Business Analytics & Data Science
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Business Analytics & Data Science ( Sept 23)

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About Course

Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms, and technology. Uses mostly structured data. Uses both structured and unstructured data.

What is business analytics? Business analytics bridges the gap between information technology and business by using analytics to provide data-driven recommendations. The business part requires deep business understanding, while the analytics part requires an understanding of data, statistics and computer science.

What does a business analyst do? According to LinkedIn Talent Solutions, a business analyst acts as a communicator, facilitator and mediator, and seeks the best ways to improve processes and increase effectiveness through technology, strategy, analytic solutions, and more.

What is data science?

Data science is the study of data using statistics, algorithms and technology. It is the process of using data to find solutions and predict outcomes for a problem statement.

What does a data scientist do?
Data scientists apply machine-learning algorithms to numbers, text, images, videos and audio, and draw various understanding from them. According to Hugo Bowne-Anderson writing in the Harward Business Review, “Data scientists lay a solid data foundation in order to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth. "Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions. In others, in tech, data science is about infrastructure, testing, machine learning, decision-making, and data products.” 

What are the skills of a data scientist? The core skills required in data science are as follows:

  • Statistical analysis: You should be familiar with statistical tests, likelihood estimators for a keen sense of pattern and anomaly detection.
  • Computer science and programming: Data scientists encounter massive datasets. To uncover answers to problems, you will have to write computer programs and should be proficient in computer programming languages such as Python, R and SQL.
  • Machine learning: As a data scientist, you should be familiar with algorithms and statistical models that automatically enable a computer to learn from data.
  • Multivariable calculus and linear algebra: This significant mathematical knowledge is needed for building a machine learning model.
  • Data visualization and storytelling: After you have the data, you have to present your findings. Data scientists use data visualization tools to communicate and describe actionable insights to technical and non-technical audiences.

Course Curriculum


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