How AI is Used in Data Analytics

Table of Contents

• How AI Is Used in Data Analytics
• 3 AI Tools for Data Analytics
• How to Learn AI and Data Analytics at Fullstack Academy

Artificial intelligence is reshaping many fields and data analytics is no exception. Data analysts are leveraging the power of strong AI tools to uncover valuable insights, drive business decisions, unlock new career opportunities, and more. We spoke with Vivek Sharath, an Instructor at Fullstack Academy, about how AI is used in data analytics today. If you're trying to decide between Fullstack Academy's Data Analytics Bootcamp and AI & Machine Learning Bootcamp, which both teach AI tools, Vivek also shares how to choose the right program to fit your career goals.

Meet Our Expert: Vivek Sharath

• Vivek is an AI and Machine Learning Instructor at Fullstack Academy, helping students learn at a deeper level with workshops that reinforce what was learned in the live online classes.
• Vivek supports students with immediate assistance with their questions, plus engages in mock interviews throughout the course to ensure knowledge retention and highlight areas of strengths and needed improvement.

How AI Is Used in Data Analytics

We often associate AI and machine learning with data scientists, but is AI also used by data analysts?

Yes! AI is used in a wide variety of industries and fields these days and that includes data analytics. For any role, AI can take on different shapes and forms. AI has been the backbone for data scientists for a while, but is now easing the daily lives of data analysts with new AI tools.

Using AI can offer speed and velocity to projects. Before AI, there were a lot of manual tasks that needed to be done, such as reviewing data, pre-processing data, and interpreting results. Using AI can reduce some mundane work, allowing data analysts to focus on the task at hand, which is problem-solving and effectively reporting what they discover.

The Pros & Cons of Using AI in Data Analytics

3 AI Tools for Data Analytics

1. Python: While Python is not AI-specific, it is very important for data analysts to interact with data for wrangling and pre-processing. If you are a data analyst who wants to build statistical models or run statistical tests, Python also has a great source of open libraries that you can use to perform them!
2. Tableau: It may not be an AI-native tool, but Tableau is a very common data visualization tool that people have used for several years and is often listed in job descriptions for data analyst roles. Now with Tableau’s new AI functions, you can easily create visualizations using their UI and platform, and benefit from its AI insights.
3. Structured Query Language (SQL): While SQL isn't AI, databases with integrated AI tools can assist in query design. SQL queries can be tricky and it's always important to run it efficiently. As a data analyst, you know what data you want and you can use some of the built-in AI tools to help you create this query so that you're not wasting time with the fine details of the query itself and you're executing it at an efficient rate.

Are these AI tools only being used by seasoned data professionals or are entry-level roles also using them?

I've seen more entry-level roles use the AI tools I’ve mentioned. While AI tools are employed by workers at all career stages, there's a noticeable trend of greater usage among entry and mid-level positions. I think people who have recently graduated or switched to this career are more comfortable with learning and using new AI tools. In any case, whether you're a senior or a junior, it's important to know these tools because they are meant to boost productivity and make you a better data analyst.

Are employers expecting their new data analytics hires to know AI tools?

Knowing AI tools can give you a competitive edge in the job market these days. Having that understanding of how to problem solve and understand the data is 80 percent of what's required in today’s data analytics roles. Understanding AI tools is what's going to set you apart from other candidates because you will be demonstrating to the employer your foundational knowledge of these productivity tools.

Are there certain industries that are relying more heavily on AI data analytics tools?

We're at the stage where most, if not all, industries are adopting more AI! Three industries that heavily utilize data analytics are finance, healthcare, and cybersecurity. Finance has always relied on data analytics. Healthcare was an early adopter of AI, using bioinformatics and image processing to give the patient the best experience possible using numbers, machine learning, and data analytics. In cybersecurity, AI has been a major player in stopping cyber threats and malicious attacks, which involve data analytics and machine learning.

For example, Netflix’s movie recommendation system relies heavily on analytics to determine which films are best to suggest to users. The same goes for Facebook and Amazon. Facebook recommends certain posts that they believe are going to be the ones that you will interact with, based on data analytics under the hood. Amazon in the e-commerce world shows users products that they will most likely purchase based on what they've searched for in the past.

How to Learn AI and Data Analytics at Fullstack Academy

Will students learn AI tools in Fullstack Academy’s Data Analytics Bootcamp?

In the data analytics bootcamp, students start with a foundation in understanding the math and problem-solving needed for data analysis. They learn Python as well as tools like Tableau and Power BI that have built-in AI tools. After we get the foundation down, this course introduces students to working with AI tools. Beyond learning any one tool, it’s important to understand how these tools work and learn how to use them, so you can keep evolving in your career.

Do you think that understanding AI will boost a Data Analytics student’s hireability? Will understanding AI also make Data Analytics students eligible for more diverse tech roles?

The technical foundation that students build at Fullstack Academy can help people pivot into multiple careers, including data analyst roles. If you already have a strong software background, you could focus on analytics software engineering and machine learning engineer roles, or you could pivot into more of a research role. It depends on your background and your career goals. The foundations taught here are common statistical tests that are heavily used in the research world.

Fullstack Academy also offers an AI & Machine Learning Bootcamp. How is that bootcamp different from the Data Analytics Bootcamp?

Both bootcamps spend time building a strong foundation that includes: 

• understanding how to problem-solve
• spending time with Python
• spending time with statistical tests

The data analytics program is geared for an analyst to research and understand data by working with visualization tools, understanding what kind of information you can get from the data, and how to properly present the data. The Data Analytics Bootcamp is also beginner-friendly. Whereas the AI & Machine Learning Bootcamp is focused deeply on intelligent model building and recommends students to possess coding skills and 3-5 years of professional experience in a highly computational field.

What is your advice to people trying to decide between the Data Analytics Bootcamp and the AI & Machine Learning Bootcamp?

It comes down to what you want to do!

• If you want to build models from the ground up using available libraries, the AI & Machine Learning Bootcamp is the way to go. 
• If you see yourself in more of an analyst role (such as a business analyst or research analyst) where you need to be skilled with numbers, but aren’t necessarily interested in building machine learning or deep learning models, then the Data Analytics Bootcamp is a better fit.

Both bootcamps require some level of programming. It's not expected of incoming students, but having a good programming background can help expedite some of those initial growing pains at the beginning of our data bootcamps. 

Find out more and read Fullstack Academy reviews on Course Report. This article was produced by the Course Report team in partnership with Fullstack Academy.