IBM Data Science Professional Certificate study guide
IBM Data Science Professional Certificate · Work through your exam, one topic at a time.
The IBM Data Science Professional Certificate is a Coursera-based series of courses that introduces the full entry-level data science workflow, from methodology and Python programming through SQL, data analysis, visualization, machine learning, and a peer-reviewed capstone. It is aimed at career changers, students, and working professionals who want a structured, hands-on path into data science rather than a single proctored exam. The IBM Data Science Professional Certificate study guide has 9 sections and about 2,616 words.
01Data Science Foundations and Methodology
This domain tests the project lifecycle rather than a single library: how a business question becomes a data requirement, an analytical approach, a model, an evaluation, and a deployed decision. Expect questions on the IBM Data Science Methodology, CRISP-DM, supervised versus unsupervised problems, and the roles of data engineers, analysts, and scientists.
303 words, about 2 min02Python for Data Science and Programming Fundamentals
This domain tests practical Python: data types, containers, control flow, functions, classes, exceptions, file handling, and the idioms used in notebooks. Questions often ask you to predict output or choose the safer construct.
276 words, about 1 min03SQL and Relational Databases
This domain tests relational thinking and query construction: filtering, joins, aggregation, subqueries, keys, normalization, and the difference between DDL and DML. Expect to read a query and predict its rows or explain why it returns too many.
309 words, about 2 min04Data Analysis with Python (NumPy, Pandas, Data Wrangling)
This domain tests loading, inspecting, cleaning, transforming, and summarizing tabular data with pandas, plus NumPy vectorization. Questions often hinge on label versus position indexing, missing-value handling, and merge behavior.
301 words, about 2 min05Data Visualization and Dashboarding
This domain tests chart choice, encoding accuracy, and dashboard communication. Expect questions that show a chart type or design decision and ask what it communicates, what it distorts, or what should replace it.
292 words, about 1 min06Machine Learning with Python
This domain tests the scikit-learn workflow: splitting data, building pipelines, training supervised and unsupervised models, and evaluating them with metrics that fit the problem. Expect questions on leakage, imbalanced classes, and overfitting.
281 words, about 1 min07Applied Data Science Capstone Project
The capstone domain tests end-to-end execution: framing a question, acquiring data, wrangling, exploratory analysis, modeling or clustering, evaluation, and presenting a reproducible result. The IBM capstone commonly uses location data and the Foursquare API to compare neighborhoods, so expect questions on API use, feature engineering, clustering, and communication.
286 words, about 1 min08Tools and Platforms (Jupyter, Watson Studio, GitHub)
This domain tests the working environment: notebook execution, Watson Studio project assets, and Git/GitHub collaboration. Questions often ask why a notebook fails to reproduce or which Git command moves work from local to remote.
305 words, about 2 min09Professional Skills and Communication
This domain tests whether you can turn analysis into a decision. Expect questions on stakeholder communication, data ethics, documentation, uncertainty, and presenting recommendations without overclaiming.
263 words, about 1 min