Data analytics is a part of the business all around the globe and plays a significant role in exponential growth. Big data has provided a chance to significantly improve operations and meet targets opening career choices such as Data Scientist and Business Analyst.
Raw data is complex. Its trends have to be understood by analysis and research. The data patterns have to be followed using special skills to determine ways to grow businesses and functionalities that will bring the necessary change. Data scientists and business analysts mutually carry out this task. Though both help in field expansion, they have their roles and responsibilities. We will understand these differences and the variance in the work they do. This article will let you choose between a data science certification and a data analytics certification if you want to make a career in data analytics.
Learning of the Blog
- Overview
- Business Analyst
- Data Scientist
- Differences
- Final Words
Quick suggestion: You can enroll in data science for beginners course by a data science expert as it explains the two roles and provides the methodology to decide between the two paths based on skills, education, and more.
Overview
Data Science and Business Analytics- both the industries are experiencing skyrocket growth and are often used interchangeably. The current market size is 38 and 67 billion dollars, respectively, and is expected to grow to 140 and 100 billion dollars by 2025. This implies a surge in demand for the two profiles. Both careers are rewarding. Let us first look at the job profiles and then study them in-depth. Business analysts understand business requirements, layout plans, and develop actionable insights. On the other hand, Data scientists analyze, prepare, format, and maintain information. Data scientists have skills pertaining to mathematics, statistics, and computer science, whereas business analysts have combined integrative skills such as domain knowledge and business acumen.
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Business Analyst
Business analysts extract information from large data sets containing both structured and unstructured data. The data’s role is to help decision-makers understand the company’s past performance and current position along with future performance forecasts. They develop the right analytical model for presenting information to a business leader, aiding them to steer the company in the right direction. Education is statistical analysis, and data management forms the foundation of business analytics. Data visualization is a critical component of the job. The available job profiles are business analysts, senior data analyst, and business systems analyst.
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Data Scientist
Data scientists develop more technical skills in areas of data collection and analysis because they work more on the front end of the data collection and business analysis. They design, develop, and deploy algorithms to collect and analyze data. A business analyst looks for data trends, and a data scientist looks at what drives that trend. Mathematics and Machine Learning form the basics of education for data scientists. Data science professional certificate can help candidates become proficient in the coding algorithm used for predictive analysis and connector algorithms for different data sets. Jobs in the field vary from research scientist to senior data analyst.
Business Analyst vs Data Scientist
The following table provides a comparison between a Business Analyst and a Data Scientist.
| Business Analyst | Data Scientist |
| A business analyst looks into client and business requirements. | Data scientists primarily models and analyses data. |
| Business analysts communicate with clients to understand business perspectives. | Data Scientist delves into business generated data to extract meaningful insights. |
| A business analyst works only with structured data. | Data analyst works with both structured and unstructured data. |
| A business analyst needs to have statistical skills, excellent interpersonal skills, and problem-solving techniques. | A data scientist needs to have mathematical skills, knowledge of machine learning algorithms, and statistics. |
| A business analyst needs to know SQL, R, Tableau, and Excel. | Data Scientist needs to know Python, R, SAS, Spark, Tensorflow, Hadoop, etc. |
| The business analyst uses models like schema on load. | A data scientist uses models like schema on a query. |
| Business Analysts came to the rising in the 1970s when they started documenting all manual processes. There was a need to automate repetitive tasks, identify problems, and deliver quality technology at the expense of business needs. In the 1980s, Business Analysts evolved to support business goals and be a mediator more effectively between IT resources and business resources. | Data analysis dates back to 1962, when John Tukey wrote about ‘The Future of Data Analysis.’ It started trending from 2006 to 2011 until now that data scientists are the most demanding job profiles. |
| Job requirement- Engineering/MCA or MBA, BBA with 4-5 years of experience. | Job Requirement- Bachelor’s degree in Computer Science, Data Analytics, Maths with a 3+ year experience in analytics. |
| An entry-level analyst can expect six lakhs per annum. | Fresher salary is 6 to 7 lakhs per annum. |
| Various tools are Blueprint, Bit impulse, and Azure. | Data scientists’ tools are none other than Data warehousing, Data visualization, and machine learning. |
| Business Analyst leads client training sessions and provides end-user feedback to the product team. | Data Scientist automates reporting for weekly business metrics. |
| Extra Skills- Strong process analysis, Documentation skills. | Extra Skills: Natural Language Processing, AWS knowledge. |
| Problem statement- To build a business plan to decide how many employees a bank needs to do a particular amount of business in 2021. | Problem Statement- To build a fraud detection model. |
Conclusion
We have seen the differences between the two fields. In general, the job role of both is to increase the business value by providing ways of improvement and growing market value. Both are wise career choices, as data-driven decisions are needed for most businesses. Any discipline that fits a person’s skill set can be a rewarding career path. The predictions by a data scientist and process improvements by business analysts assist the company to have a bright present and a brighter future.
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