Data Science Skills & Tools

New / Eng
BigDataPro

New Eng

BIO
Aspiring ML Engr. / Data Scientist
BigDataPromore
Jul 22, 2017 15 Comments

These are the skills and tools needed by a data scientist that I can think of. Feel free to add or remove.

1. Excel
2. SQL
3. R
4. Python
5. Tableau
6. SAS
7. Presentation
8. Splunk
9. Power BI
10. Machine Learning

comments

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TOP 15 Comments
  • New / Other
    wdwG55

    New Other

    BIO
    Data scientist
    wdwG55more
    Increasingly a lot of companies are using Looker as well
    Jul 29, 2017 0
  • Indeed / Product gsrDt45
    Many data scientists work on or very close to production systems. I'd add a basic level of computer science understanding and practical abilities to this list for many roles.
    Jul 22, 2017 0
  • Uber ywudi
    Should add specific Python packages like Pandas, numpy, matplotlib, sklearn, etc
    Jul 22, 2017 0
  • Adobe / Eng
    Shizhi

    Adobe Eng

    PRE
    Intuit, Autodesk
    BIO
    Keep a left Sucker
    Shizhimore
    presentation
    Jul 22, 2017 1
  • Apple took cim
    Don't data science folks need understanding of Machine Learning?
    Jul 22, 2017 2
    • Yahoo ItsTime
      I was thinking the same because thats what most of our data scientists do. Now i am wondering what do these people in other companies do. May be they r confusing data analyst/ data eng with scientists.

      P.S. I do all of that other people have mentioned but i am not a data scientist
      Jul 22, 2017
    • Apple took cim
      Same at Apple. A lot of DS people do machine learning.
      Jul 23, 2017
  • New rFEy10
    Splunk. Not a requirement but a good to know type of thing.
    Jul 22, 2017 1
    • New / Ops
      Srixun

      New Ops

      PRE
      s3security
      Srixunmore
      Splunk is on the way out. Sales are way down, check out LogRhythm though. very strong tool. lots of news on it.
      Jul 25, 2017
  • SAS / Consultant hihihello
    Using the tools listed above, data scientist need to develop all or some of the following skills:
    1. ETL of data
    2. Data mining / data massaging
    3. Data preparation (clean, validate, replace missing values)
    4. Data visualization & exploration (build visuals to understand high level relationships in your data
    5. Advanced analytical / statistical model building
    6. Predictive model building/machine learning model building
    7. Model building and comparison to select best model
    8. Deploy models/ put into production
    9. Manage & maintain models
    Dec 10, 2018 0
  • New / Eng
    ueFX33

    New Eng

    BIO
    Director, Software Engineer, machine learning
    ueFX33more
    Data cube is doing a good job of killing all these requirements
    Jul 25, 2017 0
  • SAP VictorJ
    I interviewed for DS position and was asked also/DS just like normal SDE so add also/DS as well.
    Jul 22, 2017 0
  • eBay gojilla
    Problem solving; Business understanding
    Jul 22, 2017 0
  • Microsoft Phoebe
    PowerBI/Dax
    Jul 22, 2017 0

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