10 Data Science Career Paths
10 Data Science Career Paths

Data Science is an interdisciplinary field of scientific algorithms, tools, and technologies to extract useful insights from the data. This blog post lists the 10 data science career paths with job responsibilities and required skills, tools and technologies.

Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.


According to Zeeshan-ul-Hassan Usmani, the following is a list of 10 data science career paths with required skills, tools and technologies.

  1. Data Science Developer
  2. Machine Learning Developer
  3. Data Scientist
  4. Data Engineer
  5. Data Analyst
  6. Data Scavenger
  7. Data Visualization Expert
  8. Data Story Teller
  9. Big Data Expert
  10. Data Science Researcher

10 Data Science Career Paths

#TitleResponsibilitiesRequired Skills
1Data Science DeveloperTo implement the algorithms using programming languagesPython, R, SQL, Jupiter Notebook, RStudio, etc.
2Machine Learning DeveloperMachine Translation, Deep Learning, Computer Vision Scikit, NLP, Tensorflow, Feature Extraction, Classification, etc.
3Data ScientistTo implement models for future predictionsA/B Testing, Causal Inferences, Co-Relation, Pattern Matching, etc.
4Data EngineerTo make sure that the data is ready to be fed into ML algorithms/ ModelsPrepare data pipelines, Auto feeding of data to ML algorithms, ETL, SQL, etc.
5Data AnalystTo clean and analyze dataData cleaning, Data Wrangling, Data Classification, SQL, ETL, etc.
6Data ScavengerTo collect and prepare dataData Collection, Web Scrapping, Data Repositories, Data Extraction, Selenium, URLLib, Scrappy, etc.
7Data Visualization ExpertTo display data in a meaningful waySeaborn, Tableau, Plotty, Matplotlib, Qlikview, etc.
8Data Story TellerTo present the data like a useful story for evaluation of the commercial analytical value of the dataR, Matplotlib, Data Visualization, etc.
9Big Data ExpertTo extract and analyze huge amount of dataScala, MongoDB, ApacheSpark, Hadoop, Weka, R, etc.
10Data Science ResearcherTo find new possibilities, algorithms, tools, and technologies to efficiently and effectively utilize dataMath, Statistics, Linear Algebra, Calculus, Data-Driven Policy Making


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