15 Mistakes to Avoid in Data Science
15 Mistakes to Avoid in Data Science
What you’ll learn
Skills you’ll gain
By avoiding common data science mistakes, you can save time and grow your skills faster. In this course, taught by leading data science experts, you'll learn the top mistakes you should avoid as a data scientist. you'll explore fifteen key mistakes, ranging from big-picture blunders such as overpromising on your results, to more nitty gritty matters like how to avoid writing code from scratch. The clarity you'll gain in this course will give you an effective methodology upon which to build your data science skill sets.
Syllabus
Download syllabus-
1
Communicating with overly technical language Data science is a highly technical field, which can alienate the non data-savvy.After watching this video you'll be able to discuss how using overly technical language can negatively impact your data science projects. 1m
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2
Skipping the fundamentals It's unavoidable to encounter repetitive "simple" tasks when working with data, but it's important to be incredibly thorough despite the repetition. 1m
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3
Moving too quickly It's easy to get excited when working with novel datasets or chasing after unique problems. 1m
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4
Having a data set that is too small If you overlook the size of your dataset, you may run into serious problems later. 1m
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5
Failing to adopt new tools Oftentimes the best analysis is dependent upon using the best tool or the most current algorithm. 1m
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6
Not considering the level of variation Variation is a really important data science project and is often poorly communicated to stakeholders. 1m
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7
Lack of documentation Documentation must be at the core of every data science workflow. 1m
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8
Relying solely on formal education A formal degree or certification program is a great foundation, but it doesn't keep current on its own. 1m
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9
Taking too long to share results Most data projects are time sensitive and it's always necessary to maintain the right balance. 1m
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10
Including your bias When working with datasets it's easy to inadvertently introduce persona bias into the analysis. 1m
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11
Overpromising solutions to stakeholders Setting expectations appropriately is a necessary skill for every data scientist. 1m
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12
Building tools from scratch Writing code is a key skill for a data scientist but it is tedious work. 1m
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13
Assuming knowledge level of stakeholders Presenting data without matching the language to the knowledge level of the room is a common mistake. 1m
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14
Not telling a story with the data Data is only helpful for achieving goals if it's well understood. 1m
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15
Not confirming with stakeholders A common mistake in data science is making assumptions about the goals of a project. 1m
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1
Get started on the right path 1m
Certificate
Certificate of Completion
Awarded upon successful completion of the course.
Instructor
Madecraft
Madecraft is a full-service learning content company based in Santa Barbara, California. The company's award-winning production pipeline is creating the next generation of learning content that teaches and delivers real-world skills. Madecraft assembles industry-best talent and trusted partners with a vision: give clients top-tier learning content in the voice of their brand.
The Madecraft team is committed to raising the stakes and elevating the craft with efficient and scalable content aligned to client specifications and built to engage and empower people. If you or your company are looking for a team to build online courses, visit Madecraft at onlymadecraft.com.
Madecraft
Full-Service Learning Content Company
Accreditations
Link to awards
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