Review: Data Analysis with R Specialization by Duke University

Every profession can benefit from being more data-driven. As an aspiring analyst, or someone looking to upskill with stronger analytical chops, we'd recommend checking out Duke University's Data Analysis Specialization. Learn practical concepts to assist with things like data-driven decision making and statistical hypothesis testing.

This specialization will also teach you how to analyze and visualize data in R to create reproducible data analysis reports. It will also cover experimentation and statistical inference, showing off both frequentist and Bayesian statistical models to understand natural phenomena and make data-based decisions.

By the end of the program, you will be able to visualize and communicate statistical results correctly, clearly, and in context to justify strategic decisions and projects.

The three courses to complete the specialization are:

• Introduction to Probability and Data with R
• Inferential Statistics
• Linear Regression and Modeling

The material is taught by real industry professionals and covers the basics to become a digital analyst-- either marketing or product-- very quickly and efficiently.

If you only have time for one course and want to be extra dangerous with data this year, we especially loved the course on inferential statistics. To get personal, we at Bridged got our start as a product analysts with specializations in A/B testing and hypothesis execution. A/B testing is a crucial career infrastructure step to improving a product or learn effectiveness of various marketing strategies.

If you're familiar with statistics and testing, you can break in to several different technical careers, including UX researcher, product manager or almost any type of analyst. Putting a statistics course with bayesian influences from Duke on your resume is definitely a way to show-off an upskill.

The curriculum is meant to be consumed over a 17-week period, but it can also be completed at your own pace.

We’ve recapped the learning objectives from each week to set your expectations for course material. The great part about this program is that you can jump to any course, and any section if it’s interesting to you. For example, if you’re an established analyst only looking for A/B testing or statistical concepts, jump to course 2 (OK, we know you get it). However, you only can get the certificate if you complete all 17 weeks of content.

To audit an individual week-- find the exact course (we've linked them individually here) and click "audit" to save it to your profile. Then open the desired week on the side panel that aligns with our recaps.

Course 1: Introduction to Probability and Data with R

Learning Objectives from Week 1: Introduction to Probability and Data

• Introduction to sampling and exploring different types of data.
• Basic probability theory.
• Examination of different sampling methods and how they impact data analysis.

Learning Objectives from Week 2: Data Deep Dive

• Explore data via numerical summaries and visualizations.
• Understand rules of probability and commonly used probability distributions.
• Conduct data-driven studies for cohesive evaluations.

Learning Objectives from Week 3: Data Analysis Concepts with RStudio Project

• Download R and RStudio installed on your local computer or through RStudio Cloud.
  
• We love the hands-on approach this course takes with R. The more usage, the more proficient you'll become!
  

Learning Objectives from Week 4: Exploratory Data Analysis and Introduction to Inference

• Delve into analyzing numerical and categorical (otherwise known as quantitative and qualitative!) data
• Start to get familiar with statistical inference-- otherwise known as the process of learning things about a population based on a reliable sample size of data.

Learning Objectives from Week 5: Inference Project RStudio Project

• Practice using inference to make decisions about a product's users based on a sample.

Learning Objectives from Week 6: Introduction to Probability

• Understand basic concepts from probability and conditional probability.
• Learn Bayes' Theorem & Bayesian Inference.

Learning Objectives from Week 7: Probability Project with RStudio

• Practice various probability concepts with real user data.

Learning Objectives from Week 8: Probability Distributions

• Introduction to the normal and binomial probability distributions.
• Exploration of research questions using real data and statistical methods.

Course 2: Inferential Statistics

Learning Objectives from Week 1: Course Introduction

• Walks through Coursera basics and specialization recommendations. Feel free to skip this!

Learning Objectives from Week 2: Central Limit Theorem and Confidence Intervals

• Cover the basics of statistics and how to use a sample size as a representation for a general population, including the tradeoffs between sample size and sampling variability.
• Walk through sampling variation definitions and applications.
• Learn practical statistical calculations for error, sample size, standard deviation. This is more math heavy. Stick with it!
  

Learning Objectives from Week 3: Inference and Significance

• Identify when and why to use formal hypothesis testing using confidence intervals.
• Focus on decision errors and statistical vs. practical significance.

Learning Objectives from Week 4: Inference for Comparing Means

• Learn the T-Distribution and how to compare means. This is incredible applicable to A/B testing and probably the most valuable week of the class.
• Bootstrap a simulation to achieve sample size for a confident result.

Learning Objectives from Week 5: Inference for Proportions

• Study inference for categorical (qualitative) data.
• Complete a project to answer a data analysis question around experimentation.
  

