Intermediate Python for Data Science
This course builds upon CoRise's Intro to Python for Data Science course, and dives deeper into data visualization and foundations of machine learning. You'll learn how to use core data science libraries - Scikit-learn, and Plotly. At the end of the course you'll have a portfolio of data science applications in Streamlit that you can use to showcase your learning!
Course taught by expert instructors
Fmr. Staff Machine Learning Researcher at Twitter
Luca Belli was the Co-founder and Research Lead for Twitter's Machine learning Ethics, Transparency and Accountability (META) where he guided industry leading approaches for responsible ML practices and product changes. Previously he operated as a Data Science and Machine Learning Engineers at Conversant and WolframAlpha. His research interests lie at the intersection of feedback loops, algorithmic amplification (with a special eye on politics), and algorithmic audits. He holds a Ph.D. in Math from Tor Vergata University in Rome.
Learn and apply skills with real-world projects.
Anyone with some programming knowledge interested in becoming a Data Analyst, Data Scientist or Machine Learning Engineer.
Software Engineers skilled in programming looking for a career change into data science.
Financial Analysts, Accountants, or other professionals looking to learn Python for powerful analysis.
Comfort with Python fundamentals (as covered in CoRise Python Crash Course/Intro to Python for Data Science) - variables, functions, lists, loops, dictionaries, pandas, NumPy
Try these prep courses first
Customized, colorful data visualizations of Airbnb data using Plotly that are deployed as Streamlit applications.
- Graphs, Charts and Histograms
- Customizing Visualizations
- Overlaying Plots and Advanced Visualization
Implement scikit-learn algorithms to apply supervised learning techniques to the Airbnb dataset.
- Feature engineering for machine learning
- Supervised Machine Learning (Linear Regression and Decision Trees)
Apply unsupervised machine learning techniques to the dataset, and tune the model for improved performance.
- Unsupervised Machine Learning (k-means)
- Tuning Machine Learning Models
Work on projects that bring your learning to life.
Made to be directly applicable in your work.
Live access to experts
Sessions and Q&As with our expert instructors, along with real-world projects.
Network & community
Core reviews a study groups. Share experiences and learn alongside a global network of professionals.
Support & accountability
We have a system in place to make sure you complete the course, and to help nudge you along the way.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.