Data Scientist vs. Business Analyst

Comparisons between data scientists and business analysts are not very common. But surprisingly, they share many business goals. So, how they work towards these goals sets them apart. Data scientists work towards goals using statistical and mathematical lenses, while business analysts approach goals with a holistic approach. We will look at the work done by both professionals and cover the similarities and differences in this article.

Data Scientist vs. Business Analyst: The main difference

Business Analyst

For example, business analysts help identify business problems and opportunities and provide solutions for their companies. Suppose Business Analyst (BA) High customer churn identifies the problem. Thus, BA will identify all stakeholders involved, evaluate the organization’s current processes, set timelines, define measurable objectives, express requirements, and document. They also work to get the necessary approvals. Business analysts are domain experts who collaborate with data professionals to solve business problems – churning problems.

Once the data scientist provides insights – a process of identifying customers who will brainstorm – business analysts use specific tools and Business analyst skills To evaluate insights from business perspectives, market trends, inconsistencies and other key business interests. They then report on the steps needed to prevent business visualization and churning. These insights are then communicated to business stakeholders and in collaboration, business analysts assist in decision making, planning and implementation of organization changes. They are responsible for creating the user manual and final documentation.

Business Analyst – Goals

  • Provide possible and economical solutions to business problems within the given time frame
  • They work with all stakeholders involved who are catalysts for change
  • Define a roadmap and create a workable plan
  • Set KPI
  • Manage, implement changes and documentation work

Business Analyst – Skills Need

  • Visualization tools like Excel, MS Office, SQL, Mooknatya, Power BI
  • Cumin, Trelo, VCO, Pencil
  • Problem solving and analytical thinking
  • Communication and interaction
  • Leadership and negotiation skills

Data Scientist

Information scientists Prepare a large amount of business information for analysis. Provides insights into initial data search and mining information, which is helpful when creating models. So, Machine learning Algorithms, statistics, artificial intelligence and much more are used to solve business problems. Data scientists can use regression analysis to make predictions and predictions.

Thus, data scientists can prepare and model a model for predicting customers who will suggest corrective action before the customer leaves. Therefore, data scientists interact with business analysts and other stakeholders on technical results.

Data Scientist – Goal

  • Use data and machine learning algorithms to solve business problems.
  • Make business more efficient by interpreting data with actionable insights.
  • Create the right solution.

Data Scientist – Skills Need

  • Statistics, Python, R, SQL, NoSQL, Hadoop, Spark, etc.
  • Machine learning algorithm
  • Big data technique
  • Data Visualization (Tables, Power BI)
  • Problem solving, analytical thinking, communication
  • Business knowledge

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Both professionals work to recommend solutions to business problems. They work with stakeholders on solutions, outcomes and effects. The role of a business analyst may vary in different organizations.

IIBA has given a new definition Introduction to Business Data Analyst, Who bridges the gap between business and actual mathematical-statistical data analysis. Thus, a business data analyst understands the big picture and the business context is a dreamer who sets an attitude about the breadth and breadth of the analytics initiative and is a business domain expert with data analysis knowledge. Business data analysts act as advisors to data scientists, guiding their analysis with business expertise. They use Data visualization And Data telling stories To involve decision makers and drive a data-driven culture.

Find out more about how to get started as a Certified Business Data Analyst (CBDA).

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