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Has anyone else begun to resent data science?
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Problem solving, algorithmic thinking, storytelling.
The rest of the tools and languages can be learned in a few weeks/months without problem. But if you can't explain to your stake holders what value the analysis you just did for them brings, you are wasting your time.
Super important and incredibly hard to teach
1. Technical aptitude (sql, python, R, ML, excel, tableau, whatever your team uses)
2. Ability to explain what you did to the non technical business side. Sell it. Be confident and persuade them to trust your analysis.
3. Ability to explain what you did to your technical team so that you may collaborate and improve your models
SQL easy
it’s not a technical skill - it’s the ability to problem solve and think beyond lines of code.
problem solving, communications, etc
Domain & industry knowledge of the data
Technical skills ranked (IMO):
*SQL
*Excel
*Visualization/Dashboard design/implementation
*Python/R
*Understanding of APIs
*Cloud Computing
*DevOps
All have their value, but ranked largely based on how pervasive each are for data analyst roles across the board. In 5-10 years Cloud Computing will probably be a top 3 skill (baseline/prerequisite)
Friendship
The real VISUAL DISPLAY OF QUANTITATIVE INFORMATION is the friends we made along the way.
Story telling. Data doesn’t mean jack if you don’t know what it is telling you, and how it is solving the problem you set out to answer.
Communication
This 100%. There’s no value in doing any sort of analysis if you can’t explain it to anyone.
SQL/ETL/databases, Python/R, Regressions/Clustering --> AI/ML, Visualizations (Tableau, Qlik, Plotly)
=xlookup
lol
Stack exchange
Understanding the business problem you are trying to solve.
Storytelling and articulating clear insights.
Making friends in high places. Then SQL
If you can use a pivot table you’re already ahead of the curve
SQL
Azure
If sum
SQL
It’s wild how this basic ass language does so much for us
Curiosity / passion / interest about real world problems.
Technical skills will help answer the problem, but shaping the question and translating into some kind of data/model specification is how you work out what to do with the technical skills.
Technical skills are obviously useful, but they have become a bit of a commodity - there are always going to be people who have put in more hours at school/on udemy/on stack exchange than you.
E.g. building a sql query is helpful but it is more useful to an org to know that the query is the right one to be doing in the first place.
There are some domains where technical skills are clearly very valuable (e.g. ai/ML experts where $ returns to model tuning are large), but if we are making sweeping generalisations then I stand by domain knowledge/passion.