Best AI tools for data scientists

Data science is a strange mix of detective work and accountability.

You’re expected to explore quickly (“what’s going on in this data?”) and also be right (“are you sure this number is correct?”). That tension is exactly where AI tools can help, as long as you keep validation, reproducibility, and testing as non-negotiables.

Used well, AI becomes a fast assistant for:

• the first 30–90 minutes of EDA (profiling, starter visuals, hypotheses)
• drafting SQL and explaining queries (plus guardrail sanity checks)
• turning notebook chaos into stakeholder-ready narratives
• generating checklists you’ll actually run (data quality, drift, edge cases)

At a glance

• Best for: fast EDA, SQL drafting/explanation, analysis write-ups, sanity checks
• Great first stack: a notebook environment + ChatGPT/Claude + a reproducible modeling/testing workflow (e.g., dbt for warehouse work)
• Use AI for: drafts and hypotheses
• Do yourself: metric definitions, validation queries, and anything that requires truth

What the model is good at in a DS workflow

Strong use cases

• “What should I check first?” EDA checklists and starter charts
• translating business questions into metric specs (grain, filters, windows)
• drafting and refactoring SQL (especially with explicit assumptions)
• summarizing results into a one-page memo with caveats

Weak / risky use cases

• producing “insights” from raw data without you verifying the computation
• writing one giant query that nobody can review
• any workflow where you can’t reproduce the result later

Tool picks (with rationale)

1) Julius AI: fast profiling and chart drafts

Great for quickly answering: “what’s in this file?” and “what looks weird?”

Why this pick: it compresses the orientation phase. You still validate the story.

2) Rows (AI spreadsheet): lightweight analysis when the org lives in sheets

Helpful when stakeholders are spreadsheet-native and you want quick pipelines and explanations.

Why this pick: lowers friction when notebooks are overkill.

3) Deepnote (or similar collaborative notebooks): shareable, rerunnable work

Collaboration is part of reliability.

Why this pick: easier review, reruns, and handoffs than “here’s my local notebook.”

4) ChatGPT: SQL drafts, cleaning plans, and translation to plain language

Strong generalist for day-to-day DS work.

Why this pick: fast iteration on ideas and wording.

5) Claude: long-context synthesis and writing

Useful when you need to digest long investigation logs, multiple result tables, or messy notebooks.

Why this pick: helps you produce a coherent narrative with fewer omissions.

6) dbt + an AI helper: modeling discipline and tests

Not glamorous, but extremely high leverage when you’re working in a warehouse environment.

Why this pick: reproducibility and tests prevent “we shipped the wrong number.” AI can help write repetitive docs/tests; you define the rules.

Step-by-step workflow (fast, then correct)

Step 1: Define the question as a metric spec

Before you touch SQL, write:

• definition (what counts?)
• grain (per user? per day? per account?)
• filters (include/exclude)
• window (time zone, cutoffs)
• assumptions (unknowns to validate)

Prompt:

“Restate this business question as a metric spec: definition, grain, filters, window, and assumptions.”

Step 2: Do a data quality profile before “insights”

Ask for:

• missingness patterns (overall and by segment)
• duplicates / key integrity
• invalid ranges and suspicious categories
• type issues (dates as strings, numeric fields as text)

Step 3: Generate 3–5 starter visuals (as hypotheses)

Good first charts are usually:

• an outcome distribution
• a segment comparison for a primary dimension
• a time trend
• one relationship/correlation candidate (careful)

Then immediately ask:

“What could make these charts misleading? List data issues, bias, and definition traps.”

Step 4: Draft SQL with guardrails

When using AI to draft SQL, ask for two outputs:

1. the main query
2. a sanity-check query

Prompt:

“Generate SQL for this metric in (dialect). Also output a ‘sanity check’ query that validates row counts and join assumptions.”

Guardrails that save you:

• expected row counts at each step
• duplicate key checks
• null-rate checks
• baseline comparison to a known metric

Step 5: Break big work into reviewable chunks

Prefer:

• CTEs with clear names
• intermediate validations
• multiple smaller queries over one monster query

Step 6: Turn results into a stakeholder memo

A structure that travels well:

• What we looked at
• What we found (validated)
• Confidence and limitations
• What we recommend next

AI can draft the prose; you make sure it’s honest.

Concrete examples

Example: “fast EDA memo” bullets

• Dataset: 1.2M rows of events (last 90 days). Potential key: (user_id, event_time, event_type).
• Quality: ~8% missing device_type; missingness is higher in Region B.
• Validated findings: conversion is down ~3–5% in cohorts exposed to X, starting the week of Y.
• Caveats: attribution logic changed on date Z; results depend on that definition.

Example: SQL sanity checks to request

• “How many rows do we expect after each join?”
• “Which join keys could duplicate records?”
• “What’s the null rate for the key columns?”

Mistakes to avoid

• Copying queries you don’t understand. If you can’t explain the joins, you can’t trust the output.
• Treating AI charts as truth. EDA tools can mislead when types/filters are wrong.
• Skipping reproducibility. Save queries, assumptions, and versions.
• Overselling confidence. Make uncertainty visible (bias, missing data, proxy variables).

FAQ

Will AI replace the hard parts of data science?

No. The hard parts are framing the problem, understanding the data-generating process, and defending conclusions. AI speeds up the mechanical parts.

What’s the safest way to use AI with sensitive data?

Follow org policy. Often the safest approach is to share schemas, aggregated summaries, and de-identified samples rather than raw rows.

What’s the simplest setup that works?

A notebook environment + one general assistant (ChatGPT/Claude) + a testing/documentation workflow (dbt in warehouses). Add EDA helpers as needed.

Try these walkthroughs

• EDA
• SQL analysis

Closing thought

AI can make you faster. Your job is to keep the work reliable. Use AI to get to a good draft quickly, then lock it down with validation, tests, and clear communication.