4 Common Mistakes Amateur Data Scientists Make

As the role of data science grows, so do the opportunities for data scientists in the market. Data analysts and scientists are some of today’s most sought-after professions. 
Since there are many aspiring data scientists coming into the field straight out of college or newly transitioning from another role, there’s an influx of “rookie” mistakes cropping up, both behind closed doors and openly shared in data science forums. What Does a Beginner Data Scientist’s Job Entail?

The following tasks are to be managed by a data scientist when they first start working on a project: 

  • Mine data from different databases to drive product development forward for a business. Develop algorithms and data models to apply to the data sets available at your workplace. 
  • Work with stakeholders to identify opportunities to enhance the worth of the company’s data. Worth is increased by driving business solutions through the data at hand. 
  • Assess the accuracy of all data sources and the data accumulation techniques in place within the organization.
  • Make use of predictive modelling techniques to increase revenue generation, optimize ad targeting and provide the right customer experience. 
  • Develop a test model to improve the testing framework. 
  • Coordinate with the different teams across the organization to ensure proper implementation of the models. Keep monitoring the outcomes through these models and recognize any flaws on a timely basis. Don’t work alone, however; maintain contact with everyone. 
  • Develop tools for accessing data accuracy and evaluating the performance of models. These tools will make your job easy and automate the evaluation of models and analysis tools. 

Common Mistakes Amateur Data Scientists Make 

Data scientists make numerous mistakes when they are first adjusting to the work environment. While errors are an important part of job growth, some are avoidable by learning from the mistakes of others. Here are a few common rookie mistakes and how you can avoid them. 

Failing to Work on Visualizing or Exploring Data 

Data visualization is an extremely important and wonderful facet of the work that a data scientist has to do. Despite its importance, some new data scientists skip over it and hurry on to the model building stage. This can have serious repercussions as understanding the data you have in front of you is perhaps the single most important part of the job. Data scientists need to be inherently curious, and there is no room for skipping over steps. 
Spend time on data visualization, and ensure that you explore the data before any model-building stages. 

Learning Multiple Tools at Once 

Many amateur data scientists are tempted to learn all the tools at once, which can prove to be overwhelming, and may lead to failing to master some or all of the tools before you.  

Stick to one (or few)  tools until you’re confident you’ve mastered them before you move on to another. The key to absorbing new skills is to apply them immediately after learning them, so you should pick up skills as you’re ready to apply them to your job.

Lacking a  Structured Approach to Problem Solving 

A structured approach can go a long way in helping you problem-solve. It helps break down the problem into logical parts and helps you visualize each corresponding solution. .

Follow simple tips to develop critical thinking and a disciplined approach. Approach a problem in steps, rather than trying to solving it as a whole. 

Focusing on Achieving Model Accuracy over Interpretability and Applicability 

Model Accuracy is a goal, of course,  but if you’re unable to explain how you got the 96 percent accuracy on a model, and which features led to it, your organization won’t accept the model.

What Skills Are Required?

Working as a data scientist requires a lot of skills and attention to detail. Here are some of the skills that you’ll need to acquire and/or strengthen at the beginning of your career: 
Programming: Regardless of the company or the role that you have to play as a data scientist, it is imperative that you know the ins and outs of programming, as it helps one understand the tools of the trade, like Python and SQL. 

Statistics: We cannot talk enough about the role of statistics in building a great data scientist. Data analysis is an important part of being a data scientist, and to do that you need to have a strong grasp of statistics. The stakeholders in a data-driven company will look up to you to take actionable insight from data results and identify trends from it. 

Machine Learning: It is good to know machine learning algorithms such as random forests and ensemble methods. 

Data Wrangling: The data you receive might not be (and likely isn’t)organized. This is why it is good to know data wrangling methods, which will help you work with impure data forms. 

Data Visualization: Visualizing the data in front of you, by using visualization tools and having sufficient knowledge pertaining to visualization is extremely important. 

The best way to prevent mistake and get the required skills is to focus on applicability and interpretability instead of accuracy. Try talking to some of the industry experts for their opinion and work on it. You can scroll through all of the available data science training courses Simplilearn offers here. Getting educated and certified isn’t a guaranteed way to avoid mistakes—they’re part of the process after all—but they do make for a smooth start to your career in data science.

About the Author

Ronald Van LoonRonald Van Loon

Ronald is named one of the 3 most influential people in Big Data by Onalytica. He is also an author for a number of leading big data & data science websites, including Datafloq, Data Science Central, and The Guardian, and he regularly speaks at renowned events.

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