It’s a fact that data science and software development are fast-moving industries and anyone who wants to grow in their career needs to have skills in both areas. But what skills are the most critical to get a long-term, high-level career in data science?
Skills required for data analyst
- Database administration skills Data analytics skills Data visualization skills Data processing skills Data mining skills Big data skills Content analysis skills In many respects, the developers of data science software are the unsung heroes of the business world.
- Data scientists spend many hours finding interesting and valuable information and software developers need to make that information readily available to the rest of the company.
- If you are a developer, you can develop the analytical software that powers so much of what’s great about data science software. If you are a data scientist, you can analyze the data and turn it into actionable insight for your company.
- Source – thedatamarche.com Statistics is crucial when it comes to making predictions. If you want to grow in a data science role, the foundation needs to be solid. This will require acquiring statistics skills. However, you don’t necessarily need to have a masters degree in statistics.
- You can take Stats 101 and get started. Network coding Source – info-strategies.com Data science doesn’t necessarily require advanced programming skills, but you will need to be able to code to do the job effectively. It is important to pick up some common coding languages.
- There are many free online tools to help with this. For example, if you want to learn how to write code for TensorFlow, there are many resources available to you online. Python is the most popular for data analysis.
Data visualization skills
Visualization is one of the most important skills for a data analyst. Most companies today are starting to use data to drive their decisions, so the analyst needs to have some skills in visualizing information.
Data visualization can be broken down into several broad categories:
- Charts, graphs, and infographics.
- Charts – Tables with fields for data points Graphs
- Lines and shapes that represent relationships between groups of data Infographics –
- Images that display facts or other visualizations But it’s not just for analytics, there are a number of other ways to visualize data to make it more visual.
- For example, a developer could make a graphical representation of log data that can be used for diagnostics.
Understanding of Machine Learning
- Whether you’re a data scientist or just someone who has a curiosity about learning how to implement machine learning (ML) models, it’s important to have a knowledge of ML. In many ways, data scientists are also data scientists.
- To become a master at ML, you need to understand the concepts behind different AI techniques. One of the most popular techniques is deep learning, which involves learning specific neural networks (which can be created with a large number of processing units) to carry out specific tasks.
- You can use this knowledge to begin automating processes and improving products on your own, or you can collaborate with other companies to create tools that help others improve their own data.
Data wrangling skills
This one is easy: you must be great at data wrangling.
- Data wrangling is a sub-set of data science that requires the ability to query, manipulate, and visualize data for insights.
- It’s often described as data mining for data scientists, but there’s more to it than that.
- More than 90% of all data science roles require data wrangling and most data scientists need to have more than a passing understanding of it, so it’s important to hone this skill in graduate school.
- We’ve developed a guide to data wrangling and it covers everything from how to use scripting languages like R to extract data to how to format data to put it in a user-friendly format, among many others.
Knowing how to write code can be an important skill to pick up, even if you don’t plan to work in software development. You can think of coding as the foundation of data science and many of the same skills can be useful in other fields as well.
The list of data science skills is long and diverse. We’ve tried to present what we consider to be the most important and helpful skills and tools, but as a data scientist, you should always be on the lookout for new techniques or tools that may be useful.
If you have a suggestion for a skill that’s missing here, we’d love to hear about it. For more on data science, check out the latest book, Data Science from Scratch by Joel Grus
Related article: 5 essential non-technical Skills for employment