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Top Advanced Data Science Skills To Equip Yourself With For 2023
Top Advanced Data Science Skills To Equip Yourself With For 2023

September 19, 2022

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The discipline of data science is quickly becoming the one that attracts the youngest brains. It is more about practical application than just theoretical knowledge. The ability to apply data science in real-world situations helps understand market issues. All general practitioners and specialists still need a fundamental set of data science from scratch if they wish to be employable with full-stack skills. Let's look at the top real-world data science skills you should have by 2023. 

 

These are the top data scientist abilities that will enhance their resumes significantly.

 

  • Data Wrangling

Data wrangling is essential if you create models, investigate novel characteristics, or even go deep. Data wrangling is the process of converting data between different formats. One of the useful data science talents to develop is this one to remain in demand in the job market. Feature engineering, which is a subset of data wrangling, is the process of removing features from raw data.

 

  • Writing SQL & Building Data Pipelines

As a data scientist, learning how to create strong SQL queries and schedule them on a workflow management system like Airflow is essential. It will simplify things for you by assisting in developing basic data pipelines and enhancing the obtained insights. You'll also save a tonne of time if you can create reliable pipelines for your projects rather than relying on data analysts. 

 

  • GitHub

In essence, GitHub is a cloud-based storage system for files and directories. Although Git is not the first skill to learn, it is crucial for practically every coding-related position. Git lets you collaborate and work on multiple projects at once. It records every revision of your code. 

 

  • Data Storytelling

To construct a visually attractive dashboard with over 95% accuracy, one needs to have outstanding communication skills to communicate ideas and goals to others. So storytelling refers to how you present your theories and ideas. The tech industry drastically undervalues communication and storytelling abilities. Therefore, the ability that can set juniors apart from seniors and managers is storytelling. 

 

  • Regression and Classification

Although you won't constantly be working on building regression and classification models, companies will want you to be familiar with them if you are a data scientist. Mission-critical models significantly impacted the firm to put things in perspective. One of the useful data science talents to develop is this one to remain in demand in the job market.

  • A/B Testing

In an experiment known as A/B testing, two groups are compared to see which performs better on a specific metric. The most useful and popular statistical idea in business is A/B testing. Why? By Combining 100s or 1000s of tiny improvements, A/B testing enables you to make big modifications and advancements over time. A/B testing is crucial to comprehend if you're interested in the statistical side of data analytics.

 

  • Explainable AI

Many machine learning algorithms were regarded as "brick boxes'' for a long time because it wasn't evident how these models arrived at their predictions based on the inputs they were given. Similar to the coefficients in a linear regression equation, SHAP and LIME are strategies that inform you of both the impact on the model output and the relevance of each feature. You can develop explanatory models with SHAP and LIME and more clearly explain the reasoning behind your predictive models. 

 

 


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