In the field of data science, 75% of job postings list a master’s degree as the preferred qualification, despite a bachelor’s degree being the minimum requirement. Individuals with 3 years of relevant experience, but without the data scientist title, often find themselves at a disadvantage. These candidates, despite possessing the necessary knowledge and skills, struggle to secure interviews. The financial burden of pursuing a master’s degree deters many from further education, even though an online degree could provide the coveted “master’s stamp.” This statistic highlights the challenging landscape for aspiring data scientists who are weighing the costs and benefits of additional education against their current qualifications and experience.
Splendor of SQL
@iam_Uchenna
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Mar 22 This video covers the four main types of data science roles: Machine Learning, Product Analytics, Full Stack, and Engineering. It explains the distinct responsibilities and required skills for each.
i_am_Don
@wordsbydon_
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Apr 8 If you are taking Data Science as a Course when you can just learn ML and be qualified for Machine learning engineer,Deep learning engineer,AI engineer and‘Data Scientist’ job roles,you have your priorities messed up.
Cuesoft
@cuesoftinc
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Apr 7 In today’s world, the lines between roles and responsibilities in tech can get blurry. One of the most common mix-ups? Data Analysts and Data Scientists.Although both roles work closely with data, they serve different purposes, require distinct skill sets, and create unique
Santiago
@svpino
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Feb 5 I’ve never worked with a company that had a “dataset” waiting for me.In fact, most companies don’t even have any data, and what they do is not useful to solve the problems they have!I don’t care if you think you are a “data scientist” or a “machine learning engineer”: you’ll
Zach Morris Wilson
@EcZachly
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Feb 19 In the future, data engineers won’t be supplying data, they will be architecting decisions.They will solve the following questions:– what is going on in the business?
Solved with data pipelines– what’s the ground truth we can trust?
Solved with master data management–
Digital Master Channel
@DigitalMasterCh
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Apr 8 Prioritise business needs when using big data. Embrace data collection and storage. Make data visualisation key. Manage data iteratively. Leverage cloud solutions for cost savings and simplified management. Manage data properly for compliance and usability.RT
@antgrasso
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