Data science and machine learning can often require an overwhelming amount of skill. However, after exploring a lot of data science skills, in this article, I wanted to share five common data science skills that all Data Scientists should know.
You will come across even more required data science skills and useful skills throughout your career and practice, but I hope these common skills serve as a good start or improvement to your current journey as a Data Scientist.
Also, Read – Use of Data Science.
Structured Query Language (SQL)
As a Student in Data Science or even an Expert in Data Science, you might be surprised to see this as my first skill. It is typically associated with data analysis, while data scientists focus on a programming language and machine learning algorithms. But, before writing code for your Machine Learning algorithms, you need to collect data.
The most popular skill I can think of is SQL. Most businesses have a database with tables that you can query. The result of the query can be the dataset that you are using for your Data Science model. While you generally don’t need to be an SQL expert to become a successful Data Scientist, there are some key SQL concepts and commands that you need to familiarize yourself with.
Data Science Skills with Python or R
A programming language is a must known thing for Data Science skills. The most used programming languages in Data Science is Python and R. So to see yourself in going towards in the right direction in your journey in Data Science, you must be an expert in Python or R.Â
Python, however, is beneficial because of the large number of libraries or packages that already include common data science and machine learning algorithms. In addition to accessing a lot of interesting information and making your Data Science more efficient, you can also work with object-oriented programming in Python.
Object-oriented programming also helps make your models more efficient, while creating a scalable framework for deployment. Also, it helps your model to be deployed more easily in OOP format, as you can do it yourself or communicate with a software engineer, data engineer or machine learning engineer for your deployment.
Data Science Skills in Jupyter Notebook
When using Python, you can also use the popular Jupyter Notebook tool to organize and find your dataset and run your main set of code. It’s nice to comment in cells as well as create titles and bullet points so you can easily collaborate with other Data Scientists, or if you want to come back to your model in the future and have found cells well documented.
Visualizations Skills
Data Visualization is also one of the most important Data Science Skills. Being able to visualize multiple parts of the Data Science process is extremely important. You might want to visualize the business problem, the dataset, and visualize the model itself. Perhaps the most popular time to visualize in Data Science is after the model has been created.Â
When you explain your results to stakeholders, you are describing complex ideas and results that could be best explained visually. I only use Python’s Matplotlib package for most of the visualizations. If I need to use an Intercative visualization then I prefer the Plotly package in Python.
Data Science Skills in Communication
Communication is a skill that doesn’t focus much on data science skills by educational institutions, but it is an important skill to possess as a data scientist. Before you start with your coding skills in any data science task, you will need to speak with several stakeholders and subject matter experts. You may need to convince them that data science is needed in the first place for the specific situation.
Also, Read – Linear Algebra for Machine Learning.
I hope you liked this article on the most important Data Science skills that every Data Scientist should know. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.
