Common Mistakes in Data Science

Common Mistakes in Data Science

Part 2

Continuing from the previous episode, we present 10 more Data Science bloopers that data science professionals should steer clear of:

We continue with our list of common Data Science blunders that industry-observers point out. Here are 10 more of them:

  • Setting wrong priorities: Data scientists often tend to prioritise targets to hypotheses. This is because they prefer numbers as the most reliable measure of performance. However, the importance of meticulously testing the hypothesis, either with a control group or through a thorough analysis of data is crucial. Data accuracy, although important, is not the only factor of a good model. The accuracy of a solution depends on the algorithm, the data and the parameters set.
  • Relying on past data models: Not building new, additional data models can be a big problem. Sticking with old models that have clicked in the past will prevent you from keeping up with market trends. As requirements change with time, new models should be devised to play around with. Each problem has its own variables and needs a customised approach.
  • Choice of inappropriate tools: Advanced tools always sounds like a fascinating proposition, but not everything cutting-edge can always serve your purpose. Rather, more basic tools like decision trees or logistic regression often works perfectly. It’s always best to start with simpler approaches and gradually progress. Sophisticated algorithms are not always required. Investing time in the planning stage to select the most appropriate tools for a project will save a lot of effort in the long run.
  • Relying on intuition: Data scientists often work on theories based on intuitive predictions regarding what a particular data set might reveal. This is common – but can lead to vital misinterpretation of data. Doing an exploratory analysis of the collected data is more important, rather than relying on individual hunches.
  • Know your relationships: Two kinds of relationships are vital in data analysis – correlation and causation. While correlation is a statistical technique that determines the existence of a relation between two elements, mere existence of a relationship doesn’t imply a cause-and-effect matrix. Proper testing of data can eliminate such false conclusions.
  • Undermining business needs: Data professionals often tend to undermine the judgement of their business clients. Business people possess vast industry experience that helps them interpret certain crucial data parameters. And being in the business, often they can be better than the data consultant they hire. Data scientists should always work in tandem with them to perceive the business angle and produce the best results.
  • Not making business decisions: Data science covers a wide range of business applications and scenarios. At the end of the day, it is all about making informed business decisions. Data has no value unless it is channelized for some purpose. A data professional needs to keep in mind the reason why a particular dataset was collected, and how the analysis results will be leveraged. Think from the business perspective.
  • Forgetting that data can mislead too: This might sound too basic, yet it is a common hurdle in practice. Data, in itself, is lifeless – it is the analysis that makes it speak. You might start with the wrong dataset only to spend time in course-correction later on. Validating your data should be built into your strategy at the planning phase. Several factors can spoil the quality of your data, the most common being:
    • Dynamism
    • Errors
    • Lack of balance
    • Omissions
    • Outliers
    • Redundancy
    • Variability
    • Volume
  • Focussing too much on the entire dataset: It is often neither practical nor needed to use an entire dataset obtained. Undue temptation to use all of it may lead to the inclusion of imperfections and redundancy. If the fraction of your dataset containing imperfections is minor, then you may simply eliminate the subset of imperfect data from your dataset. However, in case improper data is significant, data imputation techniques could be used to approximate missing data.
  • Mind your language: Finally, you need to know how to express your findings in a way that is comprehendible to your end users. Statistical jargon, ML models, technical acronyms and latest technologies are for you to perceive and use as the data professional. It is not expected of your business clients to either know or understand them. Presenting your data analysis in a way where the business user gets your point without elaborate technical explanations of models, tools and statistical approaches is always the best.


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