AI News, The most important data science skill (hint: it’s not what you think)
- On Sunday, June 3, 2018
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The most important data science skill (hint: it’s not what you think)
A common question I get from beginning data science students is “what skills do I need?”
In response, many people give these beginners a long list of data science skills that they need to learn: …
On final analysis, data scientists are not ultimately not hired to create models, analyze data, create presentations, or any of those things.
In fact, in some sense, models, analyses, software, and presentations are obstacles that stand in the way of what businesses really need.
This is an extremely important distinction to make, because it’s possible to spend hundreds of hours on a data science project that contributes zero value.
It’s also possible to spend 15 minutes on a quick-and-dirty SQL data pull that can save a business thousands, even millions of dollars.
Now, I don’t want you to get confused: as a data scientist, you are hired to create business value with the tools of data science … analyses, models, data management, etc.
If you use those tools in a value-heavy way, companies will fight over you and you’ll be able to command a high salary.
If you can’t create value with the tools of data science, you’ll sadly be one of the bottom 80% who struggle to succeed in the data industry.
(Note: this is why I refuse to watch Mad Men … I lived Mad Men for a while.) Some of my business partners were working on a large project for a major corporate client.
They were doing an overhaul of the client’s marketing, starting with demographic analysis, segmentation, and a few other deep dive analyses.
About 3 days before the presentation to the client, they asked me for a fairly detailed analysis of the client’s customer list and past marketing efforts.
When I talked to them in order to flesh out the project requirements, my business partners said that they wanted an analysis with over 100 plots, charts, and tables.
They spent the following 48 hours trying to make sense of it all, including sleeping on the floor in the office overnight.
Instead of giving them something of value, I gave them a stack of charts and graphs that just caused them to question their life decisions.
This is a data science blog, and I initially told you that “you’re actually not hired to create models, analyze data, create presentations, or any of those things.”
The fact is, as a data scientist, you do need to create models, analyze data, create presentations, but you do so in service of creating business value.
Being able to create business value is the most important data science skill, and you can’t take your eye off of that.
At it’s core, finding insights is mostly about analyzing data to find recommendations that can add value to the business.
If you’re new to data science or a junior member of a data science team, this is where you should be focussing your efforts.
More often than not, the best way to add value as a junior data scientist is to analyze data and find recommendations that can improve metrics that are important to the business.
via data analysis largely consists of applying basic data visualization tools.
Typically, you need to apply the toolset strategically to look for opportunities to improve key performance indicators (i.e., KPIs).
So for example, if you’re in a marketing department, you’ll commonly want to optimize things like return on investment, or the number of new customers.
If you’re working in customer service analytics, you’ll be looking for ways to improve “customer satisfaction.”
Before you learn the process for finding insights in data though, you need to master the data analysis toolkit.
As I commonly point out, many people want to skip these basic skills and move on to the advanced material right away.
Many data scientists fail to master the basic toolkit, and they are terrible at finding insights in data.
If you can give them what they want – if you can find insights and create value for the business – they’ll pay.
Moreover, at a high level, almost every business is trying to optimize for profits, shareholder value, revenue, or market share.
Finance teams have a suite of financial metrics that they may be trying to optimize (depending on the goals and financial condition of the business).
Engineering teams creating a physical product may be trying to optimize the performance of some element of that product, like fuel efficiency in a car.
isn’t half as cool as saying, “yeah brah, I’m building a deep network at work to categorize cat pictures.”
But even though they aren’t as sexy as some modern tools, linear regression and logistic regression are true workhorses.
If you want to get a junior data science job, I highly recommend that you learn linear regression and logistic regression.
In some cases, they are resistant to problematic data like correlated data, data with missing values, skewed data, etc.
could write at some length about the best tools for making predictions, but as a junior data science hopeful, stick to a few tried-and-true techniques.
hate to reinforce the stereotype of the nerdy, poorly-spoken science nerd that can’t communicate well, but it’s sort of true.
Some of these people were brilliant coders and analysts, but if you asked them to package their recommendations into a PowerPoint deck or you put them in front of a management team, they were frankly, terrible.
A large and colorful display of feathers is a signal to female peahens that the male peacock is fit, healthy, and successful.
They cobble together programs by cutting and pasting small code pieces, and tinker with it until it runs without errors.
But there is a large set of wannabe data scientists that can’t write code to accomplish even basic tasks.
The issue here is that even though 80% of wannabe data scientists can’t create business value, they can fake it.
For example, it’s very easy today to go online and effectively buy a certificate that says you know ggplot2 and dplyr.
There are many online course vendors that simply issue a certificate at the end of a purchased course that says “John Smith has completed a course in ggplot2.”
I know a student that completed a $30,000 data science masters course from an elite university, but at the end he still couldn’t write data science code.
Long time readers of this blog will know that I strongly recommend that beginning data science students learn data visualization first.
Being able to write data science code fluently, rapidly, and from memory will enable you to display your skill.
When a hiring manager sees you write code clearly and fluently, you will have a much better chance of getting the job.
Like playing an instrument, writing actual code on a keyboard is a physical skill, and it’s essentially impossible to fake.
(Hint: the best way to do this is to serve your clients well in the first place by creating massive value for them and the business.) You just read several thousand words on creating value and signaling value as a data scientist.
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