A Data Analysts main responsibilities includes finding, retrieving, wrangling, and delivering insights from data. Data Analyst also help to report and uncover meaningful insights from the data underlying products. Specifically, they are responsible for obtaining, analyzing, and reporting on data ranging from business metrics to user behavior and product performance.
For example, responsibilities may entail:
As a Data Analysts it's important to have a strong combination of analytical (math/stats and programming), communication (presentation/data visualization) skills, systematic approach to problem solving with a high attention to detail, and the ability to apply them in a business context. Below we've outlined a few ways where you can learn some new skills.
There are a number of publicly available datasets on the web—they can be a great resource and provide you with opportunities to build up a portfolio of interesting independent projects. Our friends at Mortar have curated a master list of interesting data sets found by some of the best and well-known data scientists in the field today.
If machine learning is more your style, Kaggle competitions can be a great arena to hone your skills and prove yourself (some companies search the Kaggle leaderboards when hiring!).
To showcase your new skills and projects you can create your own website through GitHub pages, WordPress, Medium, or other webpage or personal blog platforms.
A good portfolio should showcase a series of projects and show the range of skills that you've learned.
Ideally these projects will demonstrate your:
Most importantly, these projects should demonstrate your outstanding communication skills. Specifically, showing that you can analyze complex data sets, find interesting insights, and present them in a clear and simple manner in the right business context.
Learn about the skills a Data Scientist should have.
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Learn what it means and how it fits into data analysis.
If you are interested in becoming a data scientist, you should be competent in and able to apply the following skills in your day to day work.
It is important to have programming skills as a Data Analyst. There are times where you may use a non-programming tool like Excel, but some of the best and most common tools, like Pandas, Numpy, and other libraries, are programming based. With these programming based tools you'll be able to do a more in-depth analysis and much more efficiently. Both Python are R are good programming languages to start with because of its popularity.
At the very minimum, you should be able to understand the fundamentals of descriptive and inferential statistics. You should understand the different types of distributions, which statistical test are applicable to what context, and be able to explain the basics of linear regression in an interview.
The techniques in machine learning are incredibly powerful especially if you have huge amounts of data, and you need to use this data to predict the future or make calculated suggestions. You should know a few of the most common algorithms from supervised and unsupervised learning (they are two different classes of machine learning algorithms), such as k-nearest neighbor, support vector machines, and k-means clustering. You may not need to know the theory and implementation details behind these algorithms, but it's important to understand when to use these algorithms.
In an ideal world, the data sets that you work with will be cleaned and ready to be analyzed. However, in the real world, that's rarely the case. It's very likely that your data sets will be missing values, be ill-formatted, or are entered incorrectly. Let's talk about dates, for example, some systems will represent September 1st, 2014 as 9.1.2014 and some other systems may represent it as 09/01/2014. In situations like this your data munging skills will come in handy.
As a Data Analyst, your job is to not only interpret the data but to also effectively communicate your findings to other stakeholders, so you can help them make a data informed decision. Many stakeholders will not be interested in the technical details behind your analysis, that's why it's very important for you to be able to communicate and present your findings in a way that is easy to understand.
To get you started here are some of the most popular programming languages and tools to become acquainted with.
Our data analyst Nanodegree program will help you learn all of the skills listed, but there are other great resources as well. Here are some of our favorites from our friends: