As more data is uncovered and collected across industries, businesses are finding a need for qualified candidates who can harness that information to facilitate continuous growth and refinement. McKinsey Global Institute even estimates that by 2018, the job market will see a need for over 1.5 million Data Analytics managers. When applying and interviewing for such jobs in this field, your experience certainly matters. But, it’s a data portfolio (like Rose’s here) showcasing your tangible experience that employers are looking for.

While many other, traditional programs and degrees teach you the skills you’ll need to know in theory, they don’t provide you with the real world experience utilizing those hard skills sought after by employers. If you are looking to break into analytics, and have a background using analytical tools and skill sets, but don’t yet have a data portfolio to show employers, there are a number of resources you can pull from. Here are a few that you can leverage to gather data and build up your portfolio of work:

Data Resource 1: Chicago Data Portal

The Chicago Data Portal includes data on reported incidents of crime from 2001 to present, and reflects information up to the previous 7 days, containing more than 65,000 records. One caveat to note is that the data cannot be viewed in Microsoft Excel, so when downloading, you’ll want to open it in a text editor, like Sublime Text, or use R for instance. You could use this data to create a dashboard or data map visualization, breaking down date, offense type, location, and more in a digestible manner.

Data Portfolio Resource 2: Sentiment140

Another source you can leverage to gather datasets is Sentiment140. The data allows you to discover the sentiment of a brand, topic or product (positive, neutral, negative) as discussed on Twitter. A few of the fields provided are the polarity of the tweet, the date of the tweet, and the text of the tweet. For example, you could use the data set to gain insights that would help plan and influence brand management by seeing the sentiment of a particular brand like say, Coca Cola, over time. (We all know Kendall Jenner didn’t help.)

Data Portfolio Resource 3: Amazon Web Services

Amazon has a great variety of data to share through their AWS Public Datasets repository. One such source is the Wikipedia Page Traffic Statistic V3 dataset. The Wikipedia Page Traffic dataset contains a sample of data used to power, including hourly page traffic statistics from Wikipedia over the duration of 3 months: January 1, 2011 – March 31, 2011. You could use this information to analyze and create a visualization of top trending topics over specific times, seeing what changes, what stays relevant, etc regarding brands, cultural influences and more.

Data Portfolio Resource 4: Data SF

The DataSF Website hosts hundreds of open datasets from the city and county of San Francisco, across more than 34 different publishing departments. Some data you can pull to create a map visualization includes crime rates, 311 cases, citizen complaints, film locations and more. If you are interested in a specific industry, this site is a great resource to leverage as it most likely has data related to that industry to help you build a portfolio that speaks to and reflects your career goals.

Data Portfolio Resource 5: Kaggle

Kaggle provides a Rotten Tomatoes movie review dataset that you can use to perform a sentiment analysis on movie reviews. You could showcase application of your skills with R and Python to “classify the sentiment of sentences from the Rotten Tomatoes dataset.”

The Level Data Analytics experiential learning programs combine skills with invaluable networking and hands-on experience analyzing data. Each unit of the program is built around a case study where you work with real data sets to speak to goals and solve for issues seen by C-suite executives. This culminates in a Capstone project, as you partner with a big name or local company and act in a analyst role to consult, scope, execute and present a project that they can use to refine processes, gain insights and more. Click here to learn more and apply to one of our fall sessions!

level data analytics data portfolio


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