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10 Practical Data Science Skills You Need to Master in 2022

by Ruby Gracy
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data science skills

Many How to Do Data Science courses and articles tend to focus on fundamental skills like statistics, math, and programming. However, these fundamental skills can be difficult to turn into practical knowledge that will help you get a job.

Below is a unique list of practical skills that will play a leading role in your employment in 2022.

The first four are key for any data scientist, no matter what you specialize in. The remaining skills (5-10) will vary depending on your specialization. 

For example, if your focus is on statistics, then you might want to focus on the area of ​​statistical inference. Conversely, if you are more interested in text analytics, then you can concentrate on learning NLP. If you are interested in decision theory – focus on explanatory modeling, etc.

1. Writing SQL queries and building data pipelines

Knowing how to write robust SQL queries and schedule them on a workflow management platform like Airflow will make you an extremely in-demand data scientist, which is why it’s #1.

Why? There are many reasons for this:

1. Flexibility : Companies love data scientists who can do more than just model data. Companies LOVE full-stack data scientists. If you can help build core data pipelines, you can improve the information you collect, create more reliable reports, and ultimately make life easier for everyone.

2. Independence : There will be times when you need a table or view for a model or Data Science project that doesn’t exist. Being able to write robust pipelines for your projects instead of relying on analysts or data engineers will save you time and increase your value.

Therefore, you MUST be an expert in SQL as a data scientist. No exceptions.

2. Data transformation/feature engineering

Whether you’re building models, exploring new opportunities to build, or doing deep dives, you need to know how to process data.

Data Wrangling  means converting your data from one format to another.

Feature Engineering  is a form of data processing thatbut specifically refers to extraction features from the raw data.

It doesn’t matter how you handle your data, whether you use Python or SQL, but you should be able to manipulate your data however you like (within the limits of the possible, of course).

3. Version Control

By “version control” I specifically mean  GitHub  and  Git . Git is the main version control system used in the world, and GitHub is essentially a cloud-based repository for files and folders.

Although Git is not the most intuitive skill to learn at first, it is important to know it for almost every single coding role. 

4. Narrative (i.e. communication)

It’s one thing to create a visually pleasing dashboard or complex model with over 95% accuracy. BUT, if you can’t communicate the value of your projects to others, you won’t get the recognition you deserve and, ultimately, you won’t be as successful in your career as you should be.

Storytelling refers to “how” you communicate your ideas and models. Conceptually, if you draw a parallel with a book containing pictures, then your ideas/models are the so-called pictures, and “narration” refers to the story that connects all the pictures.

Storytelling and communication are very underrated skills in the tech world. It is they who distinguish juniors from seniors and managers.

5. Regression/classification

With the construction of regression and classification models, that is, predictive models, you will not always work , but employers expect you to have these skills if you are a data scientist. 

Even if it’s not something you’ll be doing often, you need to be able to do it in order to be able to create high performance models. Some data scientists may only create a couple of machine learning models in their entire career path, but they will be mission-critical models and have a significant impact on the business.

Therefore, you should be well versed in data preparation techniques, advanced algorithms, hyperparameter tuning, and model evaluation metrics.

6. Explanatory model

There are two types of models that you can build. One is a predictive model that assumes an outcome based on a set of input variables. The other is  the explanatory model, which is not used for forecasting, but to better understand the relationships between input and output variables. 

Explanatory models are usually created using regression models. The reason is that they provide many useful statistics for understanding the relationships between variables.

Explanatory models are greatly underestimated because incredibly helpful. They are essential if you want to get into the realm of decision science.

7. A/B testing (experimentation)

A/B testing  is a form of experimentation where you compare two different groups to see which one performs better based on a given metric.

A/B testing is perhaps the most practical and widely used statistical concept in the corporate world. Why? A/B testing allows you to combine hundreds or thousands of small improvements, which over time will lead to significant changes and improvements.

If you are interested in the statistical aspect of data science, it is important to understand and study A/B testing.

8. Clustering

It’s not always data scientists who have to use clustering in their careers, but it’s a core area of ​​data science that everyone should at least be familiar with.

Clustering is useful for a number of reasons. With it, you can find different customer segments, you can use clustering to label unlabeled data, and you can even use clustering to find cutoff points for models.

9. Recommendation

The recommender system is one of the most practical applications in data science. Referral systems are so powerful because they can drive revenue and profits. In fact, Amazon claimed to have increased its sales by 29% thanks to its recommender systems in 2019. 

So, if you’ve ever worked for a company where users have to make  choices,  and those choices are big , recommender systems might be a useful application to learn.

10. NLP

NLP, or Natural Language Processing , is a branch of artificial intelligence that focuses on text and speech. Unlike machine learning, NLP is still very far from the maturation stage, and this makes it as interesting as possible. 

NLP has many uses…

  • To analyze the sentiment of a text to see how people feel about a business or business products;
  • To monitor the company’s social media, sharing positive and negative comments;
  • NLP is the core of building chatbots and virtual assistants;
  • NLP is also used for text extraction (document analysis).

All in all, NLP is a really interesting and rewarding niche in the world of data science.

Thanks for reading!

We hope that this list of practical skills will help you in mastering Data Science and give you the right direction for the coming year. Good luck with your studies! 

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