Data Scientist

A Data Scientist is an data practitioner who can perform data science tasks (using data science systems).

• Context:
  • They can (typically) have Skill in Pattern Recognition through data clustering and time-series analysis.
  • They can (typically) have Skill in Predictive Modeling through supervised learning.
  • They can (typically) have Skill in Statistics through hypothesis testing and statistical software.
  • They can (typically) have Skill in Programming through Python programming and script programming.
  • They can (typically) have Skill in Data Processing through data transformation and data cleaning.
  • They can (typically) have Knowledge of Applied Statistics through hypothesis testing and statistical inference.
  • They can (typically) have Knowledge of Predictive Modeling Algorithms through supervised learning and unsupervised learning.
  • They can (typically) perform Model Evaluation Tasks through measurement frameworks.
  • They can (typically) conduct Statistical Testing through model comparisons.
  • ...
  • They can (often) have skill in Prototyping through Scripting Languages.
  • They can (often) have skill in Large-Data Querying through database frameworks like Apache Hive.
  • They can (often) have skill in Large-Data Processing through data processing frameworks like Apache Spark.
  • They can (often) have skill in Communicate of Findings (to product, engineering, and management teams).
  • They can (often) specialize in a Data Mining Subtask.
  • They can (often) design Evaluation Metrics for AI systems.
  • They can (often) develop Evaluation Frameworks for AI capability assessment.
  • They can (often) communicate Analysis Findings to product teams, engineering teams, and management teams.
  • ...
  • They can range from being a Data Science Worker to being a Data Science Hobbyist, depending on their employment context.
  • They can range from being an Inexperienced Data Scientist to being an Experienced Data Scientist, depending on their professional experience.
  • …
• Example(s):
  • Industry Data Scientists, such as:
    • Product Data Scientists for product improvement.
    • Marketing Data Scientists for customer behavior analysis.
    • Financial Data Scientists for risk assessment.
    • Healthcare Data Scientists for patient outcome prediction.
  • Research Domain Data Scientists, such as:
    • Natural Language Processing Data Scientists for text analysis.
    • Computer Vision Data Scientists for image processing.
    • Protein Interaction Data Scientists for biological system analysis.
    • Genomics Data Scientists for genetic pattern analysis.
  • Platform Data Scientists, such as:
    • Recommendation System Data Scientists for content suggestion.
    • Search Engine Data Scientists for query optimization.
    • Social Network Data Scientists for user engagement analysis.
    • E-commerce Data Scientists for purchase pattern analysis.
  • Competitive Data Scientists, such as:
    • Data Science Competition Participants for algorithmic challenges.
    • Kaggle Competition Data Scientists for predictive modeling contests.
    • Analytics Olympiad Participants for statistical problem solving.
    • Hackathon Data Scientists for rapid prototyping.
  • ...
• Counter-Example(s):
  • Data Analysts.
  • Machine Learning Engineer, who may focus on model development rather than evaluation methodology.
  • AI Researcher, who may emphasize theoretical advancement over practical measurement.
  • AI System Developers, who may lack deep statistical testing knowledge.
  • Business Intelligence Report Writers, who may lack pattern recognition concepts.
  • Statisticians, who may lack important computer science concepts.
  • Computer Systems Analysts, who may lack important statistical concepts.
  • Software Programmers, who may lack important statistical concepts.
  • Machine Learning Researchers, who may not be a practitioner.
  • Database Administrators.
• See: Data Munging, Data Science Course.

---

---

References

2025-01-21

• Perplexity.ai
  • Question: What are the responsibilities and required skills of a Data Scientist? Please explain their core responsibilities, essential skills, and role evolution.
  • Answer: A Data Scientist is a professional who collects, analyzes, and interprets large amounts of data using analytical, statistical, and programming skills to develop solutions for organizations.
    • Core Responsibilities:
      • Data Collection and Processing:
        • Extract data from multiple sources and identify valuable data sources.
        • Clean, validate, and preprocess both structured and unstructured data.
        • Develop data collection procedures and maintain data integrity.
      • Analysis and Modeling:
        • Analyze large datasets to identify patterns and trends.
        • Create predictive models and machine learning algorithms.
        • Use statistical methods and AI tools to automate processes.
        • Develop comprehensive analytical solutions from data gathering to visualization.
      • Business Impact:
        • Propose solutions and strategies for business challenges.
        • Collaborate with business teams to understand objectives.
        • Present findings using data visualization techniques.
        • Work with sales teams on cost optimization and effort estimation.
    • Essential Skills:
      • Technical Competencies:
        • Programming languages (Python, R, SQL).
        • Statistical analysis and mathematics.
        • Machine learning and artificial intelligence.
        • Data visualization tools (Tableau, Power BI).
        • Cloud computing platforms.
      • Business Skills:
        • Problem-solving abilities.
        • Communication and presentation skills.
        • Business understanding and domain knowledge.
        • Project management capabilities.
    • Role Evolution:
      • The data scientist position has evolved from the data analyst role, with additional responsibilities including business sense and advanced communication skills.
      • They work across various sectors, including finance, consulting, manufacturing, pharmaceuticals, government, and education.
  • Citations:

