Diving into data science and the Udacity DAND

March 05, 2017

I have been thinking about starting a blog for some time but I never knew what to write about. So when I decided to enroll in the Data Analyst Nanodegree from Udacity I thought it might be a good opportunity to start a blog as well.

I won’t go into the details here on why I’m starting to study data science, but among the different online alternatives I looked at, I felt that the Udacity Nanodegree offered the best mix of statistics, programming (in both python and R), and project-based assignments. I believe that after completing this nanodegree I will have a good foundation for a portfolio of data science related projects.

At the time of writing, there are one optional and seven mandatory projects included in the nanodegree (See below).

• P0, Analyze Bay Area Bike Share Data (Optional intro to the data analysis workflow and Jupyter notebook)
• P1, Test a Perceptual Phenomenon (Statistical tests and drawing conclusions based on data)
• P2, Investigate a Dataset (Using numpy and pandas to answer questions with and about datasets)
• P3, Wrangle OpenStreetMap Data (Munge and clean up OpenStreetMap data for a part of the world you care about)
• P4, Explore and Summarize Data (Apply exploratory data analysis techniques on a selected dataset using R)
• P5, Identify Fraud from Enron Email (Use machine learning to identify possible fraud with emails and financial data)
• P6, Make effective Data Visualization (Create data visualizations according to current best practices with dimple.js or d3.js)
• P7, Design an A/B Test (Design and analyze an A/B test in order to find improvements to a website)

For anyone interested to know more about the contents all the course material and project descriptions are accessible free of charge. However, you will not be able to send in your project for evaluation without paying.

At the moment I have only finished the first introductory project to get a feeling for the whole submission process, but my experience so far has been good. Submitting my project in the evening the day before, I received feedback already during the next day and the feedback received really helped up my motivation.

The content is not much to talk about, which is to be expected from an introduction, but the next project looks promising. I will continue to update this blog with my results from each of the projects and anything else I might learn on the way that is worth sharing.