Who the course is designed for:
You are a numbers person and mathlover who wants to hone in on statistics skills in order to apply them to data science projects. You wish to prioritize accuracy and avoid fallacies while building towards data science success.
Outcomes
 An understanding of basic statistical hypothesis testing and confidence intervals.
 The ability to model data using well known statistical distributions, as well as handle data that is both continuous and categorical.
 The ability to perform linear regression and adjust for multiple hypothesis.
 An understanding of how to calculate the number of samples needed to achieve required sensitivity and specificity.
 An understanding of bootstrapping and Monte Carlo simulation.
What you'll receive upon completion:
 Certificate of completion
 Certificate link and instructions on how to add to your LinkedIn profile
 3.3 Continuing Education Units
Dates & Instructors
Live Online
Statistics for Data Science
May 1 to June 7
Tuesdays and Thursdays
6:30  9:30pm Pacific Time
Nathan Grossman
Instructor
As a former engineer, Nathan enjoys data science because it allows him to apply the mathematical skill set he developed designing algorithms for individual products to optimize the behavior of entire organizations. Nathan began his career developing signal processing algorithms for wireless communications at Qualcomm. He subsequently worked in data analytics for intellectual property applications, and in data science for telecom applications. Nathan is currently a data scientist at Wells Fargo, working on machine learning for FinTech applications. He holds a BS in Mechanical Engineering from the University of Illinois, an MS in Electrical Engineering from the University of Michigan, and an MBA from the University of Pennsylvania’s Wharton School, as well as certificates in data science and data analytics from UC Berkeley and UC Santa Cruz. When not working, Nathan enjoys skiing, cycling, playing tennis and spending time with his wife and three sons.
Prerequisites
This course is open to beginners, but students should have some experience with coding (Python or R preferable but not required) and have a basic understanding of calculus, linear algebra, and probability. A brief review will be provided but
Before the first day of class, students should familiarize themselves with Chapters 16 of CK12 Foundation’s Basic Probability and Statistics – A Short Course. Each chapter should take between 12 hours to work through.
Considering our immersive data science bootcamp?
Professional development alumni can apply the amount of tuition paid for one parttime course towards enrollment in an upcoming bootcamp upon admittance.
Course Structure & Syllabus
Class 1
Basic Probability, Expected Value, Variance, Point Estimates, Introduction to R
Review of basic probability, including how to compute basic properties of a random variable such as the expected value and variance. Clearly define what is a point estimate and how that varies from a statistical estimate. How to compute these properties will be examined via R.
Class 2
Further Probability, Central Limit Theorem, Law of Large Numbers, Hypothesis Testing
Use probability to calculate probabilities about binomial and normal distribution. Explore the central limit theorem and the law of large numbers to understand how to calculate probabilities of events for averages. This will lead into basic hypothesis testing and an exploration of how to interpret testing results.
Class 3
PValues, Multiple Comparisons, Bonferroni Adjustment
Explore the formal definition of a confidence interval as well as its interpretation. Discuss the issue of multiple comparisons and provide an example of a false positive. Explain the use of a Bonferroni Adjustment as well as the False Discovery Rate.
Class 4
Introduction to Regression, Prediction, Hypothesis Testing for Regression
Given a set of continuous outcomes and predictive variables, create a linear regression model using R. Explain how to use that model to generate predictions for new observations as well as test if any of the coefficients have statistically significant parameters.
Class 5
Model Selection for Regression, Backwards/Forwards, R^2 and other selection criteria
Look at how to select models when using a variety selection criteria such as R^2 and adjusted R^2. Look at backwards, forwards and best subset regression. Briefly cover logistic regression and how/why it’s used.
Class 6
Categorical Data, 2x2 tables, Simpson’s Paradox
Introduce the odds ratio for a 2x2 table as well as a statistical test for independence and introduce 2x2xk table with an example of Simpson’s paradox.
Class 7
Independence, MxN tables and trend, Fisher’s Permutation Test
Go over further examples of independence, along with the introduction of larger tables. Trends and advanced categorical analysis will be covered. Go into Fisher’s exact permutation test to explore what hypothesis testing can be done on small sample sets.
Class 8
Correlation & Causation
Provide several examples of how to calculate correlation for both continuous and categorical variables. Provide how to calculate confidence intervals to determine if the correlation is significant. Explore the correlation implies causation fallacy and provide some counterexamples.
Class 9
A/B testing, Hypothesis Testing proportions, More General Hypothesis
Provide several examples of hypothesis testing as it relates to Data Science and web design. Cover hypothesis testing & confidence intervals for proportions and variance.
Class 10
Sample Size & Power Calculation / Method of Moments Estimation
Work through several examples on how to calculate the required sample size given a specific level of false positives and a prespecified power level. Go into more detail on why it’s only possible to reject or fail to reject a null hypothesis (and not to accept a null hypothesis). Next, switch gears and cover Method of Moments, compare it to MLE, and take a look at a few examples.
Class 11
Bootstrapping, the Information Matrix & Variance Bound
Discuss some options one can use if dealing with small amounts of data, specifically the bootstrap method. Touch upon the information matrix and how to calculate a theoretical lower bound on the variance of any statistic of interest.
Class 12
ExpectationMaximization Algorithm, Bias/Variance Trade Off
Explore details of the expectation maximization algorithm and how it’s used in the presence of latent variables for estimation. Work through an analytical example as well as how to use R to do it. Cover the Bias/Variance tradeoff when modeling and the pitfalls of overfitting.
Live Online Interactive Learning
Learn from worldclass data science practitioners.
Our Live Online instructors bring deep industry experience from a broad range of industries and companies including Viacom, Spotify, and Capital One Labs. You’ll have an Instructor and Assistant Instructor to support you throughout your learning process.
Interact with instructors and classmates in realtime.
This course is truly live, which means you can interact with the instructors and your fellow students in realtime. Stay engaged by asking questions and participating in polls and conversations, and join your course Slack channel for additional support, communication, and collaboration.
Learn online without sacrificing the value of live instruction.
The world is your classroom. Log in from wherever you are and gain access to live, interactive data science instruction that will push your career further in the right direction. In case you have to miss a class, you can access all recordings 24/7 to stay caught up and refer back.
Earn CEUs for accredited courses.
Not only will you walk away with new data science skills and knowledge, you’ll also earn up to 3.3 Continuing Education Units (CEUs). Our courses are accredited by ACCET, who requires we maintain high standards in areas such as quality of instruction and positive student feedback.
Register for an ondemand sample class
Our 1hour ondemand sample class is a great way to preview what the Live Online experience is like for the Statistics for Data Science professional development course.
Nathan Grossman, instructor of the Statistics for Data Science course, will cover a few introductory basic Python programming topics and mathematical principles.
 Brief Introduction to Probability
 Law of Large Numbers
 Introduction to Random Variables
 Followed by Q&A
FAQs

