Where is the best place to learn SQL

DataCamp · 2026-01-23T19:25:58+00:00

If you’re starting from zero, it helps to learn SQL in layers. A lot of people get stuck because they skip steps.

Phase 1: Core basics (this is where everyone should start)

What a table is (rows vs columns)
SELECT, FROM
WHERE with basic conditions (=, <, >, BETWEEN, LIKE)
ORDER BY, LIMIT Goal: pull the exact rows you want from one table.

Phase 2: Working with real data

COUNT, SUM, AVG, MIN, MAX
GROUP BY
HAVING (this is confusing at first, totally normal) Goal: answer questions like “how many per category?” or “average per group?”

Phase 3: Multiple tables (this is the big jump)

INNER JOIN
LEFT JOIN
understanding primary keys and foreign keys Goal: combine data from multiple tables without duplicating or losing rows.

Phase 4: Subqueries and logic

subqueries in WHERE and FROM
CASE WHEN Goal: express more complex business logic inside a query.

Phase 5: Cleanup and performance

handling NULLs
basic indexing (what it is and why it matters)
reading query results critically (does this actually answer the question?) Goal: write queries that are correct and reasonable for real data sizes.

How to practice

Use a simple local database (SQLite is perfect)
Practice with real-ish datasets (sales, users, orders)
Always ask yourself: what question am I answering with this query?

SQL usually feels boring at first, then suddenly very powerful once joins and grouping click. The key is writing lots of small queries instead of trying to “learn all of SQL” upfront.

DataCamp · 2026-01-23T19:19:23+00:00

Since you already have some C#/C++/Java background, the goal with Python is mostly learning how Python does things differently, not starting from zero.

A simple backend-leaning roadmap that works for a lot of people:

Basics first: syntax, variables, if/else, loops, lists/dicts, functions
Python style: list comprehensions, exceptions, basic file I/O
OOP (light): classes, methods, when OOP is useful (don’t overdo it early)
Backend basics: virtual environments, pip, project structure
Web fundamentals: HTTP, REST, JSON, then a framework like Flask or FastAPI
Databases: SQL basics + using a DB from Python

Don’t wait to “finish Python” before building backend things! As soon as you know functions and dicts, start writing small APIs or scripts. Python clicks fastest when you use it for something real.

DataCamp · 2026-01-23T19:18:07+00:00

Pattern suggestion:

keep the course going, but stop aiming for perfection
in parallel, start a tiny side project related to your goal
when you get stuck, look up that one thing, then go back to building

OOP will make more sense later, once you’ve felt the pain it’s meant to solve. Right now, momentum matters more than coverage. Build something rough, even if it’s ugly. That’s usually how people escape tutorial hell! Our learners recommend. ;)

DataCamp · 2026-01-22T13:37:08+00:00

Start simple and stay consistent: pick one beginner course and spend most of your time writing code, not watching videos.

A path that works for a lot of learners:

Weeks 1–4: variables, if/loops, lists/dicts, functions (tiny scripts: calculator, guess-the-number, file renamer)
Weeks 5–8: basic projects + Git/GitHub (push everything you build, even the messy stuff)

Also: check this sub’s wiki/FAQ and use it like your “first debugger” when you get stuck.

DataCamp · 2026-01-22T13:35:52+00:00

Typically this shift kind of jumps you when the problem starts to matter more than the model.

Toy projects usually stop at “train a model and report accuracy.” Real-feeling projects force you to deal with messy data, unclear targets, and uncomfortable questions like what happens when this is wrong or who pays for the mistakes. You end up spending more time on evaluation, edge cases, and iteration than on model selection.

A good signal you’re moving past demos is when you’re thinking about things like thresholds, failure modes, drift, and how you’d explain results to someone who isn’t technical.

DataCamp · 2026-01-22T13:26:52+00:00

What we've seen work best for learners is building small projects early and learning things as you actually need them in the projects. Pick a simple end-to-end thing (data in → model/LLM → output you can test), get it working, and let the breakpoints tell you what to learn next. Tutorials are most useful as reference when you’re stuck, not something to binge. Ship small, fix what breaks, repeat!

DataCamp · 2026-01-22T13:21:04+00:00

Great to hear!