[1] https://www.simplilearn.com/data-scientist-job-description-article
[2] https://www.visier.com/blog/essential-data-scientist-skills/
[3] https://careers.societegenerale.com/en/tips-candidates/what-does-data-scientist-do
[4] https://csweb.rice.edu/academics/graduate-programs/online-mds/blog/data-science-skills
[5] https://emeritus.org/in/learn/what-are-the-roles-and-responsibilities-of-a-data-scientist/
[6] https://www.simplilearn.com/what-skills-do-i-need-to-become-a-data-scientist-article
[7] https://www.splunk.com/en_us/blog/learn/data-scientist-role-responsibilities.html
[8] https://www.geeksforgeeks.org/top-7-skills-required-to-become-a-data-scientist/
[9] https://und.edu/blog/data-science-roles-and-responsibilities.html
[10] https://business.linkedin.com/talent-solutions/resources/how-to-hire-guides/data-scientist/job-description
[11] https://graduate.northeastern.edu/knowledge-hub/what-does-a-data-scientist-do/
[12] https://www.datacamp.com/blog/top-15-data-scientist-skills
[13] https://www.datascience-pm.com/data-science-roles/
[14] https://www.tableau.com/learn/articles/data-science-skills
[15] https://www.reddit.com/r/datascience/comments/y78uss/what_technologiesskills_should_a_data_scientist/
[16] https://www.careervillage.org/questions/703145/i-want-to-be-a-data-scientist-what-skills-do-i-need

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Data_science Retrieved:2018-7-25.
  • Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. When Harvard Business Review called it "The Sexiest Job of the 21st Century", the term "data science" became a buzzword, and is now often applied to business analytics, business intelligence, predictive modeling, or any arbitrary use of data, or used as a glamorized term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as "data science" to be more attractive, which can cause the term to become "dilute[d] beyond usefulness." While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To its discredit, however, many data science and big data projects fail to deliver useful results, often as a result of poor management and utilization of resources.

2018b

• https://linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal
  • QUOTE: ... We decided to restructure data science along three tracks. These described what we were looking for and are areas we want to attract talent. The Analytics track is ideal for those who are skilled at asking a great question, exploring cuts of the data in a revealing way, automating analysis through dashboards and visualizations, and driving changes in the business as a result of recommendations. The Algorithms track would be the home for those with expertise in machine learning, passionate about creating business value by infusing data in our product and processes. And the Inference track would be perfect for our statisticians, economists, and social scientists using statistics to improve our decision making and measure the impact of our work. …

2015

• http://dataconomy.com/the-22-skills-of-a-data-scientist/
  • QUOTE: To recap: Data Business People (DB) are leaders and entrepreneurs. Data Creatives (DC) are multi-talented artists and hackers. Data Developers (DD) are programmers and engineers. Data Researchers (DR) are scientists and statisticians. ...
    • Algorithms (ex: computational complexity, CS theory) DD,DR
    • Back-End Programming (ex: JAVA/Rails/Objective C) DC, DD
    • Bayesian/Monte-Carlo Statistics (ex: MCMC, BUGS) DD, DR
    • Big and Distributed Data (ex: Hadoop, Map/Reduce) DB, DC, DD
    • Business (ex: management, business development, budgeting) DB
    • Classical Statistics (ex: general linear model, ANOVA) DB, DC, DR
    • Data Manipulation (ex: regexes, R, SAS, web scraping) DC, DR
    • Front-End Programming (ex: JavaScript, HTML, CSS) DC, DD
    • Graphical Models (ex: social networks, Bayes networks) DD, DR
    • Machine Learning (ex: decision trees, neural nets, SVM, clustering) DC, DD
    • Math (ex: linear algebra, real analysis, calculus) DD,DR
    • Optimization (ex: linear, integer, convex, global) DD, DR
    • Product Development (ex: design, project management) DB
    • Science (ex: experimental design, technical writing/publishing) DC, DR
    • Simulation (ex: discrete, agent-based, continuous) DD,DR
    • Spatial Statistics (ex: geographic covariates, GIS) DC, DR
    • Structured Data (ex: SQL, JSON, XML) DC, DD
    • Surveys and Marketing (ex: multinomial modeling) DC, DR
    • Systems Administration (ex: *nix, DBA, cloud tech.) DC, DD
    • Temporal Statistics (ex: forecasting, time-series analysis) DC, DR
    • Unstructured Data (ex: noSQL, text mining) DC, DD
    • Visualisation (ex: statistical graphics, mapping, web-based data-viz) DC, DR

2014

• http://www.experfy.com/jobs/915-Data-Scientist
  • QUOTE: As a Twitter Data Scientist specializing in analysis, you will use statistical analysis and data mining techniques to better understand how users engage with Twitter, participate in creation and measurement of new and experimental features, and define meaningful success metrics for Twitter products. You should be passionate about finding insights in data and using quantitative analysis to answer complex questions. You should have a strong background in statistics and data analysis. Experience in modeling, machine learning, and working with large datasets is a plus.

    Responsibilities

    • Conduct statistical analyses to learn from and scale to petabytes of data
    • Use Map-Reduce frameworks such as Pig and Scalding, statistical software such as R, and scripting languages like Python and Ruby
    • Write and interpret complex SQL queries for standard as well as ad hoc data mining purposes
    • Communicate findings to product, engineering, and management teams

2013

• http://wikipedia.org/wiki/Data_science
  • … A practitioner of data science is called a data scientist. The term was coined by DJ Patil and Jeff Hammerbacher. Data scientists solve complex data problems through employing deep expertise in some scientific discipline. It is generally expected that data scientists are able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects are not required. However, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. There is probably no living person who is an expert in all of these disciplines - if so they would be extremely rare. This means that data science must be practiced as a team, where across the membership of the team there is expertise and proficiency across all the disciplines.

    Good data scientists are able to apply their skills to achieve a broad spectrum of end results. Some of these include the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data and building rich tools that enable others to work effectively. The skill-sets and competencies that data scientists employ vary widely. Data scientists are an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and analysis, that can help businesses gain a competitive edge.

    A major goal of data science is to make it easier for others to find and coalesce data with greater ease. Data science technologies impact how we access data and conduct research across various domains, including the biological sciences, medical informatics, social sciences and the humanities.

---