Junior Data Scientist
Real portfolio pieces built during the course, not a certificate for its own sake. Each one is work you can show.
- Model reviewA colleague's too-good model reviewed: the leaks found, why the score cannot be trusted, how features and the test must be rebuilt, and what the honest score is compared against.
- Targeting analysisThe honest model's numbers turned into a targeting decision: why accuracy misleads, precision and recall at the size actually targeted, and the money compared with today's rule.
- Experiment designHow to find out whether the win-back offer causes anyone to buy: who gets it, who does not, what is measured, when it is read, and why it is decided in advance.
- Model cardThe lapse model on one page for the people who decide: what it predicts, how well against the rule, what it uses and does not, its limits, how it is watched, and the decision it supports.
What you'll learn
Course content · 7 modules, 20 lessons
Sign up to unlock every lesson - the titles below show exactly what is inside.
What a data scientist adds beyond analysis: a prediction tied to a decision, and a baseline it must beat.
Requirements
- Comfortable with the fundamentals this course's own Module 1 covers, or equivalent experience.
- No prior experience in this field is required to start.
- A computer with a reliable internet connection.
- Comfortable using a web browser - no software to install.
Description
Every CertClue course follows the same seven-part shape: fundamentals, the role translated out of job-posting language, a real working day, the job's recurring rhythms, a multi-day simulation, the portfolio you build along the way, and a handoff into your next move. Here is what that looks like for junior data scientist.
Who this course is for
Anyone aiming to become a junior data scientist, including career changers with no background in it yet. This is the entry rung of a realistic ladder:
Answers business questions from data: cleans it, queries it, and presents what it shows.
Decides where a model helps a business decision, builds and evaluates it honestly, tests whether it works, and keeps it working after launch.
Sets which problems data science takes on, reviews others' models, and owns the business case for them.
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