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Applying Statistical Thinking With Python

Combine descriptive and inferential statistics with Python to perform exploratory data analysis and make predictions.
Who Is This For?

Employees interested in combining their knowledge of Python and statistics to perform exploratory data analysis and make predictions.

Any prerequisites?
  • Python: Beginner/Intermediate level knowledge of Python, including but not limited to a variety of Python data types, aggregates, basic functions; introductory experience working with Pandas DataFrames and NumPy arrays.
  • Statistics: Familiarity with the following concepts in descriptive and inferential statistics, including but not limited to univariate (eg., measures of central tendency and spread) and bivariate (eg., covariance, correlation) descriptive statistics, basic graphical representations of data (eg., histograms, box plots, scatter plots, etc.), probability, sampling distributions; a course in statistics that covers descriptive and inferential statistics is an appropriate primer for this course.
What will I be able to do after this Course?
  • Explore a tabular dataset in Python using both summary statistics and data visualizations.
  • Calculate and interpret a confidence interval for a sample mean or sample proportion.
  • Choose, implement, and interpret a hypothesis test in Python.
  • Create a linear regression model in Python and use it to make predictions.
Reimbursement FAQ

Course Overview

Sprint 1: Exploratory Data Analysis and Descriptive Statistics
- Perform graphical exploratory data analysis by plotting data with Python.
- Perform quantitative exploratory data analysis by computing summary statistics with Python.
- Project: Help a newspaper determine whether referral bonuses could help them increase their subscriber base by exploring the results of their experiment.
Sprint 2: Introduction to Probabilistic Thinking
- Calculate and interpret probabilities for situations that can be described by discrete and continuous random variables using both formulas and simulation.
- Calculate the probability of observing a particular sample statistic given a population distribution, using both formulas and simulation.
- Project: Help a newspaper’s marketing team decide on budget spend by determining whether potential newspaper subscribers are more likely to respond to an email or direct mail offer.
Sprint 3: Statistical Inference - Hypothesis Testing
- Use re-sampling methods to calculate and interpret a confidence interval for a sample statistic.
- Perform and interpret the results of a hypothesis test using simulation-based methods and built in Python functions.
- Project: Revisit your original analysis of referral bonuses from Sprint 1 to back up your findings with hypothesis tests.
Sprint 4: Introduction to Linear Modeling in Python
- Explore linear trends by visualizing data and using descriptive statistics.
- Build an optimized simple linear model.
- Your Capstone Project: Help a newspaper increase their current subscriber base by applying your knowledge from the course to find three million new subscribers!

What’s in a Modal course?

1:1 Coaching
Receive personal guidance, instruction, and motivation from real, practicing industry experts.
Real-world simulations
Practical coursework blends simulated and real-world projects, ensuring you are building job-ready skills.
Integrated code editor
In-browser coding environment mitigates challenges while enabling paired programming and inline feedback.
Structure & flexibility
Engage with content when your schedule allows. Our assignments and deadlines help you stay on track and our coaches keep you accountable.
Individual guidance
Courses for a variety of career goals, skill needs, and company objectives, ensuring learning is both relevant and productive.
Capstones projects
Challenging and satisfying capstone projects allow you to demonstrate the skills you’ve learned, while reinforcing collaboration and business skills.

Meet our coaches

Linda Liu
Director, Data Science & Analytics

Working with the learners makes it an incredibly rewarding journey. The shared excitement and collaborative growth highlight the entire fulfilling coaching experience!

Nataliia Maksimova
Director, Business Intelligence

It's incredibly inspiring to introduce people to the fascinating world of data. Sharing my passion for data and showing that it's not just dry numbers but a creative field where you can grow and innovate is deeply rewarding.

Udit Mehrotra
Senior Data Scientist

Seeing the growth in my learners is not only heartening but also assuring because I know I had a significant role to play in shaping their journey.

Interested in buying multiple seats for your team?
Contact us