Data Science is the fact of collecting, analyzing, and interpreting large amounts of data for driving insights, to help a business improve, using advanced technologies. A term coined by DJ Patil and Jeff Hammerbacher in 2008, as the first claims, to define their jobs at Linkedin and Facebook.
A Data Scientist is someone who is data-driven with high-level technical skills, capable of building complex algorithms using large amounts of data, to answer questions and drive strategy in the organization. The responsibilities and main tasks of a Data Scientist may vary from a company to another, depending on its size, its type, etc. The role of a Data scientist can include Data Collection, Cleansing, Analytics, and Data Visualization in a small company, just ML Algorithms in a big company, and all the below in a medium-sized company.
But how a Data Analyst is different?
There is a lot of grey area with many job titles that may not always be accurate in reflecting what is one’s actual job and responsibilities. A Data Analyst may deal with many similar tasks like a Data Scientist (working with data, writing queries, analyzing and deriving information, etc.), and the skills of both may overlap (knowing SQL, R/Python, Tableau, mathematics, algorithms, Data visualization, etc.), however, the two are not the same:
While the Data Analyst works with SQL and BI tools to answer questions from the Business team by curating the insights from data, the Data Scientist builds statistical models, works with Machine Learning, and formulates questions that will help the business, by predicting the future based on past patterns.
A Data Scientist is someone who is better at statistics than any Data/Software Engineer, and better at engineering than any Analyst/Statistician.
On the other hand, here are 9 must have skills for a Data Scientist by kdnuggets:
- R Programming
- Python Coding
- Hadoop Platform
- SQL Database/Coding
- Apache Spark
- Machine Learning and AI
- Data Visualization
- Unstructured data
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