Business Analytics Vs Data Science



Business Analytics vs Data Science.


Business Analytics and Data Science, both are two different terms which are always debatable by a layman. However a professional should use this correct as the impact of this on a business is definitely very large.


In this article we will go thru the difference between the two in detail.


In simple words:


"Business Analytics is analysis of Data to make Business decisions for an organization."


"Data Science is study using Data using statistics which provides key insights but not busniess changing decisions."


Business Analytics Data Science


Analysis of Data Data study using statistics.


Uses Structured Data Structured/Unstructured Data


No Coding is involved Coding is involved to derive from Algorithm


Future is Coginitive Analysis Future is AI and Machine Learning


Useful for Business decisions. Not useful to take Business decisions.


Both the above streams are nische skills and have very competitive markets with very handsome packages to the professionals.


Business Analytics:


Statistical study of Data, mostly on structured data is known as Business Analytics. It provides solutions to many business problems in an organization. Business Anaytics is vital in our digitally-driven world as it essentially gives you an additional sense: a commercial vision that can help you see and process far more than the information that presents itself on the surface. And there are business examples and insights out there that demonstrate that every notion.


Benefits of Business Analytics:

  1. Improves Business decision making

  2. Enhances Productivity

  3. Unleash the Business Insights

  4. Boosts Finances

  5. Increase in Sales

  6. Streamline business processes

  7. Motivates Employee Performance

  8. Better Inventory Control

  9. Insights on Marketing Strategies

  10. Customer Focus.

Data Science:


The term science is usually synonymous with the scientific method, and some of you may have noticed that the process is very similar to the process characterized by the expression, scientific method.


Data Science Life Cycle:

  1. Make Observations

  2. Think of Interesting Questions

  3. Formulate Hypotheses

  4. Develop Predictions

  5. Gather Data for Predictions

  6. Develop Theories.

  7. And refine the process from step 1 again.

For statistics, mathematics, algorithms, modeling, and data visualization, data scientists usually use pre-existing packages and libraries where possible. Some of the more popular Python-based ones include Scikit-learn, TensorFlow, PyTorch, Pandas, Numpy, and Matplotlib.


Data scientists can have a major positive impact on a business’ success, and sometimes inadvertently cause financial loss, which is one of the many reasons why hiring a top notch data scientist is critical.


Benefits of Data Science:

  1. Identify and predict problem areas

  2. Customized recommendations

  3. Optimize business process

  4. Automate predictions

  5. Helps in Mitigation of problem areas.

By now you should have got a fair amount of data to differentiate the above, if you still have any specific question I am more than happy to take some questions from the comments section.


ciao,

Rajeev Jagatap,





Business Analytics Consultant










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Mail me :  upgrade@rajeevjagatap.com | Data Analytics 

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