Back in 2022, the landscape looked completely different.
Younger folks today have no idea what that era was like.
I’d pour countless hours into:
- Manually crafting Python and SQL code, line after line
- Committing to memory which libraries to bring in and the specific functions within them (like
from sklearn.metrics import r2_score) - Tracking down and fixing code bugs
- Creating documentation to explain my code
- Assembling dashboards to examine massive datasets
Even within just the past 12 months, as AI-powered tools have grown more and more sophisticated, my role as a data scientist has shifted. I’ve moved away from being purely a code generator and evolved more into a strategic thinker — someone with deep knowledge of my organization’s data, who knows how best to showcase it and pull meaningful conclusions from it.
Claude is accelerating the transformation
I genuinely believe Claude is one of those tools that will reshape this industry and this profession at a pace nobody anticipated. I’ll be honest — it’s a bit intimidating. At the same time, data scientists who take the initiative to learn and control this technology will maintain their competitive advantage.
Here are 3 ESSENTIAL skills every data scientist needs to be sharpening right now:
1. Claude Dashboards
There was a time when I’d devote an entire day constructing a Tableau dashboard for a client, just to answer a handful of questions about a large dataset that might not get revisited for months.
Today, Claude can deliver a fully functional, interactive dashboard in mere minutes, packed with:
- KPI metric cards
- Line charts
- Bar charts
- Drill-down buttons
- Tabs
- … and plenty more
Let’s walk through a straightforward example using the AEP hourly energy dataset (CC0 license).
Claude Prompt:
I have a time series dataset of hourly energy consumption (AEP_MW) with a datetime column. Build me an interactive HTML dashboard that includes:
1. Four KPI cards showing average load, peak load, minimum load,
and summer vs winter comparison
2. A line chart showing average load by hour of day split by weekday vs weekend
3. A bar chart of average monthly load with higher months highlighted in a warmer color
4. A bar chart of average load by day of week with weekends in a different color. Use a clean, minimal style.
Here’s what it produced:
Several insights jump out right away from the dashboard — things you simply couldn’t uncover from staring at a raw CSV:
- Weekday energy use surges sharply around 5–6 PM, while weekends peak earlier (around 2 PM) and at a noticeably lower level overall
- July and August consumption far outpaces spring months, confirming a strong summer seasonality driven by air conditioning demand
- Saturday and Sunday loads run consistently about 10% below weekday levels
These kinds of dashboards work great for exploratory data analysis as well as for generating one-off reports for stakeholders who just need a snapshot of the current situation. You can even set up automated dashboard generation on a recurring schedule to get fresh reports every week.
2. Claude Cowork for Prioritizing Jira Tickets & Tasks
Here’s what a typical Monday morning looked like for me in the past: open Jira, sift through 20 open tickets, try to recall the context behind each one, figure out what’s blocking what, and cobble together a rough priority list for the week.
Claude Cowork differs from Claude Chat in that it actually integrates with your desktop and can read and write files. It connects to Jira (or another Scrum/Agile platform) and can summarize your weekly priorities. Here’s an example:
Pull all my open tickets from the current sprint. For each one, give me: the ticket ID, a one-sentence summary of what needs to happen, the current status, and any blockers. Rank them by priority and tell me what I should tackle first today.
Here are a few other prompts you can use with Cowork:
Writing tickets to Jira
Here are my notes from today’s model review meeting: [paste notes – or link to the notes if your Cowork is connected to Google Drive]. Create Jira tickets for each action item in the DS project.
For each one, write a clear title, a 2-sentence description of what
needs to happen and why, set the priority based on urgency,
and assign them to the current sprint.
Preparing for a stakeholder meeting
Read the last 3 weeks of comments on tickets tagged ‘model-deployment’ and write me a 5-bullet status summary I can share with the engineering team lead. Keep it non-technical.
Drafting documentation from scratch
Open the file preprocessing_pipeline.py in my project folder and write a README section explaining whatIt seems like you’ve provided the end of an article about using AI tools like Claude Code for data science workflows, but the beginning is missing. Could you please share the full HTML content you’d like me to paraphrase? I’ll be happy to rewrite the text to be easier to read and understand while keeping the HTML structure intact.