Course 3: Linear Regression and Modeling

Learning Objectives from Week 1: Course Introduction

• Very similar to week 1 of course 2 (Inferential Statistics). Walks through Coursera basics and specialization recommendations. Feel free to skip this!

Learning Objectives from Week 2: Linear Regression

• Learn how to use linear regressions to plot charts and make predictions.
• Identify how and when to draw conclusions from a data analysis.

Learning Objectives from Week 3: Continued Linear Regression

• Study outliers, inference and testing with in linear regression and variability partitioning.

Learning Objectives from Week 4: Multiple Regression

• Practice modeling numerical response variables using multiple predictors (numerical and categorical).
• Learn concepts around multiple linear regression, model selection, and model diagnostics.
• Create a report using data analysis (EDA), modeling, and prediction on a data set from Paramount using RStudio.

Quick Notes

This program heavily focuses on R, which is primarily a data scientist's tool. If you're looking for web analytics, there are other programs (view our alternative course recommendations) that may be more suited for your needs.

There's an argument against R for Python-- most companies will accept either, and many of the concepts are interchangeable beyond the syntax. Maybe try auditing first to figure out what program is best for you!

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The program is only $49/month, and comes with a Linkedin Certificate on behalf of Duke University (remember– this is a prestigious place!!). If you complete the curriculum on the proposed timeline, it should take about 4-5 months, though you could blitz through it on a break in far less. While that seems steep, compared to a degree or bootcamp this micro-certification is a steal!

If you have a learning budget, or are dedicated to upskilling your career with a data-focus– we recommend paying for and completing the program to get the shareable certificate (GET RECEIPTS!). This will help make your Linkedin more searchable to recruiters who may be looking for specific keywords and programs.

To audit the program and simply learn the material, this program is completely free! Thanks Coursera!

This is a newer program and does not have too many reviews (each course has between 1,600 and 5,400 ratings). However, from the reviews it does have, the courses within the specialization are very highly reviewed by real and aspiring analysts alike– averaging between 4.7 and 4.8 per course!

Some of our favorite positive review points:

• Professor has her unique way to explain the concept through various real life examples. I really enjoy the course the whole time. Can't wait to move on to the next course asap. Thanks!
  -AP
• Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.
  -KN

Aggregations of negative review points:

• Not enough math (we agree!)
• Peer-reviewed grading-- so often the grade does not truly match.
  Bridged comment: Peer-graded assignments are often common in micro-certifications due to the low cost of the program.

... and our favorite overall review:

I have a major in mathematics and this is by far one of the best courses I have ever taken on introductory statistics. Instructor explains all the concepts clearly with tons of examples. The labs are very well formed you will never be lost with them. The final project turns out to be fun and informative! Overall, it was a great experience. I recommend it to anyone wanting to get into data science field and/or improve their basic knowledge of statistics and R programming.

-Akshay J.

For the fans of qualitative data: University of Michigan's User Experience  

This class is sponsored by U of M, and has a great rating of 4.8 with 1.5k reviews. With a Coursera subscription (included with your product management specialization, may we add!), you can get a certificate OR audit for free! Read our full writeup here.

For the aspiring product manager: UVA's Product Management Fundamentals

UVA Sponsors a prestigious specialization program through Coursera. This program focuses on test and learn strategies, identifying and acting on customer insights, and running and effective product program. Read our full writeup here. It's free to audit, but if you want the certificate (recommended), it's $79 a month to complete at your own pace.

For the web analytics power-user: Google's Data Analytics Specialization

Google also sponsors a data analytics certificate program through Coursera. This is one of the more coveted certificates in the industry for learning the Google Analytics tool specifically, hence our recommendation of this course from Duke. Google's course is also free to audit, but same rules apply if you want the certificate to show off at $49 a month.

Comparable mid-level program: University of Minnesota's Analytics for Decision Making

The University of Minnesota also runs a great Coursera program with a 4.7 star average. It's free to audit, but if you want the certificate it's covered under a $49/month Coursera subscription. We especially love course 2 for the experimenters out there... you can never go wrong with data-driven optimization strategies.
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This is a newer beginner-level class that has a great overview of types of analytics, and when to use each method to maximize effectiveness.

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Duke's Analytics Program is a great way to sharpen your analytics skillset and expand into emerging technology without breaking the bank with an additional college degree or bootcamp.

Here at Bridged we are huge fans of stacking micro-certifications to achieve desired career results. This program could be one notch in your arsenal to really kick your technical expertise into gear!