Explore the need for data science, its definition, and how it differs from business intelligence.
Computer science
Requirements: | .doc file | Assembly Language
Data Science 1. Data Science: Understanding the Buzz 2. Welcome to this session on data science. In this article, we will explore the need for data science, its definition, and how it differs from business intelligence. We will also discuss the prerequisites for learning data science and the activities performed by a data scientist on a daily basis. Additionally, we will look at the data science lifecycle and the increasing demand for data scientists. 3. What is Data Science? 4. Data science is a field that involves using statistical and computational methods to extract insights and knowledge from data. It involves a range of techniques, including data mining, machine learning, and predictive analytics, to uncover patterns and relationships in data. 5. Examples of Data Science Applications 6. Data science has a wide range of applications across industries. For example, in the automotive industry, self-driving cars rely heavily on data science to make decisions such as when to speed up or slow down, when to turn, and when to apply the brakes. In the airline Industry, data science can be used to improve route planning, predict delays, and make promotional offers. In logistics, companies like FedEx use data science models to optimize delivery routes and cut costs. 7. What Does a Data Scientist Do? 8. A data scientist is responsible for collecting, cleaning, analyzing, and interpreting data to extract insights and knowledge. They use a range of tools and techniques to work with data, such as programming languages like Python and R, statistical models, and machine learning algorithms. Data scientists also communicate their findings to stakeholders and use their insights to inform business decisions. 9. The Data Science Lifecycle
10. The data science lifecycle involves several stages, including problem formulation, data collection, data preparation, data analysis, model building, model evaluation, and deployment. Each stage requires specific skills and techniques, and data scientists must be proficient in all of them to be effective. 11. Conclusion 12. Data science is a rapidly growing field with applications across industries. By using statistical and computational methods to extract insights and knowledge from data, data scientists can help businesses make better decisions and improve their operations. If you are interested in learning data science, make sure to have a strong foundation in programming, statistics, and mathematics. 13. Steps in Data Science 14. Data science involves several steps, including: 15. Asking the right question and exploring the data 16. Performing exploratory analysis on the data 17. Modeling the data and selecting appropriate algorithms 18. Training the model and running the data through the process 19. Visualizing and communicating the results to stakeholders 20. Business Intelligence vs. Data Science 21. Business intelligence and data science differ in several ways: 22. Data source: Business intelligence primarily uses structured data, while data science also incorporates unstructured data. 23. Method: Business intelligence is primarily analytical, while data science goes deeper to understand why certain behaviors occur and seeks to provide deeper insights. 24. Skills: Business intelligence requires some statistical knowledge but primarily focuses on visualization, while data science requires both statistical and visualization skills. 25. Focus: Business intelligence focuses on historical data, while data science combines historical data with other information to predict future trends. 26. Prerequisites for Data Science 27. There are three essential traits required for a data scientist:
28. Curiosity: Asking the right questions is crucial to obtaining accurate results. 29. Common sense: Creativity is required to find ways to use incomplete or missing data. 30. Communication skills: Results must be communicated effectively to stakeholders. 31. Additional Prerequisites 32. Two additional prerequisites for data science are: 33. Machine learning: A strong understanding of machine learning is necessary for data science. 34. Modeling: The ability to identify appropriate algorithms and models and to train them is also necessary.
Collepals.com Plagiarism Free Papers
Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.
Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS
Why Hire Collepals.com writers to do your paper?
Quality- We are experienced and have access to ample research materials.
We write plagiarism Free Content
Confidential- We never share or sell your personal information to third parties.
Support-Chat with us today! We are always waiting to answer all your questions.
