{"id":6102,"date":"2020-05-27T11:34:00","date_gmt":"2020-05-27T06:04:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/difference-between-business-analyst-data-scientist\/"},"modified":"2024-09-03T15:01:21","modified_gmt":"2024-09-03T09:31:21","slug":"difference-between-business-analyst-data-scientist","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/difference-between-business-analyst-data-scientist\/","title":{"rendered":"Difference between Business Analyst & Data Scientist"},"content":{"rendered":"\n

Business analysts are responsible for a range of tasks including understanding business requirements, laying out plans and developing actionable insights. Data scientists, on the other hand, are professionals responsible for analysing, preparing, formatting, and maintaining information. Business analysis combines integrative skills like analytics, business acumen and domain knowledge, whereas data science involves skills pertaining to computer science, mathematics and statistics. <\/p>\n\n\n\n

Business Analyst<\/strong><\/td>Data Scientist<\/strong><\/td><\/tr>
Business analysts look into client and business requirement <\/td>Data Scientists primarily model and analyse data <\/td><\/tr>
Business analysts communicate with clients to understand business perspectives<\/td>Data Scientists delve into business generated data to extract meaningful insights<\/td><\/tr>
Business analysts work only with structured data<\/td>Data analysts work with both structured and unstructured data<\/td><\/tr>
Business analysts need to have statistical skills, excellent interpersonal skills and problem solving techniques<\/td>Data scientists need to have mathematical skills, knowledge of machine learning algorithms, and statistics<\/td><\/tr>
Business analysts need to know SQL, R, Tableau, and Excel<\/td>Data Scientists need to know Python, R, SAS, Spark, Tensorflow, Hadoop etc<\/td><\/tr>
Business analysts use models like schema on load <\/td>Data scientists use models like schema on query<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Difference between Data Science and Business Analytics<\/strong><\/h2>\n\n\n\n

Business analysts are professionals who look into the ever changing needs of any business and assist them in implementing those changes. They form a bridge of communication between various departments in a business organisation to execute any business plan.<\/p>\n\n\n\n

Data scientists are responsible for developing algorithms and drawing data inferences. Since data science aims at unveiling complex data patterns by studying and understanding data sets, it is important that data scientists are well versed in multidisciplinary skill sets.<\/p>\n\n\n\n

Both these roles are in fact, similar in a lot of ways, since both involve data gathering, inference accumulation and data modelling. The scope of data science<\/a> often overlap and the skill sets are not mutually exclusive. In any business environment, data scientists and business analysts work closely to understand and implement strategies. However, there are certain differences between these two branches that aspiring professionals must consider to understand which is best suited for them. Typically, data science can be taken up by early career professionals but business analytics is better suited for professionals with experience in business development, technology and project management<\/a><\/strong>.<\/p>\n\n\n\n

If there\u2019s one thing that has emerged as a force to be reckoned with in the world today - it\u2019s data. Data is driving and shaping modern businesses exponentially. However, data in itself doesn\u2019t hold much value for businesses unless it is analysed and categorised. Experts who dabble in data analytics can either be from a data science or a business analytics background. While both data scientists and business analysts are often seen working in close collaboration in a data driven environment, each of the roles involves different tasks and responsibilities. 
Both data science and analytics<\/a> are popular career choices for young professionals today. If the myriad ways in which data work fascinates you then you can choose from either of the two career paths after considering your educational background, experience, skills and interests. To help you choose a career path, we have listed down the essentials and requirements of each of these roles.<\/p>\n\n\n\n

Who is a Business Analyst<\/b><\/h2>\n\n\n\n

Let\u2019s look into a sample business analyst job description to understand the various tasks and responsibilities involved in the role-<\/span><\/p>\n\n\n\n

Job Overview of Business Analyst<\/strong><\/h3>\n\n\n\n

We are looking for a business analyst for finance who will be responsible for leading projects, improving processes and supporting systems used by the Finance and Accounting departments as business requirements evolve. The ideal candidate will work closely with business stakeholders from teams within Finance and Accounting in all geographies to understand business challenges. Additionally he\/she will document all business processes and requirements to meet those challenges. He\/she will drive process improvement ideas with a focus on - scoping, coordinating, planning, executing testing, and executing launch activities, and provide ongoing support. He\/she will also be responsible for triage and analysis of production support issues.<\/span><\/p>\n\n\n\n

Job Responsibilities in Business Analyst<\/strong><\/span><\/h3>\n\n\n\n