International Research Data Analysis Excellence Awards: How to Learn AI for Data Analytics in 2025 !

Data analytics has changed. It is no longer sufficient to know tools like Python, SQL, and Excel to be a data analyst.

Data skills for decision making.

As a data professional at a tech company, I am experiencing firsthand the integration of AI into every employee’s workflow. There is an ocean of AI tools that can now access and analyze your entire database and help you build data analytics projects, machine learning models, and web applications in minutes.

NVIDIA DGX Spark

If you are an aspiring data professional and aren’t using these AI tools, you are losing out. And soon, you will be surpassed by other data analysts; people who are using AI to optimize their workflows.

In this article, I will walk you through AI tools that will help you stay ahead of the competition and 10X your data analytics workflows.

With these tools, you can:Build and deploy creative portfolio projects to get hired as a data analyst
Use plain English to create end-to-end data analytics applications
Speed up your data workflows and become a more efficient data analyst

Additionally, this article will be a step-by-step guide on how to use AI tools to build data analytics applications. We will focus on two AI tools in particular - Cursor and Pandas AI.

For a video version of this article, watch this:

AI Tool 1: Cursor

Cursor is an AI code editor that has access to your entire codebase. You just have to type a prompt into Cursor’s chat interface, and it will access all the files in your directory and edit code for you.

If you are a beginner and can’t write a single line of code, you can even start with an empty code folder and ask Cursor to build something for you. The AI tool will then follow your instructions and create code files according to your requirements.

Here is a guide on how you can use Cursor to build an end-to-end data analytics project without writing a single line of code.

Step 1: Cursor Installation and Setup

Let’s see how we can use Cursor AI for data analytics.

To install Cursor, just go to www.cursor.com, download the version that is compatible with your OS, follow the installation instructions, and you will be set up in seconds.

Here’s what the Cursor interface looks like:

Cursor AI Interface

To follow along to this tutorial, download the train.csv file from the Sentiment Analysis Dataset on Kaggle.

Then create a folder named “Sentiment Analysis Project” and move the downloaded train.csv file into it.

Finally, create an empty file named app.py. Your project folder should now look like this:

Sentiment Analysis Project Folder

This will be our working directory.

Now, open this folder in Cursor by navigating to File -> Open Folder.

The right side of the screen has a chat interface where you can type prompts into Cursor. Notice that there are a few selections here. Let’s select “Agent” in the drop-down.

This tells Cursor to explore your codebase and act as an AI assistant that will refactor and debug your code.

Additionally, you can choose which language model you’d like to use with Cursor (GPT-4o, Gemini-2.5-Pro, etc). I suggest using Claude-4-Sonnet, a model that is well-known for its advanced coding capabilities.

Step 2: Prompting Cursor to Build an Application

Let’s now type this prompt into Cursor, asking it to build an end-to-end sentiment analysis model using the training dataset in our codebase:
Create a sentiment analysis web app that: 1. Uses a pre-trained DistilBERT model to analyze the sentiment of text (positive, negative, or neutral) 2. Has a simple web interface where users can enter text and see results 3. Shows the sentiment result with appropriate colors (green for positive, red for negative) 4. Runs immediately without needing any training Please connect all the files properly so that when I enter text and click analyze, it shows me the sentiment result right away.

After you enter this prompt into Cursor, it will automatically generate code files to build the sentiment analysis application.

Step 3: Accepting Changes and Running Commands

As Cursor creates new files and generates code, you need to click on “Accept” to confirm the changes made by the AI agent.

After Cursor writes out all the code, it might prompt you to run some commands on the terminal. Executing these commands will allow you to install the required dependencies and run the web application.

Just click on “Run,” which allows Cursor to run these commands for us: