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Video course · 15 chapters · 659 min · certificate

Data Science Complete Guide: Build Machine Learning Models

A complete data science path: roles and roadmap, Python, pandas and visualization, exploratory analysis, machine learning from regression to clustering, a recommendation-system project, interview questions and MCP servers.

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What you'll learn

  • Distinguish data scientist, analyst and engineer roles
  • Follow a practical data science roadmap
  • Use Python, NumPy and pandas for data work
  • Visualize data and run exploratory analysis
  • Build regression, classification and clustering models
  • Complete a project and prepare for interviews

Chapters

15 chapters · 659:07
  1. 6:24 01Intro Members What is data science? How data powers recommendations, fraud detection and forecasts, and what data science is.
  2. 9:16 02Careers Members Data roles compared Data scientist versus data analyst versus data engineer: what each does.
  3. 9:00 03Roadmap Members A data science roadmap Statistics first, then Python, data handling, visualization, machine learning and projects.
  4. 2:41 04Python Members Python for data science Why Python is the language of data science and what to learn first.
  5. 66:03 05Data handling Members NumPy and pandas Working with arrays in NumPy and tables in pandas for fast data manipulation.
  6. 50:24 06Visualization Members Visualization with Matplotlib Plotting data with Matplotlib and choosing the right chart for the data type.
  7. 38:58 07EDA Members Exploratory data analysis Understanding data, spotting outliers and deciding the right solution before modelling.
  8. 11:26 08ML Members Types of machine learning Supervised learning (classification and regression) and unsupervised learning.
  9. 197:04 09Regression Members Linear regression Predicting continuous values with linear regression, and building toward a project.
  10. 40:25 10Classification Members Logistic regression Predicting categories with logistic regression, explained through an everyday story.
  11. 4:13 11Models Members SVM and decision trees Support vector machines find the best separating boundary; decision trees split data by rules.
  12. 55:17 12Clustering Members K-means clustering Grouping unlabelled data into clusters with K-means.
  13. 80:35 13Project Members Project: recommendation system Building a recommendation system, the kind used by companies like Netflix.
  14. 65:15 14Careers Members Interview questions Common data science interview topics, from imbalanced data to regression.
  15. 22:06 15Bonus Members MCP servers A bonus on MCP servers and how they're changing AI workflows and agents.

Jobs this course suits

Our AI checked this course against 500 jobs; these get the most out of it. Each job links to its learning path.

Bioinformaticians