Data Science is a broad field so as its team but the question is why you want to build a data science team?Data Science has become the top growing job and its Entry-level salaries can range into six figures, and can lead to more job opportunities by 2020. We are going to mention main important aspects of a data science organization.
Although there aren’t any hard-and-fast rules, the core questions to keep in mind are generally:How should data scientist roles be defined?Where should data scientists report?Where should the data science function live?What should an organization do to set up data science for success?Here we have explained all the possible skills required to build a successful and highly effective team.
Data Science Organization Structure: Based on Michael Hochster’s taxonomy there are mainly two types of data scientists:Analysis one, which focuses on making sense of data through statistical analysis and one who focuses building and developing predictive models and algorithms to power data products. We have mainly three categories which defines the data science team structure such as: data scientists, data engineers and machine learning (ML) engineers. We will define these roles one by one below:
Data Engineers: Data engineers test, maintain, and build infrastructural components that data architects design. They maintain and create databases, machine learning and production processes. Data engineers don’t need particular skills of machine learning and statistics but they should be in the team when needed.Their preferred skills are as follows: SQL, noSQL, Hive, Pig, Matlab, SAS, Python, Java, Ruby, C++, and Perl etc.
Data Scientist: Data scientists is one who helps to solve business problems with the help of machine learning, data mining and using statistical techniques. They help in the modeling process of a business and then work with engineers and stakeholders to collect the data. Once all criteria have been identified the process of building tests, models, and evaluation starts. Data scientists need strong understanding of programming and statistics to leverage various mathematical problems and software tools. They also need strong common sense and business understanding to achieve desired results.Their Preferred skills are as follows: R, SAS, Python, Matlab, SQL, noSQL, Hive, Pig, Hadoop, and Spark etc.
Data Analysts: A data analysts monitor processes, evaluate data quality and monitor production model performance. As data science team grows, need of a data analysts become most important part of it. This becomes daily routines of an analyst and helps you to maintain the workflow in a streamlines way.Their Preferred skills are as follows: R, Python, JavaScript, C/C++, and SQL etc.
The Business Intelligence Analyst: If the data engineers are the black coffee then a BI analyst is the cream or sugar of a data science team. BI analyst process and raw material obtained from data engineers and helps in creating knowledge and insight from the raw information. If you data scientists lacks domain expertise then a BI analyst fill this gap.Their Preferred skills are as follows: data visualization, business intelligence, SQL etc.
Conclusion: If your business needs results or understanding of business relationships then data science can help. The demand for data scientists is growing day by day in the industry so businesses are focusing on building a great team. As soon as, these teams start to bear fruit, advantage over the competition can be significant.ing here…
Refrences:
https://www.datanami.com/2018/08/06/how-to-build-a-data-science-team-now/
https://www.kdnuggets.com/2018/09/winning-game-plan-building-data-science-team.html
https://www.altexsoft.com/blog/datascience/how-to-structure-data-science-team-key-models-and-roles/
https://towardsdatascience.com/what-is-the-most-effective-way-to-structure-a-data-science-team-498041b88dae
https://www.analyticsvidhya.com/blog/2018/01/ultimate-learning-path-becoming-data-scientist-2018/