In case you haven’t heard the news, on Tuesday, September 17th, at 8:30 a.m. PT/11:30 a.m. ET/9:00 p.m. IST, Coursera CEO Jeff Maggioncalda delivered a live keynote address. Tune in to see the data that’s shaping Coursera’s approach to learning, view exclusive product demos from the Courserians building them, and hear from learners like you. View the live keynote here.
And now, let’s turn our attention to this week’s (very much related) topic: how data analysts use generative AI to streamline their workflows and maximize their impact. In previous issues, we’ve looked at practical AI skills and prompting techniques that could improve your productivity. Today, we’re building on those explorations by examining some concrete ways data analysts are leveraging this transformative technology to actually make their work easier.
First, though, it’s critical that we make a brief but important public service announcement about generative AI and data security. Here it goes:
📣PSA: Check Your Org’s Data Protection Policy!
Previously, we’ve talked about the ways GenAI is making cybercrime more common and more dangerous. For this reason, it’s more important than ever to remain vigilant and protect your personal data. Similarly, many companies implement data protection policies that aim to reduce the risk and severity of cyberattacks. These policies may limit the tools you’re allowed to use at work or the information you’re allowed to share with those tools. Before you engage with any AI tools at work, be sure to check your company’s data protection policy.
📈How data analysts are using GenAI
Think about generative AI not as a technology that’s meant to do a data analyst’s job, but instead as a tool that can support it. And, characteristically clever as always, that’s exactly what some data professionals are already doing. Rather than contorting this cutting-edge technology to do the kinds of cognitively demanding tasks that are best left to the experts (i.e. them), data analysts are instead using it to streamline their workflows and automate essential but time-consuming tasks.
Here are some of the impactful ways these data pros are using GenAI to cut down on tiresome tasks, heighten data security, and unlock greater productivity gains:
- Code generation and conversion: Data analysts use coding languages like SQL, Python, and R to clean and analyze data. Using AI, data professionals can now quickly generate common lines of code needed to do their work. Some analysts even use the technology to convert code from one language to another, resulting in less time spent translating between programming languages.
- Creating documentation: Data analysts create documentation to detail all the different components and processes used in a project, ensuring their findings are replicable. While this critical task can be labor intensive, with GenAI, teams can efficiently synthesize process notes into formal documentation.
- Produce synthetic data: Synthetic data mimics real-world data sets, but lacks genuine data points. Data analysts use synthetic data to refine machine learning models, preserve data privacy, and validate algorithms. Generative AI tools can create synthetic data that shares the mathematical properties of true data points without actually copying it, helping to ensure its confidentiality when shared with third parties.
- Build visualizations: Data analysts use visualizations to illustrate their findings to outside stakeholders in easily understandable, eye-catching ways. While many tools are already available to create simple, effective, and engaging visual aids, generative AI opens up many new ways for analysts to present their insights to decision-makers.
📚Learn more about AI and data analysis
We’re just at the beginning of the generative AI revolution. Build the skills you need to thrive in this fast-changing landscape with one of these AI and data analysis courses:
For an overview of generative AI and what it can (and can’t) do, explore DeepLearning.AI’s Generative AI for Everyone course.
To build data-specific generative AI skills, try IBM’s Generative AI for Data Analysts Specialization. You’ll learn practical tips to start incorporating GenAI tools into your data analysis process.
To learn transferable prompt engineering skills, consider Vanderbilt University’s Prompt Engineering Specialization. You’ll learn prompt engineering basics in addition to advanced data analysis skills.
Or, find even more data-specific GenAI programs here.
That’s all for this week. Let us know how you’re using generative AI for data analysis by dropping a comment below. See you next week!
P.S. Interested in learning how other career professionals are using AI? Check out our guides for product management, cybersecurity, and business management—and let us know what career you’d like us to cover next!