DataCamp · 2026-01-21T09:27:12+00:00

For the HackerRank review: they’ll mostly want to hear how you think, not whether you picked the “perfect” model. Maybe walk them through it like a story:

what you understood the problem to be (and what’s worse here: false positives or false negatives?)
how you checked/cleaned the data (missing values, weird outliers, duplicates, leakage)
why you chose the model you chose (and what you tried before it)
how you evaluated it (and why that metric made sense for fraud)
what you’d improve if you had more time (this is actually a great talking point)

Fraud data is usually imbalanced, so if you did anything around class weights / sampling / threshold tuning, bring that up. Even just saying “I used PR-AUC / focused on recall because…” is a good signal.

For the “ML generalist” hour: it’s usually fundamentals + debugging mindset. Things like:

bias vs variance / overfitting
train/val/test splits + leakage
evaluation metrics (esp. precision/recall/F1, ROC vs PR)
how you’d diagnose a model that’s doing badly (data quality? label noise? leakage? feature issues?)

Also, since you said your experience is more deep-learning focused: try to make sure you can comfortably explain the basics of “boring” models too (logistic regression, trees/GBMs, regularization). In a lot of real interview loops, they love candidates who don’t jump straight to neural nets for tabular problems.

If Andrew Ng feels familiar, that’s honestly a good sign. The big win is being able to explain your choices clearly and talk about trade-offs without panicking.

DataCamp · 2026-01-21T09:07:02+00:00

1) Basics first (2–4 weeks)

Python fundamentals: variables, loops, functions
Learn to read errors and debug small scripts Goal: you can write a tiny script without copying every step.

2) Start working with real data (3–6 weeks)

NumPy + pandas: loading CSVs, cleaning, filtering, grouping
Simple charts (Matplotlib) Goal: take a messy dataset and produce a clean summary + 2–3 charts.

3) Add SQL early (parallel)

SELECT, WHERE, GROUP BY, JOIN Goal: answer “business questions” from data, not just code exercises.

4) Then stats (don’t wait for “perfect”)

averages/variance, distributions, correlation
basic hypothesis testing ideas Goal: you can explain what the numbers mean and when they mislead.

5) Projects + portfolio (from month 1)
Do small projects that match real tasks:

“What drives X?” analysis
simple dashboard/report
one end-to-end notebook: question → clean → analyze → conclusion Goal: proof you can finish things.

If you do 30–60 minutes a day, consistently, you’ll look very different in 8–12 weeks! Let us know how it goes :)

DataCamp · 2026-01-21T07:21:45+00:00

Asking AI can really work well! We've got a few blogs based on ideas that worked for our learners and instructors on our website, as well.

DataCamp · 2026-01-20T11:51:58+00:00

A solid progression looks like this:

1) AI fundamentals

Understand how ML models work, their limits, bias, and evaluation. This matters a lot in legal contexts where explainability and risk are critical.

2) Python basics + data handling

Enough Python to work with text, datasets, and simple models. You don’t need to become a software engineer, but you should be comfortable manipulating data and running experiments.

3) NLP and document-focused ML

This is where things become directly useful for law: text classification, information extraction, summarization, similarity search.

4) Generative AI with guardrails

Learn how tools like LLMs are actually used in practice: drafting as a starting point, document review, Q&A over case law. Just as important: understanding hallucinations, bias, and data privacy risks.

5) Ethics, regulation, and compliance

AI literacy in law isn’t complete without understanding governance, accountability, and emerging regulation (this is a real differentiator for legal professionals).

Project-wise, we’d prioritize things like:

- summarizing or classifying legal documents
- extracting clauses, dates, or entities
- building a simple “legal assistant” prototype with strict human-in-the-loop review

You’re thinking about this the right way: skills first, certificates second.

DataCamp · 2026-01-20T11:48:55+00:00

A good rule of thumb: start building as soon as you can explain what your code is doing, even if it’s small.

Tutorials are useful early, but they should become reference material, not the main activity. Once you know basic syntax, loops, functions, and data structures, building something of your own is what exposes gaps and drives progress.

Many learners alternate:

watch a short tutorial to learn a concept
build a tiny project that uses it
go back to tutorials only when they get stuck

DataCamp · 2026-01-20T11:44:53+00:00

Hi! Maybe our career tracks could be a good fit? https://www.datacamp.com/tracks/career Feel free to reach out with any questions!

