According to a recent report from, the position of data scientists is expected to increase by 19% over the next decade. Burning glass, Collect and analyze millions of job listings from across the country. As data is increasingly the lifeblood of every organization, data scientists need to have strong business insights as well as good technical skills.
Machine learning / neural network
In 2021, machine learning methods such as transfer learning and transformers are receiving a lot of attention as they are rapidly innovating in various fields. There is a lot of momentum behind PyTorch in building and training neural networks, and Keras and TensorFlow are also commonly used.
There is also a rich ecosystem of software libraries and many open source to help accelerate machine learning and data science applications.
“Data scientists are fascinated by their deep intuition about why and how machine learning algorithms work, which is important for tackling the challenges that inevitably arise during training and testing,” he said. I am. Matthew silver, Senior Director of Data Science at Vectra, a specialist in detecting and responding to AI threats. “ONNX, a neural network standard that facilitates platform, library, and language-independent model deployment, has helped streamline the use of AI in production and accelerate modeling efforts.”
“Data scientists who can walk the job and build models quickly using a common software library are the most competitive, and in almost all cases, strong software development skills are a plus,” Silver said. I am.
Data scientists who understand cloud engineering principles and cloud infrastructure are attractive to many employers. This means getting used to one of the three major public cloud providers: Microsoft, Amazon Web Services, and Google. Each provides a comprehensive toolset for data scientists for data extraction, data cleansing, visualization, and machine learning purposes.
“I’m personally looking for a data scientist who is familiar with cloud infrastructure, CI / CD pipelines, and automation,” he said. Philip Gates-Idem, Chief Architect of JupiterOne, a provider of cyber asset management and governance solutions. “Data scientists need to have a solid understanding of how to build and use tools in their cloud infrastructure.”
Statistics is a field of mathematics that seeks to collect and interpret quantitative data using models and representations of specific datasets, is the core of data science, and concepts such as probability, variability, regression, and central tendency. is included.
“If you don’t have in-depth knowledge of the statistics that are central to data science and how to apply appropriate mathematical reasoning to the problem you’re working on, the number of platforms and languages doesn’t matter. You can write it in your resume. I will. ” Lars Kenman, Chief Architect of IT consulting firm Netrix. “I think this is a challenge in the industry today. We receive a lot of resumes from people who haven’t done the hard work to internalize the scientific method.”
Project management is another important skill for data scientists, as data science projects can involve long exploration phases and can contain multiple unknowns later in the game. For example, agile techniques allow data scientists to prioritize roadmaps based on their requirements and goals.
“It is often very difficult to predict how long it will take to develop and train a machine learning model, and companies waiting for updated models and results will have a timeline and plans for this unpredictability. Often suffers from, “Silver explained. “I’m a data scientist who can take over key modeling efforts by understanding the limitations from the beginning, communicating the status of the project as the effort progresses, and predicting when the next meaningful read can be provided. Play an important role in our team. “
Data storytelling / visualization
Organizational data can contain a surprising amount of potential value, but it cannot generate value unless it reveals those insights and translates them into behavioral and business outcomes. Plotly, Tableau, and D3 are among the top data science visualization and storytelling tools in demand today.
“If clients don’t understand what you’re doing, it’s easy to underestimate what you’re doing, especially during the data preparation phase,” says Kemmmann. “It’s an important part of your role to clearly explain the process and benefits of each step in a language that your audience can relate to, and support where possible with proper data visualization.”
Data scientists have more opportunities to “practice” their data than ever before, but they need to have a good understanding of their business goals and the ability to communicate jargon clearly. Data scientists who can translate data into useful terms are those who will be able to add that value.
“The ability to transform that data into clean, easy-to-digest business information is a huge skill. Data scientists don’t necessarily have that soft skill or the ability to sit in an executive’s room to make clear decisions. Not necessarily.-Manufacturing process. ” Joshua Drew, Regional Manager of Robert Half Technology, an IT dispatching company.
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Nathan Eddy is a freelance writer for InformationWeek. He has contributed to Popular Mechanics, Sales & Marketing Management Magazine, FierceMarkets, CRN and more. In 2012 he made his first documentary film, Absence Column. He currently lives in Berlin. View full biography
https://www.informationweek.com/from-ai-to-teamwork-7-key-skills-for-data-scientists-/d/d-id/1341330?_mc=rss_x_iwr_edt_aud_iw_x_x-rss-simple From AI to Teamwork: Seven Key Skills for Data Scientists