Lantz Wagner graduated Level’s Data Analytics Core program in 2016. Level prepared him to enter into a data role in more ways than one. Here, he describes his takeaways from Level and how they’ve helped him in his professional work.

On the Level experience:

I came across Level while working in the banking industry. I noticed a shift in the climate of the workplace where more data related work was becoming apparent. I wanted to make a change in my career and stay ahead of the curve.

Level provided me with a plethora of experiential knowledge in data analysis tools. In addition to that experiential learning, Level taught me the proper ways to think about data and tasks revolving around data presented to me for when I entered the workforce. I believe this to be exponentially more valuable.

When tasked with your first data set:

There are numerous points you should be conscious of as you enter your first role in the data field. When presented with a data set, it is crucial to get as much specific information upfront as you possibly can. Clients can be vague and leave out details that may be pertinent to the problem you may be seeking to solve. For example, certain data demographics may be in the set given to you, but irrelevant in the client’s eyes. Additionally, you often can’t follow up with the client before the project is due.

First steps after acquiring some background on the project should be asking yourself, what should you prioritize and what is relevant in your data scenario. Speed is essential, but so is what you focus on. Making a plan for what you want to examine before you dive into it will save you time and provide higher quality output. When given vague instructions on an output goal, you have room to explore a little, but remember to balance creative exploration with your focused goal.

Along the way, you will most likely encounter some difficulty finding some useful information to present. If possible, be open to bouncing ideas off of other people on your team. Doing this can give you a different perspective and may help you solve the issue quicker. There are usually multiple ways to arrive at the solution.

When wrapping up a project, it is critical to ask yourself the question: is this data “insightful”? Does it tell you something you didn’t know before that is pertinent to your scenario? If so, keep digging for more reasons or possible causes to further justify your findings when presenting your data to your client.

When tasked with visualizing your data, make sure the visual tells a story you want it to show most appropriately. Knowing your audience and their comprehension level of the data set is vital to communicate your findings.

Final thoughts:

Most importantly, instead of trying to become the ‘Constant Master,’ strive to be the ‘Constant Learner’. You’ll never master it all, but the learning never stops, and that applies to any job in any industry. Mastering that state of mind will help you no matter what path you pursue. Be patient with yourself and perform to the best of your abilities. You’ll find out that most of the people around you are trying to do the same.

Internalizing these ideologies is what will help you succeed in data roles. Grasping the tools for processing the data is the first step. Learning how to be focused on a goal, being open to learning and delivering your results in a clear and concise manner is how Level helped shape me into a successful Data Scientist.


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  1. Lantz,
    excellent advice. After 28years in technology with about 30 years of learning (class, online, reading a book etc), every morning I recognize how little I know. Except for lack of funding, I will not hesitate a minute to get into the program.


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