employ models like schema.