🙌 Get a free 1-year Subscription
Purchase your second Modal course and we'll set you up with a free 1-year subscription. Learn More.
Applying Statistical Thinking With Python
When
June 6, 2024 - August 4, 2024
Course Tuition
$1,950
No Up-front Payment
Modal now offers a deferred direct bill payment option for Booz Allen employees.
Learn more
Learn more
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.
NEED HELP DECIDING?
Book time with a learning expert.
A Typical Week
Monday
Self Study
Kick-off new topic with self-study & online learning
- Coaches support learners hitting roadblocks
- Manager check-in to bring learning into company context
Tuesday
Wednesday
Labs
Learning material leads into practice environment & labs
- Coaches support learners hitting roadblocks
- Pair programming to bring learning into company context
- Community allows students to help each other
Thursday
Live Event
Interactive live session hosted by Coaches
- Community allows students to help each other
- Community Groups host expert AMAs & guided community discussions
Friday
Projects
Work on a weekly project
- Community allows students to help each other
- Group projects
- Coaches support learners hitting roadblocks
Saturday
sunday
Work at your own pace
Expert coaching and actionable feedback from Coaches
.svg)
Course Schedule
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.
- 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.
- 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.
- 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!
- 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!
Why Modal?
.svg)
Projects & Practice
Real world exercises contextualize learning in real-world context.
On-Demand Coach Support
You are never alone. Coaches are always present and can help you!
Live Sessions
Hear from guest speakers and expert instructors through engaging lectures.
Technical Labs
Hands-on labs allow you to play with new tools and concepts to build real skills.
Community of Peers
You will be part of a learning community were support is abundant.
Asynchronous Learning
Self-paced learning is scheduled for each learner, with a dashboard to help you keep on track.
Other Courses
“I love the quantity & quality of learning materials, the interactivity, the live sessions, the coaches, are invaluable. I can really feel the difference in the level of engagement that Modal has to every participant compared to an ordinary course."
.svg)
- Veselina Stoyanova - Reporting Analyst, EMAG
Learn more about FlexEd
We are excited that Modal now offers a deferred direct bill payment option for Booz Allen employees.
The deferred direct bill payment option enables employees to enroll in learning opportunities with no upfront costs. This payment option will require the employee to sign a Family Educational Rights and Privacy Act (FERPA) agreement with Modal to release grades/completion to Booz Allen to satisfy the FlexEd Program completion requirement.
Note, Modal may also be used for the FlexEd Program reimbursement payment option. See the full FlexEd Program Policy & FAQs.
Learn more about FlexEdThe deferred direct bill payment option enables employees to enroll in learning opportunities with no upfront costs. This payment option will require the employee to sign a Family Educational Rights and Privacy Act (FERPA) agreement with Modal to release grades/completion to Booz Allen to satisfy the FlexEd Program completion requirement.
Note, Modal may also be used for the FlexEd Program reimbursement payment option. See the full FlexEd Program Policy & FAQs.
Need help? Contact us