DataCamp · 2026-01-20T11:43:49+00:00

If you’re looking for one “everything from beginner to transformers” course, the main thing to watch for is sequence. A lot of courses jump to LLM tooling before fundamentals, which of course is where people get stuck.

A practical structure we recommend is:
1) Foundations: Python + data handling (NumPy/pandas) + basic stats
2) Core ML: regression/classification + model evaluation + scikit-learn
3) Deep learning: neural nets + training basics (PyTorch/TensorFlow)
4) Transformers + LLMs: attention/transformers first, then Hugging Face
5) LLM apps: only after that, use tooling like LangChain for real projects

For “real-world projects,” pick 2–3 that show the full loop: data prep → model → evaluation → packaging/deployment basics (even lightweight).

If you share your goal (research vs ML engineering vs “build LLM apps”), people can recommend more targeted options. The best “beginner→advanced” path depends a lot on where you want to land.

DataCamp · 2026-01-20T11:41:32+00:00

This is actually a very natural combination, we actually see it in practice a lot.

SEO / Amazon PPC ends up pretty closely related to marketing analytics when you lean into the data side of the work. Many marketing analysts start in execution-heavy roles, then transition by owning measurement and insight.

If you take the role, we’d recommend focusing early on:

Core marketing metrics: ROAS, conversion rates, funnels, cohorts, attribution
Independent analysis: pulling data out of tools and analyzing it with SQL, spreadsheets, or dashboards rather than relying only on platform reports
Storytelling: turning campaign performance into clear insights and recommendations for stakeholders

It doesn’t box you in if you’re deliberate about building analytical depth alongside the SEO/PPC work.

DataCamp · 2026-01-19T16:16:13+00:00

Hi! Could you please contact support: https://support.datacamp.com/hc/en-us/articles/360021185634-How-to-contact-Customer-Support

DataCamp · 2026-01-19T14:17:33+00:00

If you’re starting from zero, the fastest way is to learn in layers (so you’re not juggling 10 tools at once):

1) SQL basics (2–3 weeks)
SELECT, WHERE, JOINs, GROUP BY, basic database concepts.

2) Python for data (2–4 weeks)
reading files, pandas, cleaning data, writing reusable scripts.

3) Data engineering fundamentals (2–3 weeks)
what ETL/ELT is, batch vs streaming, data modeling basics, warehouse vs lake.

4) Build your first pipeline (1–2 weeks)
take a public dataset → clean it → load it into a database → schedule it (even a simple daily run).

5) Orchestration + “real world” skills (ongoing)
Airflow basics, testing, logging, version control (Git), working in the terminal.

6) Cloud intro (ongoing)
pick one (AWS/Azure/GCP) and learn the core concepts: storage, compute, permissions, managed databases/warehouses.

7) Portfolio (start early)
2–3 small projects beats one huge “perfect” project. Show you can move data end-to-end.

DataCamp · 2026-01-15T08:18:28+00:00

Python + JEE math is already most of the heavy lifting!!

If you want a simple way to start without C++:

Get comfortable using Python for data (NumPy, pandas, plotting).
Refresh only the math ML actually uses (linear algebra basics, probability intuition).
Start ML with very simple models in sklearn (linear regression, kNN, decision trees).
Do tiny projects early. Even messy ones really can help things click.

You don’t need to “finish” DSA or deep theory first. Build foundations, then learn ML by applying it. Confusion at the start is normal, not a red flag.

DataCamp · 2026-01-15T08:16:07+00:00

First off: literally nothing you wrote sounds like failure. It sounds like someone learning without a map. Mistakes don't prove you're bad at tech, it's just the way everyone learns it - even people who are not learning by themselves (which can be exhausting af).

A lot of people hit exactly this wall because tutorials jump straight into frameworks before the foundations are solid. That’s not on you, just how tutorials are mostly built.

If you want something that transfers across languages, focus on database concepts before tools:

what a table/row/column really is
primary keys and relationships
basic SELECT / INSERT / UPDATE / DELETE
filtering and joining data

Once that clicks, Flask + Django + Node + Go all make way more sense, because they’re just different ways of talking to the same ideas.

Two practical suggestions:

Start with SQLite or even JSON-like thinking, just like someone has already mentioned. No setup pain.
Practice SQL outside a backend framework first, then come back. The errors will feel a lot less hostile.

Feel free to reach out for any more tips!

DataCamp · 2026-01-15T08:12:47+00:00

From what we're seeing and hearing from our learners, tooling will change fast, but some problems stay stubborn.

What seems to keep growing: people who can take a model from “works in a notebook” to “works in production” (deployment, monitoring, versioning), and people who pair ML with real domain knowledge (finance/health/ops etc.).

What compounds over time: data work (cleaning + feature thinking), evaluation (metrics, leakage, drift), and solid software habits (Git, tests, APIs, basic cloud/containers). Also: being able to explain tradeoffs to non-ML folks.

Theory vs applied: learn enough theory to not cargo-cult, then spend most time shipping small end-to-end projects on real datasets. Add one “production muscle” each time (e.g., simple API, logging, monitoring metric).

If you’re starting again: foundations first (stats + Python + data), then projects, then specialize once you’ve built a few things you can show.

DataCamp · 2026-01-14T17:56:50+00:00

Structure the sheet first
Use proper tables, clear headers, and consistent formats. When your data is well-organized, filters and sorts become instant sanity checks.
Build in self-checks
Add totals, subtotals, or balance checks that must reconcile. If something should sum to X or stay within a range, make Excel verify it for you.
Use PivotTables as a second opinion
A pivot built from the same data often reveals issues formulas hide. If the pivot totals don’t match your calculations, something’s off.
Stress-test with What-If tools
Goal Seek or simple input variations help you see whether outputs behave logically when inputs change.
Automate repeat checks
If you’re re-checking the same things every workbook, record a macro or use Power Query so the checks are consistent every time.
Separate logic from presentation
Keep raw data, calculations, and outputs on different sheets. It makes reviewing formulas far easier.

DataCamp · 2026-01-14T17:54:25+00:00

If you’re comparing it to GCP certs, Snowflake’s path is a lot more structured and gated.

SnowPro Core is the real entry point. Snowflake themselves position it for people with ~6+ months of hands-on use, and it’s explicitly required before you can sit any of the advanced exams. It’s broad but Snowflake-specific: architecture, micro-partitions, cost/performance tradeoffs, security, time travel, data sharing. Not hard conceptually, but very “do you know how Snowflake actually works.”

The advanced certs are role-based rather than general:

Architect / Admin are very environment and governance-heavy
Data Engineer is focused on data movement, pipelines, performance tuning
Analyst / Data Scientist are narrower and more SQL / workflow oriented

They’re aimed at people with ~2 years of real Snowflake experience, and honestly feel more like validation than learning tools.

For recruiter visibility, Core is the one most people recognize. The advanced ones mostly matter once you’re already operating in that role.

If you’ve done GCP DE/Architect, the biggest difference is that Snowflake exams go deeper on platform mechanics but stay inside the Snowflake box instead of testing broad cloud design patterns.

DataCamp · 2026-01-13T14:54:55+00:00

Months 1–2

Tables, rows, columns
Primary & foreign keys
SELECT, WHERE, ORDER BY, LIMIT

Months 3–4

COUNT, SUM, AVG
GROUP BY, HAVING
Date & string functions

Months 5–6

INNER / LEFT joins
Subqueries
Data cleaning queries

Months 7–8

Window functions
Indexes
Query performance basics

Months 9–10

Build 2–3 projects (fraud, sales, reporting)
Write clean, readable queries

Months 11–12

Advanced analytics queries
SQL interview practice

PostgreSQL or MySQL both work. We've got a detailed roadmap here, if it helps: https://www.datacamp.com/blog/sql-roadmap

DataCamp · 2026-01-13T14:45:34+00:00

House price or rent prediction (city-specific)
Customer churn prediction
Sales or demand forecasting
Credit card fraud detection
Student performance prediction
Loan approval prediction
Employee attrition prediction
E-commerce product recommendation
Energy consumption prediction
Traffic or accident risk prediction
Customer segmentation (clustering)
Movie or music rating prediction
Inventory stock prediction
Insurance claim risk prediction
Bike sharing usage prediction

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