![]() Get Ahead in Data Science: Once employees have gained the foundational skills for data science, they have to build on those skills to generate more business value.Here, anyone in your organization can learn the basic mission of data science and find out how to impact a data science team as a collaborator. By working together, your workforce will get more out of an abundance of data. Become a Data Science Team Member: Data scientists of all levels don’t work in vacuums.Whether they’re working in IT or just have an interest in entering the field, this set of courses will help your internal talent build a foundation to succeed with data science and deliver value for your organization. Become a Data Scientist: Since data science is still so new, you may have employees that are just starting out with the official discipline.The following learning paths can help you build an effective data science training program: That’s why the LinkedIn Learning library is organized with learning paths that give employees the perfect sets of courses to advance their careers. The key to unlocking the value of eLearning is giving your workforce a guided set of courses to hone their skills rather than a massive library of disconnected courses. Overcoming the data scientist talent shortage means taking these courses and turning them into a guided data science training program. But if you’re aiming to kickstart an emerging data science function within your organization, you have to go a step further. Providing access to data science courses that reinforce these fundamental skills and capabilities can help talent development leaders deliver business value. That means giving them the data science courses necessary to improve statistics skills. And it’s not enough to just have a basic handle on stats-data scientists must be experts. Statistics and Mathematics: At the core of any attempt to analyze data or use machine learning is an ability to understand statistics and mathematics.As they work with massive amounts of business data, data scientists need to understand the keys to keeping that data safe. Data Governance: In the era of data breaches, data scientists can’t ignore the need for regulatory compliance and security. ![]() And that means keeping control of the big picture objectives of individual projects. Successful projects need to be managed efficiently. However, these projects often consist of many data scientists working together for many weeks at a time.
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