My Thoughts: Udacity Data Analyst Nanodegree Review 2024

About me & my motivation

In February 2021, I successfully completed Udacity Data Analyst Nanodegree. I want to share some key takeaways about this nanodegree that will hopefully benefit anyone who aspired to be a data analyst.

Before I go deep into this review of Udacity’s Data Analyst Nanodegree, I wish to share a bit about my professional background and self-development journey in the past, which eventually lead me to Udacity.

I was a Finance Analyst with eight years of professional experience with some reputable names such as IBM & HP. I hold various roles in Finance, including Finance, Planning & Analysis (i.e., budgeting, forecasting & analyzing business outcomes), and Audit & Tax. 

What’s the go-to tool for a Finance Analyst? Yes, you probably guess it, it’s EXCEL.

I’m pretty confident that Excel can deal with many finance tasks, provided the data can fit into Excel. I started to have this issue when I transition into Audit & Tax. 

Frequently, an annual audit requires me to have detailed transactions (usually a full year) of a business entity or a group of business entities within a region. 

Such data comes typically in huge CSV/ XLXS format (easily 100MB+), and I realize Excel doesn’t perform well in such a scenario.

Therefore, I started looking for alternatives over the internet. I needed an option that helps me to carry out financial analysis on a vast data set. 

In the past years, I tried some MOOCs such as Coursera, edX, Datacamp etc, and pick up basic knowledge about SQL, Tableau, Python & R. However, I notice the courses are delivered in the context of the academic world. 

In 2016, I came across Udacity and enrolled in Data Foundation Nanodegree. Finally, I found a platform that teaches real-world practical skills. 

After a few years of self-learning, particularly in SQL, Python & R, and reading some Udacity’s Data Analyst Nanodegree reviews, I decided to give it a try.

The purpose of this article is to find answer to following questions

1. Is the Udacity’s Data Analyst Nanodegree Worth it?
2. Will it help to land in a job?

Let’s keep rolling.

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Right price at the right time

In November 2020, I signed up at a 75% discount (~ $100 per month). I chose a monthly installment plan (yea, I didn’t take the four months with upfront payment) and managed to graduate in 3 months. Get to know about discounts in this article.

To be honest, Udacity courses are way more expensive than most of the MOOCs out there. For example, Coursera offers Data Analyst Professional Certificate @ USD 49 per month covering almost the same topics in this nanodegree.

Personally, I think the price tag is justifiable given Udacity offers career advice on LinkedIn/GitHub profile, mentors support to answer queries from the students and, most importantly, the practical, real-world skills (more details in the Project section of this review of Udacity’s Data Analyst Nanodegree).

You can sign up here.

Course structure & projects of Udacity’s Data Analyst Nanodegree

Welcome to Data Analyst Nanodegree

An orientation to students and introduction to SQL.

The very first project in DAND: Explore Weather Trends. Students are required to extract data from Udacity database with SQL and present findings with a report.

The project is sort of different from most of the SQL course
elsewhere. This project gives the student freedom to use any SQL knowledge to get data; that’s it. Contrary to other courses, I was asked to learn about subquery, temporary tables, join tables, extract substring, and many more. Eventually, I was tested with some MCQs, which sometimes I misunderstood. To me, I think this is theoretical.

This is the part I liked most about the Udacity course. It offers flexibility and freedom to get things done. It emphasizes implementation over information. And, it is practical.

After data gathering, I can choose any tool (yes, Excel & Google sheet) to analyze and present findings. I chose R as it is the most comfortable programming language to me.

In this Udacity Data Analyst Nanodegree Review, I am going to cover entire offerings of the program, including its pros and cons.

Introduction to Data Analysis

This lesson introduces us to Anaconda, Jupyter Notebook, and some
common tools such as Python. The real challenge starts in this lesson.

Project: Investigate Dataset

The project get me to choose a predefined dataset for investigation and datasets are either from real-world business entity or Kaggle.

Here, I would like to emphasize an important note which separates
Udacity from other MOOCs.

Most MOOCs purely test the student on technical skills. For instance, Python programming and test is mostly about fill in the blank in MCQ format.

Each project in DAND requires the student to conform to industry standards. For example, my project needs to comply with coding best practices (i.e., PEP8, reasonable naming convention for variables, etc.), proper use of self-defined function etc., on top of presenting my findings.

