If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
Here’s what’s new in our January 2023 Release Notes:
* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align
Can automated performance analytics work for data scientists? Yes. Though the job title may change, the mission of performance analytics remains the same. Our basic goal is to improve team dynamics and facilitate excellence in software development. But, how exactly does that work with respect to data scientists? That’s where the fun comes in. Two major issues can surface with the role of a data scientist – a) their job description, and b) a global shortage of data scientists.
Data scientists use Big Data to create value. They find data, analyze it for potential value, and build tools to distill it into actionable insights people can understand without having an aneurism. A data scientist’s job description covers more tasks, but that’s enough heavy-duty work to keep us busy for a little while.
Big Data involves data catalogs measuring in the Terabytes (1 Tb = 75 million pages of text). It’d take a few hundred people a year just to read through it, say nothing of understanding it or analyzing it. The data can be in hundreds of different file formats (txt, rtf, pdf, xslx, even jpg’s). It’s likely derived from a hundred or more sources, some in hard copy. Ultimately, they’re able to assess if there’s a meaningful relationship between two or more data points.
Let’s say we have a data scientist for an international retailer. They may track a few hundred data points for each product in both their own and their competitor’s inventory. They could look at a company’s last promotion of a specific product to see how it affected their sales and their competitor’s sales. Data scientists can take that much, much further. To gather such insights, data scientists must create algorithms – and display the data in a way that people can understand.
According to QuantHub, there was a shortage of 250,000 data scientists in 2020 concurrent with steadily increasing demand. The US Bureau of Labor Statistics estimates a growth rate of 31% for data scientists and related positions through 2029. There are over 57,000 data scientist job postings on LinkedIn at the moment. All of this helps underscore that 86% of companies find it challenging to hire qualified IT talent.
Data scientists are often expected to have a Master’s degree and several years of experience. But, companies also want industry professionals familiar with their particular market dynamics. And they want some coding skills. And, in many cases, despite the lockdowns, candidates should either live locally or be willing to relocate. And then… Mick Jagger just ran up to me and slapped me like the Orangeman from a Tango commercial singing, “You can’t always get what you want, but if you try, sometimes – you get what you need.”
As the Toptal data scientist job description above more succinctly describes, data scientists are,
“x% scientist, y% software engineer, and z% hacker.” Only, a lot of times, it’s also necessary to add, “w% industry expert.” We can simply say that many data scientists are still in the process of learning and developing their job-specific skills. So, while they may be challenging to find, they are by no means unique with respect to the need and value of continued education and training.
Moreover, companies with data scientists are likely to have more than one. They are very likely to be part of a team with other software engineers and developers. Companies with the need for more data scientists are often trying to source from within – perhaps training their engineers to be scientists while helping developers become engineers and engineering managers. So, knowledge sharing and team development are also factors that come into play.
Data scientists have metrics for almost everything related to what they’re analyzing and the value they create for the business. Their metrics don’t always extend to performance metrics like how productive or efficient they are in coding. That’s probably not something they should be spending their time on anyway, as well – automated solutions like Gitential have you covered in this respect.
Your data scientists may very well be in a position of creating new wheels – so that your developers don’t have to. It’s considered a best practice for developers to use existing solutions whenever possible. Creating new solutions involves more time and introduces a greater chance of introducing bugs. When creating algorithms for pulling and analyzing data from a catalog or repository, it’s entirely possible that you can create derivatives with some minor changes. The individuals running reports often like to see the data in different ways, using different dimensions and variables.
A data scientist can show your developers how to modify an algorithm to show the data in different ways and different/additional data points. This cannot be tracked directly, but it should translate to an increase in developer productivity.
For an end user, a data science project can be as simple as entering one variant number for a price elasticity calculator, and the tool will spit out different price ranges which can be competitive on the market. However, the data science model behind the scene is way more complex than that. One objective is to understand the complexity and code heaviness of different projects. Code complexity can provide a better understanding of how much effort it took the team to code the actual tool vs. how much time it took to design it (e.g. logged hours on brainstorming sessions).
From this, you can better assess the skills needed most on different types of projects within the same organization. You can also better understand what’s happening with projects having the same, or comparable, complexity. For software development agencies, increasing your capacity for more complex projects expands the scope of projects you can confidently take on.
What is cycle time? Why is it so important? What’s most important – Takt time vs cycle time vs lead time? How to improve cycle time in agile? Learn more in our article on the topic.
The characteristics of a perfect data scientist include, at a minimum, strong programming and analytical skills, industry expertise and experience, good communication and interpersonal skills. They already know everything about your business and they… live right across the street. There are a limited number of unicorns in our universe. But, a company with a continuous development program that prioritizes skills when hiring for the role can have a hand in creating as many unicorns as they need.
Sometimes, some simple coding experience and enthusiasm, with a measure of guidance, can grow a person to have a successful career as a data scientist. Everyone starts somewhere – no one spontaneously wakes up as a data scientist… even if it is a dream job.
Even the best software developers have a vested interest in improving their coding skills – and the skills of their teammates. At times, it may be necessary for a data scientist to fill the role of a software developer. However, the scarcity of their skills for most companies warrants a strategic view for knowledge sharing. Your developers and data scientists can work together to continuously and mutually improve their industry knowledge and coding skills.
If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
Here’s what’s new in our January 2023 Release Notes:
* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align
Here’s what’s new in our December 2022 Release Notes:
* Developing Improvements for On-Prem Data Processing;
* Improving Jira Data Connection;
* Aligning Metrics throughout the Application.
