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Data Science Professional: From Analyst to Data Scientist

Name: Data Science Professional: From Analyst to Data Scientist
Availability: InStock

The advanced path after Data Analysis: Python, NumPy and pandas, statistics and probability, advanced SQL, exploratory analysis, A/B tests, predictive models, feature engineering, competitions and a job-ready portfolio.

64 lessons22h 25m of video4 quizzes3 practical tasks Certificate

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What you'll learn

Clean, reshape and analyse real datasets with Python, NumPy and pandas
Use statistics and probability correctly: distributions, sampling, correlation, confidence and p-values
Write advanced SQL with joins, CTEs and window functions
Explore data and tell its story with clear visualisations
Design and analyse A/B tests and experiments
Build, evaluate and explain regression and classification models
Engineer features and avoid overfitting with cross-validation
Compete on Kaggle and Zindi and build a portfolio employers trust

Course content

Part 1. Start here: what data scientists really do3 lessons

Part 2. Python for data: NumPy, pandas and cleaning8 lessons

Part 3. Statistics and probability17 lessons

Part 4. Advanced SQL3 lessons

Part 5. Exploratory analysis and visualisation9 lessons

Part 6. Experiments and A/B testing3 lessons

Part 7. Predictive modelling8 lessons

Part 8. Feature engineering and competitions8 lessons

Part 9. Portfolio, communication and careers4 lessons

Part 10. Final project1 lesson

Capstone: an end-to-end data science project on an African problem · 🎓 Capstone project

Capstone project: Capstone: an end-to-end data science project on an African problem. You finish by building something real; a mentor reviews it before your certificate is issued.

About this course

Data scientists turn data into predictions and decisions — which customers will leave, which loans will be repaid, where disease will spread next, what a farmer's harvest will be. This course is the next step for learners who've finished Excel and Data Analysis (or know the basics) and want to become data scientists. You'll learn Python for data with freeCodeCamp and Keith Galli, a full university statistics course plus StatQuest's famously clear explanations, advanced SQL (window functions, CTEs), exploratory data analysis and visualisation, hypothesis tests and A/B experiments, regression, classification, trees and random forests, cross-validation, feature engineering, Kaggle and Zindi competitions, and how to build a portfolio that gets interviews. Finish with an end-to-end project on African data. About 22 hours of video plus projects — 3 to 4 months at an hour a day. Ready for more? Machine Learning & AI Engineering comes next.