It’s not a stretch to say that every B2B technology company needs to know more about Data Scientists. In 2015, Data Scientists will influence a whole array of software and hardware purchasing behavior, ranging from analytics packages, databases and visualization tools. Hence Competitive Intelligence, Market Research, Product Managers, and B2B Marketers all need to understand data scientists.
In short Data Scientists are popular. Exceedingly so.
It’s crucial to keep up-to-speed on this important group. That’s why, in this “Ahead of the Herd” post, we’re looking at Data Scientists and highlighting their skill sets, their common characteristics and their interesting insights.
To create this list we first looked for articles with the most LinkedIn shares (thanks BuzzSumo). Then we took that list and threw out all the articles that were merely advertorial, published by vendors, or those, which simply lacked insight.
In short, what follows are the articles, published in the last 30 days, that you truly need to read about Data Scientists. Enjoy!
What skills are employers looking for in data scientists?
Cascade’s Take: Good data on what types of skills align with the job of a data scientist.
“There were 151% more job ads posted online during this month than there were at this time in 2013.…Python and machine learning techniques are tied for being the most in-demand Data Scientist skills. Structured query language (SQL) isn’t far behind.”
In demand job skills – from: wantedanalytics.com
What are the best blogs for data miners and data scientists to read?
Cascade’s Take: Nearly 100 different blogs, websites, and communities for you to check out.
2 Types of Data Scientists Everyone Should Know About
Cascade’s Take: A good article, reminding us that data isn’t everything, and strategy without data to back it up is nothing.
Excerpt: “Broadly speaking, a strategic data scientist will have a firm understanding of business performance and growth, strategic thinking and communication skills, but be less well versed in the technical, nitty-gritty of setting up database systems and defining or selecting algorithms. On the other hand, the operational data scientist is more likely to come from a background of programming, statistics or mathematics, and will use these skills to implement systems to probe and interpret the data and draw out the most relevant results.”
Data Scientists at Work
Cascade’s Take: Interviews with 16 data scientists. Leading to a lot of gems – like the following.
Excerpt: “I’m not saying that non-cutting-edge models work better — indeed, I’d like to think that progress in machine learning ensures the opposite! Rather, it pays to keep things simple when you’re trying to understand your data and iteratively develop models for it. In those cases, it’s better to optimize for interpretability than accuracy. Once you’ve learned as much as you can, you can go back to more complex models. When you go back to them, you’ll hopefully now have the right training data, objective function, and features to take advantage of the latest and greatest machine learning has to offer.”
Data and Science Don’t Necessarily Make a Good Data Scientist
Cascade’s Take: Reminds us that numbers matter, but great data scientists aren’t just focused on the numbers.
“Math is specific. Database code is precise. The real world is messy. Data is scattered. Not all data is accurate. Some data issues can be fixed easily, other issues can be fixed with a good deal of effort, yet other data issues are next to impossible to address. Great data scientists are aware of the messiness of real world data, and they take it in their stride. They are adept at pushing things forward even when perfect data doesn’t exist, because perfect data is only present in textbooks.”
10 Skills Data Scientists Need
Cascade’s Take: Provides a solid hit list of topics – for the next time you chat with a data scientist.
- Know how to set up the data infrastructure
- Know how to provide data analysis and create data visualizations
- Experience with databases and database querying languages (like SQL or MySQL)
- Familiarity with big data tools (like Hive or Pig)
- Experience with statistical programming languages (like R or Python)
- Being comfortable with math (like linear algebra, calculus and probability)
- A good understanding of statistics (like hypothesis testing and summary statistics)
- An understanding of machine learning tools and techniques
- Understanding of data wrangling or data munging and the related tools
- Software engineering skills (like distributed computing, algorithms and data structures)
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