Want to Study Data Science Online While Working? 4 Steps to Success

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Professional development training can be a bit of a catch-22: you want to pursue training to further your career, but taking the time and resources to do so can make it difficult to devote your full attention to your work. However, with careful planning and the right resources, striking the perfect balance between your career and your data science training is absolutely possible.

Read on for a quick overview of some simple and effective steps you can take to make your data science training and work commitments coexist in harmony.

1. Talk to Your Boss About Data Science Short Courses

If you are considering pursuing professional development training in data science, or if you have already decided that this is what you want, speaking with your manager is a critical first step for multiple reasons. First, an informed and supportive manager can help make your workflow more manageable during your studies; for instance, they may avoid assigning you a new large project.

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Keep your boss informed, and they can help make your professional development training smoother

Second, your manager can be a valuable resource. They may have recommendations or advice from other employees who have undergone professional development training, and may even know of company or industry-supported financial resources accessible to you.

2. Cross-Reference Your Course Outline and Your Work Calendar

Once you have discussed your options with your manager and decided on a data science course, you will want to find as much information about the content, expectations, and timing of the course as you can in order to best plan your schedule in the coming weeks. This key information can generally be found in a course outline, or on the program’s webpage.

For instance, those who study data science online at Southampton have their course content spread over 6 weeks with 3 pieces of key coursework. With this information, you can schedule the flexible components of your work demands during less busy weeks, anticipate any conflicts, and prioritise your resources to ensure that the demands of your coursework and career can be met.

3. Develop a Comprehensive Organising System, and Stick to It

As an ambitious professional, you no doubt have an effective organising system that works for your life. With the addition of data science short courses to your schedule, however, you may need to adjust your methods in order to ensure that you can keep on top of your new workload, and that all materials and information relevant to your course is available for reference in one place.

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Consistency is key when it comes to organising your Southampton data science course

Whether it is an online extension of your course’s learning platform, a folder you keep at your desk, a section of your daily organiser, or a visual spreadsheet, having an easily accessible system that you use on a regular basis will help you stay on top of your progress and complete your course successfully.

4. Make the Fundamentals of Data Science Easier with Tutor Support

One of the most beneficial aspects of choosing an online course for your data science training – like Southampton’s Fundamentals of Data Science (Technical) course – is the opportunity to learn remotely while still benefitting from tutor interaction and support.

Southampton’s course is structured with a diverse range of learning activities, including presentations, tutorials, exercises, and other options. Throughout the course, all students receive individual seminars with their tutors. This is a great opportunity to address specific difficulties you may be facing in the course in a one-on-one setting, allowing you to use the rest of your time more effectively as you move through the course material and develop your data science expertise.

Studying the Fundamentals of Data Science while on the job is an achievable and worthwhile goal.

Visit the Southampton Data Science Academy’s website for testimonials from our satisfied former students and their employers!

Topics: fundamentals of data science, Study Data Science Online, data science short courses