How much do parttime courses at Metis cost?
Inperson professional development courses are $2,100. Live Online professional development courses range from $1,250$1,900.

Can I apply tuition from my parttime course to the bootcamp?
Yes! Parttime alumni can apply the amount of tuition paid for one parttime course toward enrollment in an upcoming bootcamp upon admittance.

Do I receive pass/fail grades on completion of a parttime course?
No, you receive a certificate of completion stating that you completed up to 36 hours of the course, accredited by ACCET (Accrediting Council for Continuing Education and Training). Hours vary by course.

How much homework is required outside of class time?
While there is no official homework, you can expect to spend a minimum of 3 hours per week reviewing material or working on projects. The nonclass time spent will depend on your background and the course itself. Each instructor will address this on the first day of class, and there will be lab/office hours outside of class during which students and the instructor can collaborate.

Will we work on any projects? What are some examples of projects students have completed in the past?
No, there are no projects in this course.

Do I get career support if I take a parttime course?
No. We do not offer career support for students of these courses like we do for our bootcamp students, but you will gain access to our alumni community network of 500+ data scientists. Networking events and job opportunities are posted on a regular basis in this active digital community.

Who are the instructors for the parttime courses? Are they bootcamp instructors? What are their backgrounds?
Our parttime course instructors come to teach at Metis from industry and are not bootcamp instructors. Please visit the respective course pages for specific information on each instructor’s background and current jobs.

How often do Metis parttime courses meet?
Our parttime courses typically run two nights per week over the course of 6 weeks, totaling 36 hours of instruction, but this can vary. Please see the full schedule here for the most uptodate information. We consistently add new courses, so be sure to check back routinely

Does Metis offer any parttime courses online?
Yes, we currently offer a rotating selection of our parttime professional development courses in a Live Online format, meaning once registered, you can login from anywhere to learn.

What are the benefits of taking a course in a Live Online format?
The beauty of the Live Online format is that you’re taught by our industryleading instructors live, but you can attend class sessions from literally anywhere you have an internet connection. Unlike some other online course options out there, which might consist of prerecorded lectures, our courses allow for interaction with the instructor, teaching assistants, and other students – and because these are on a set schedule, you’ll be held accountable to actually attend, do the work, and learn the material (which is what you’re really here for anyway!).

I took a Metis parttime course and now want to apply for the bootcamp. Does that give me a competitive edge?
It does not, simply because we evaluate each and every bootcamp applicant the same way in order to ensure fairness. However, if accepted into the bootcamp, you do have the advantage of applying the amount of one parttime course tuition toward the bootcamp tuition, so you essentially got your parttime course for free.

Can I use Metis parttime courses for school credit?
Yes, each parttime course earns you up to 3.3 Continuing Education Credits. CEUs vary by course.