Definition:
The two, Data Science and Business Intelligence spin around data. In any case, while Data Science is the greater pool containing more noteworthy data, Business Intelligence can be thought of as a piece of the master plan. Moreover, Business Intelligence is restricted in the extent of the business area. BI is tied in with creating dashboards, making business insights, sorting out information from data and organising it, that would assist the organizations with growing.
In any case, Data Science utilizes a wide exhibit of complex statistical calculations and algorithms and predictivemodels. Data Science is significantly more complex compared with Business Intelligence. In business intelligence, past data is examined to comprehend the current patterns of the business. However, in Data Science, we use the data's information to make future predictions and estimate it for development of the business.
The tools of business intelligence are likewise constrained to the managing of the data and curation of business procedures. Moreover, the tools of a data scientist include complex algorithmic models, data handling and processing and even big data tools. While BI centers around producing reports dependent on the internal organized data, Data Science centers around creating insights out of the data. These bits of knowledge are created by using complex predective analytics and the output isn't a report however an data model. This data model is a predictive stage that utilizes Machine Learning to increase future insights and catch patterns in the data.
Skills:
Some of the important skills required for Business Intelligence are –
1. Possession of creative thinking and strong business acumen.
2. Ability to perform problem-solving.
3. Knowledge of data analysis to make business decisions.
4. Excellent communication and presentation skills.
5. Ability to extract data using SQL.
6. Well versed with various ETL (Extract, Transform, Load) tools.
Following are the skills required for Data Science –
1. Well versed with tools like Python, R, SAS etc.
2. Able to perform complex statistical analysis of data.
3. Ability to visualize data through tools like Tableau, Matplotlib, ggplot2 etc.
4. Should be able to deal with both structured and unstructured data.
5. Proficiency in both SQL and NoSQL.
6. Knowledge of Machine Learning algorithms
7. Familiarity with tools of big data like Hadoop and Spark.
Responsibilities:
Some of the key responsibilities of working in business intelligence are –
1. Identification of the source system and engagement in business connectivity
2. Focus on key business areas and resolution strategies.
3. Working with the project managers and clients to define business requirements.
4. Performing validation on the data.
5. Implementing approved projects and delivering strategic results.
6. Reporting progress of the BI program.
A Data Scientist is responsible for the following –
1. Preprocessing and transforming the data.
2. Development of predictive models that forecast future events.
3. Fine-tuning the machine learning models and optimizing their performances.
4. Assisting the industries to identify questions required to be solved.
5. Using story-telling for visual communication of results.
