Applied Statistics for Data Science
This course provides a rigorous, hands-on overview of statistical modeling for data science. You'll cover probability fundamentals and hypothesis testing, as well as advanced topics in regression. Along the way you’ll apply your skills to projects in a real-life scenario for an e-commerce company. At the end of the course, you'll be well equipped to dive deeper into an advanced career in data science.
Course taught by expert instructors
Machine Learning Engineer at Vouch
Emily Ekdahl is a machine learning engineer at Vouch, an InsureTech company, with previous experience working as a full stack engineer, data engineer, and machine learning engineer. In these roles she has developed a wide range data solutions across the stack including data pipelines, data models, machine learning solutions, and data visualizations to drive actionable insights for businesses. She holds a Masters in Clinical Psychology and learned to love applied statistics as a research assistant.
Learn and apply skills with real-world projects.
Data Analysts/Business Analysts looking to further their career into Data Science
Other software engineers seeking to transition into data science and statistics related roles
Foundation in basic statistics (mean, median, mode, and basic summary statistics)
Knowledge of Python programming: Variables, Functions, Lists, Loops + Numpy & Pandas (as covered in CoRise's Python for Data Science - https://corise.com/course/python-for-data-science )
Try these prep courses first
Calculate descriptive statistics summarizing quarterly sales for CostPro, an ecommerce company. Use probability distributions to model other aspects of CostPro's business, and use Streamlit to create a dashboard with visual information
- Descriptive Statistics - Central Tendency and Variability
- Probability Distributions - Binomial, Normal, Poisson
Analysis of CostPro A/B test results for experimentation with a number of features.
- Null and Alternative Hypothesis
- Selecting Metrics
- Confidence Intervals
Analysis of effectiveness of campaign for Returned/Churned behavior amongst customers who i) email coupon ii) physical mail coupon iii) no coupon.
- Fisher’s exact tests
- Mann–Whitney U-test
CostPro retail customer spending predictions using linear regression. Predicting customer churn using Logistic Regression.
- Linear Regression
- Logistic Regression
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.