Practical Statistics

Students are introduced to some basic concepts: descriptive statistics, probability, distribution, hypothesis testing, A/B Test and regression. We also learn to get hands-on experience to do some calculation with Python Numpy, instead of just learning statistical concepts.

This is a pretty challenging lesson for me. The reason being, I have been out of school for ten years and my job as a finance analyst actually isn’t centered around statistics. I’m glad that I can refresh my knowledge about practical statistics without having to go through an advanced statistics textbook. I probably will fail the advanced statistics test if I study in a classroom.

This lesson comes bundled with project A/B Test analysis on an e-commerce landing page. Thankfully I managed to pass the project by spending few days on the practical statistic.

This project reminds me of the importance of the Python’s Numpy library for statistical analysis, which I never thought about since day one I started my data science journey. At the moment of writing this Udacity’s Data Analyst Nanodegree review, I’ve realized how significant this project is.

Data Wrangling

This was another challenging lesson for me. I was introduced to data wrangling workflow – gather, assess, clean, analyze data, and present findings with visualization.

Project: Wrangle & Analyze Data

I learned a lot and was impressed by this project. At the start of this lesson, I thought this would be the easiest project since I have been learning Python/R for quite some time. The fact proves me wrong.

The project is about gathering data from a popular social media platform. The tricky part is, Udacity up the game of data gathering by breaking the data into few parts. I need to gather data in CSV format, download another part programmatically in TSV format via URL (i.e., website), get some parts from social media platform via Python API, and the last part image prediction file provided in CSV format.

Imagine spending a few days just to get data in different formats from various sources, transform them into proper format and merge them for analysis. I also need to exercise independent judgment on the dataset. I have to ask myself this question: which components are useful for analysis? There is no absolute right or wrong answer to this question.

Cleaning and analyzing data present another challenge. I have been getting used to numerical analysis. The dataset is actually about user comment, rating & image prediction. So I am free to extract words from user comments, perform statistical analysis on rating, etc., on this Social Media Analysis dataset. I am amazed by how difficult it is to extract keywords from user comments in the real world, and it is no wonder Data scientists spend up to 80% of the time for data cleaning.

Data visualization

As the name suggests, the final lesson is about data visualization with Python (specifically Matplotlib & Seaborn). Again, a real-world problem is never easy. Data visualization alone seems a stranger to many, and the issue is amplified when we have to deal with a large dataset (easily 100MB+).

Project: Communicate Data Findings

I chose to present my findings on bike-sharing in the United States. I combined the monthly dataset in 2019, and the full-year dataset is 1 GB+ with 20 million+ observations.

Given the complexity of my project, I will cut short the ‘hardship’ along the journey. In my experience of Udacity’s Data Analyst Nanodegree, I can conclude that exploratory data analysis and visualization on such a large dataset requires a major upgrade on my already-know Python skill.

Fortunately, I learn to import large datasets by breaking them into smaller chunks, leveraging Numpy/Pandas to optimize data processing time, and improving the Matplotlib/Seaborn visualization method. Specifically, on Matplotlib, I learn so much about the object-oriented method.

I’ve done complete analysis of Udacity’s Data Analyst Nanodegree so that you get to know the outcomes and the expectations of this Data Analyst Nanodegree .

How is Udacity Data Analyst Nanodegree?

On top of the challenging projects, some points that are worth mentioning in this Udacity’s Data Analyst Nanodegree review:

1. Mentors are here to review each project. From my experience, some mentors are kind enough to recommend valuable resources for data wrangling or visualization. This is very different from Coursera. Students who are as good/worst as me reviewed my projects. Sometimes, there isn’t anyone to review my project because nobody is interested in the course. Can you imagine I paid for a course and end up dropping out just because of this reason?

GitHub/LinkedIn profile review and career services might be helpful, but I never use such a service. The reason being, I’m not ready for a career transition at this point. I want to build strong fundamentals in Python & R and learn more about Cloud Analytics (GCP/AWS) for data science.

Continue from point 2; I wish Udacity would offer more courses for R programming in the future. I understand that Udacity offers courses based on industry trends that favor Python; however, some students, including myself, prefer R over Python.

Conclusion: Do I Recommend Udacity’s Data Analyst Nanodegree?

I highly recommend DAND to anyone who is inspired to be a Data Analyst, given you already learn the foundation of programming and are comfortable with mathematics/statistics. Of course, self discipline & persistency are equally important.

FAQs

Also Read: Udacity Nanodegree Review 2022

Also Read: Review of Udacity’ Data Scientist Nanodegree