5 Must-Know Pillars of a Data Science and AI Foundation
Like any skill, there are some core skills you need to know before getting into data science. Without basic foundational skills, your data science journey will end as quickly as it begins. Between foundational mathematics and AI literacy and more, these are the five pillars and skills that you should know if you want to make the most of your data science journey. So let’s get started and dive right in to these AI foundation skills!
Data Literacy
To work with data, you must first understand data. Yes, we know this sounds like an old proverb retrofitted for a modern reader, but it’s true. Being able to interpret, communicate, and make informed decisions about the data you have will make or break you as a data scientist. Improving your data literacy not only involves hard skills, such as programming languages, but soft skills such as interpersonal communication, and stakeholder relations, as well as blended skills such as data visualization.
No matter how amazing the data is, if you are unable to break it down and communicate results to non-technical stakeholders such as business leaders, the results don’t matter — as they’ll lose any and all impact. Finally, data literacy is a key component of data ethics, which ensures that data is used in a responsible and ethical manner.
Coding/Programming
This will likely be the section that needs the least explanation, but we will dive deep anyways! Programming/coding skills are to data scientists as plumbing tools are to professional plumbers. Without the ability to utilize data, create models, visualizations, algorithms, or anything else, you’re left without a story. That’s because coding/programming skills such as R or Python allow you to collect, clean, and manipulate data while also providing avenues to build models or visualizations that allow you to communicate the meaning behind your data.
But it’s not only the ability to work with data, it’s also about scaling your own abilities. That’s because with a good coding/programming AI foundation you’ll be able to automate tasks, scale models, create reusable code which cuts your time, and become even more effective in your role. So don’t neglect to code no matter how mind-numbing it is to write the script “Hello World” may be. Learning is learning.
SQL
Databases might sound scary, but honestly, they’re not all that bad. And much of that is thanks to SQL (Structured Query Language). Believe it or not, SQL is about to celebrate its fiftieth birthday next year as it was first developed in 1974 as part of IBM’s System R Project. Though there have been some refits and improvements, the simplicity and direct-to-the-point nature of this coding language are why it’s still the standard for relational databases.
With that said, because of its wide use, being proficient in SQL allows you to easily work with massive databases that would be either painfully slow on Excel, or impossible. With this programing language, you can retrieve, manipulate, and analyze data from rational databases that house large and complex data sets. This is why having a strong set of SQL skills is one of the must-have skills for any data scientist.
Math
Math is an important skill for data science, even if many programs do much of the calculation for you. The reason is that as you scale up your data science skills and begin to work on algorithms and models, the ability to interpret and communicate their results will be key to understanding which of the latter would be best for a particular project.
One of the superpowers of data science is the ability to create predictive models. To develop a strong enough AI foundation to do this well, you’ll need to sharpen up math skills such as linear algebra and calculus. Linear algebra allows for tasks such as principal component analysis and calculus helps to optimize the parameters of neural networks. Both of these are important to predictive models in data science, machine learning, and AI.
AI
Finally, we get to the last pillar of data science, AI or artificial intelligence. Of course, thanks to the explosion in the popularity of AI over the last few years, the technology has been in the news quite often. But what makes AI so important? Well, the thing is AI allows for advanced techniques to analyze data and make sophisticated predictions based on data. AI algorithms are the foundation of machine learning, deep learning, and NLP — all fields that are currently revolutionization our technological landscape.
So a good understanding of AI and the growing list of techniques such as machine learning and deep learning are key for any data scientist ready to jump into the advanced realms of data science.
Conclusion
This all sounds great, right? These are the five pillars of data science and will build your path forward as an excellent data scientist. Ready to take that step, but you’re not sure where to start? Between now and May 9th-11th, getting an ODSC East 2023 bootcamp ticket will grant you access to a number of live training sessions over the next few months that will cover the above five pillars of data science, all leading up to a week of training in May that will let you use your newfound skills for more advanced topics in machine learning, deep learning, NLP, and more. Here are a few sessions that you can check out soon:
- March 2, 2023: ODSC East Bootcamp Warmup: Data Primer Course
- March 14, 2023: ODSC East Bootcamp Warmup: SQL Primer Course
- April 6, 2023: ODSC East Bootcamp Warmup: Programming Primer Course with Python
- April 26, 2023: ODSC East Bootcamp Warmup: AI Primer Course
And during ODSC East this May 9th-11th, you can check out these bootcamp-exclusive sessions:
- An Introduction to Data Wrangling with SQL
- Programming with Data: Python and Pandas
- Introduction to Machine Learning
- Introduction to Math for Data Science
- Introduction to Data Visualization
- Introduction to Deep Learning with TensorFlow & Keras
- Introduction to Deep Learning with PyTorch
- Introduction to ML using scikit-learn
- Introduction to NLP
- Hugging Face Transformers
- Idiomatic Pandas
- Introduction to Large-scale Analytics with PySpark
- A Practical Tutorial on Building Machine Learning Demos with Gradio
What are you waiting for? Get your ODSC East 2023 Bootcamp ticket while tickets are 50% off!
Originally posted on OpenDataScience.com
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