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Don’t be a one-trick data pony!

I want you to walk out of these courses with the lay of the full analytics landscape, from Excel to Python and back again via GitHub, SQL and more.

Not just that, I want you to be able to think critically about exploratory and confirmatory data analysis and feel comfortable with the entire analytics process and working with other analysts. 

This is your tour of the complete analytics stack. From here, you’ll be able to dive into other topics and techniques with ease. You’ll be that rare data professional who can build reproducible projects and communicate them.

You can watch 20 hours of videos on a topic… or you can actively make a full suite of tools your own in this series of courses.

• Diversified curriculum
• Personalized project
• Supplemental exercises
• Handouts and outlines for all topics

This approach will give you the competence and confidence to pick up new analytics technologies and techniques on your own in a short amount of time. Given the rapid course of change in the field, this is the most important skill to possess.

Hiring managers and data professionals alike are yearning for people like those educated in these topics… someone who is conversant enough in enough areas of analytics that they can be entrusted to do great work on whatever team or project they’re needed on. You will be that person after this course.

Objectives

Why am I doing this course? Well, because there’s only so much that can fit into one book…

Most analytics courses focus on one topic ad nauseam. Most analytics courses focus on one topic ad nauseam. You listen passively to the speaker deliver obscure features of a tool.

You’re totally alone in your learning, never to meet the instructor or fellow learners. You never get to practice building something yourself, and you really never get to branch out to other tools.

Schedule

Session 1 — 10/19, 12p-1:30p Eastern

• Introduction: Practice and build on exploratory data analysis
• What is analytics, anyway? Relating descriptive through prescriptive, data science, and more
• “An Analytics Presentation in Six Acts:” a framework for communicating data

Session 2 — 10/21, 12p-1:30p Eastern

• Practice assembling and presenting an analytics presentation using Excel
• Learn additional statistical inference methods (ANOVA, logistic regression, etc.)
• Demo: unstructured data analytics in Excel

Office hours — 10/22, 12p-1p Eastern

This is an opportunity to review topics covered in the course, workshop project ideas, or drill on exercises similar to those found in the book.

Session 3 — 10/26, 12p-1:30p Eastern

• What is reproducibility and why does it matter?
• Up and running with version control in GitHub and GitHub Desktop
• Build dynamic, reproducible reports with R Notebooks

Session 4 — 10/28, 12p-1:30p Eastern

• Practice telling analytics stories
• Explore further linear regression diagnostics and assumptions
• Intro to multiple linear regression

Office hours — 10/29, 12p-1p Eastern

This is an opportunity to review topics covered in the course, workshop project ideas, or drill on exercises similar to those found in the book.

Session 5 — 11/2, 12p-3p Eastern

• Introduction to SQL and SQLite
• Read SQL data into Python

Session 6 — 11/4, 12p-3p Eastern

• Build a classification model in Python
• Introduction to Python for Excel: analyzing and writing data in pandas and xlsxwriter
• Discuss capstone project

Office hours — 11/5, 12p-1p Eastern

This is an opportunity to review topics covered in the course, workshop project ideas, or drill on exercises similar to those found in the book.

Session 7 — 11/9, 12p-2:30p Eastern (note longer time)

This is it! It’s your time to share your personal capstone project with the cohort and the world. We’ll also explore even more methods for blending our slices of the analytics stack together.

• Capstone presentations: how to present and evaluate data analyses
• Using R and Python in Power BI
• User-defined functions in Python for Excel with xlwings
• Wrap-up

Datasets

You know what they say (or at least I do)… an analytics course is only as good as its datasets. We’ll stick to the real thing, with interesting and important data ranging in a variety of subjects:

• Home sale prices
• Healthcare measurements
• Major League Baseball team and player records
• Reviews left on Yelp

As you’ll see, we’ll mix a bit of business and pleasure with this data .

Capstone

This is probably the part of the course I’m most pumped for. It’s your chance to apply what you’ve learned in the course on a dataset of your choice. This can be modified work from your job, something found on places like Kaggle or Data is Plural, you name it.

Plenty of analysts can provide a cursory overview of the data. You’ll be able to confirm whether what you see is likely to be a fluke, and convert technical findings into business insights.

Some types of questions you might be able to answer for your capstone:

• Are return rates higher for our store in the month after the holidays relative to the rest of the year?
• Does an NPS score significantly influence whether a customer churns?
• Does one campaign headline result in more engagement than another?
• Is any one product line significantly faultier than any other?

What you’ll get

For each course, you’ll get these goodies for the following prices:

$1200

• Seven sessions
• Three office hours
• Class recordings
• Demo notes and handouts
• Class Slack channel
• Practice activity and solutions bank
• Certificate of completion
• Free download of First Steps in Power Query for Excel video course ($100 value)
• Free download of Advancing into Analytics Solution Demos video course ($45 value)
• First five signups! One-on-one coaching session
• First five signups! Signed book and swag (US only; we’ll arrange something if you’re international)

FAQs

What are the computing requirements?

Please have a Windows computer available for this course; some topics aren’t available for Mac and I’m less familiar with it anyway.

Do I need to read the book first?

You will get much more out of this course if you have read Advancing into Analytics first. We will practice and build on the topics from it.

Is this going to make me a data scientist?

Depends on how you define data scientist, but this course is really meant to help you become a full-stack analyst who is comfortable working with business intelligence, data engineering and data science teams alike.

Why should I learn R or Python when there’s Power BI, Power Query, etc.?

Because Microsoft wants you to. Did you know you can work with R and Python right from inside these tools? The Full-stack Analyst will show you how. Plus, Microsoft owns GitHub — so you’re really not leaving the suite of tools intended for you to use.

My team isn’t using R, Python, GitHub, etc. How will I benefit?

I admit in the book that many data analysts do fine work without these tools. That said, having this additional skillset is to your strong competitive advantage:

• Save hours of work with repeatable data analysis techniques
• Monitor and audit file histories with version control
• Build statistically sound analytics projects with industry-accepted tools
• Generate sophisticated visualizations with tools used by top data journalists and researchers
• Augment and automate your Excel workflow with free, frequently-updated packages

What people are saying

I’ve been sharing this book with analysts in my department to get up to speed with analytics in Python and R. The way it builds on Excel really makes for an intuitive transition. This will help foster deeper data literacy in the department and will provide opportunity for growth and professional development for the analysts.

Michael Zelenetz, Director of Data Management and Analytics at White Plains Hospital

The book was written in a way that helps readers transfer their knowledge about data into new tools and techniques in an approachable manner.

Nicole LaGuerre, Learning & Development Consultant at s1mplythebest.com

A perfect introduction into the first few tools one should investigate for data analytics while alluding to business intelligence platforms and other tools.

Matthew Bernath, Head of Data Analytics at Rand Merchant Bank

Not only will the reader become comfortable with using the most popular data analysis programming languages, they will also learn about techniques to wrangle, explore, and visualize data so that it comes to life and tells a story.

John Dennis, Data Analytics & Data Science Consultant

Let’s do this. Registration closes Friday, 10/15

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Questions about payment? Interested in the course, but these plans don’t work? Drop me a line and we’ll talk.

I’ll be in touch with course details after you pay.

I am so excited to work with you on this.

See you soon…