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Working with data means turning raw records into understanding. In practice, that breaks into five repeatable activities: collecting data, cleaning it, exploring it, visualizing it, and interpreting what the results do and do not support. Data visualization and statistics are two halves of the same job — statistics tells you whether a pattern is real and how uncertain it is, while visualization shows you the shape of the pattern and communicates it to others. You do not need a math or programming background to start; you need a question, a small dataset, and a tool simple enough that you spend your time thinking about the data rather than the software.

The Five Core Activities of Data Work

Most data projects, from a personal budget spreadsheet to a public health dashboard, move through the same stages.

1. Collecting

You gather observations: survey responses, website logs, sensor readings, government tables, or a hand-built spreadsheet. The key decision here is what counts as one row (a person? a day? a transaction?) and what each column measures. Getting this "unit of observation" wrong causes problems that no amount of later analysis can fix.

2. Cleaning

Real data arrives messy. Cleaning means handling missing values, fixing inconsistent categories ("USA," "U.S.," "United States"), correcting types (a date stored as text), and removing duplicates. Beginners are often surprised that this is the most time-consuming step. It usually is.

3. Exploring

Before making charts for others, you look for yourself. What is the range of each variable? Are there outliers? How are two variables related? Simple summaries — counts, averages, minimums, maximums — and quick scatterplots answer most early questions.

4. Visualizing

You encode values as position, length, color, or size so that patterns become visible. A good chart answers one question clearly. A bad chart hides the answer behind decoration or distorts it through a misleading axis.

5. Interpreting

You decide what the pattern means, how confident you should be, and what alternative explanations exist. This is where statistics and careful reasoning matter most.

Visualization vs. Statistics: How They Complement Each Other

These are not competing approaches. They answer different questions about the same data.

A practical rule: visualize to discover, model to confirm, visualize again to communicate. A scatterplot might reveal that one region behaves completely differently from the rest; a statistical model then tests whether that difference holds up; a final chart shows the finding to an audience.

Beginner-Friendly Tools and Formats

You can start with tools you already have.

• Spreadsheets (Excel, Google Sheets): Best for datasets under a few thousand rows. Built-in chart types cover bar, line, scatter, and pie. Learn to sort, filter, and use pivot tables.
• Chart types to master first: bar charts for comparisons, line charts for change over time, scatterplots for relationships, and histograms for distributions. These four cover most everyday questions.
• Simple code options: If you want to go further, R (with ggplot2) and Python (with matplotlib or plotly) are common. Both have large free learning communities. Start with one, not both.
• Design principles that matter more than the tool: label your axes, start bar charts at zero, avoid 3D effects, use color to encode meaning rather than decoration, and put the most important comparison in the most prominent position.

A Realistic Starting Path

If you have no data background, this sequence works:

1. Pick a question you actually care about. "How has my city's rent changed over ten years?" beats a generic tutorial dataset.
2. Find a small, public dataset. Government open-data portals and statistical agencies publish free tables.
3. Load it into a spreadsheet and clean it. Fix types, remove duplicates, note missing values.
4. Make three charts. One bar, one line, one scatter. Write one sentence under each describing what you see.
5. Ask what could be misleading. Is the sample representative? Is the time range fair? Could a third factor explain the pattern?
6. Repeat with a slightly harder question. Add a second variable, or try a simple statistical summary like a correlation or a group comparison.

Expect the first project to take longer than you think, mostly in cleaning. That is normal, not a sign you are doing it wrong.

What Data Can and Cannot Answer

Data can describe what happened, compare groups, estimate relationships, and quantify uncertainty. It cannot, on its own, establish causation without a proper study design, tell you what you should value, or compensate for a biased sample. A dataset collected from volunteers will not represent the general population no matter how sophisticated the analysis. Treat every result as "what this data suggests under these conditions," not as a final verdict.

Where to Go Next

FlowingData (flowingdata.com) focuses on data visualization and statistics for people who want practical, well-designed charts rather than academic theory. It is a reasonable place to browse examples, see how real datasets are turned into clear graphics, and pick up habits you can apply in your own work. Pair it with one spreadsheet tutorial and one public dataset, and you have everything you need for a first project.

The short version: working with data is a craft of asking clear questions, cleaning messy inputs, looking before you model, and communicating honestly. Start small, start visual, and let the statistics grow as your questions get harder.

If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

Deepnote is a hosted, collaborative data workspace; Jupyter is an open-source notebook environment you typically run and host yourself; Google Colab is a free-to-start hosted notebook service tied closely to Google's ecosystem and compute. The practical split: choose Deepnote when collaboration, sharing, and turning notebooks into data apps matter most; choose Jupyter when you need full control over environment and infrastructure; choose Colab when you want quick, low-friction access to GPUs and don't need team-grade workspace features. Deepnote's own site lists comparison pages for Jupyter and Google Colab, so the vendor positions itself directly against both.

Core differences at a glance

The rows above reflect what Deepnote's site states about itself; Jupyter and Colab characteristics are general, well-known properties of those tools rather than claims from the Deepnote page.

Where Deepnote differs most

Deepnote's page emphasizes three things that distinguish it from a plain notebook:

• Collaboration as the default. The site frames Deepnote as a place to "work together with your team," with code reviews, history & versioning, and security & auditing listed as platform features. In Jupyter this is possible but usually requires extra infrastructure; in Colab, sharing is file-based.
• From exploration to product. Deepnote lists "data apps & dashboards," alerts to monitor metrics, and a semantic layer (Modules, LookML, dbt). That's a step beyond a notebook — it's aimed at publishing insights, not just running cells.
• Agents and AI. The current homepage headline is "The open data workspace built for humans agents," and it shows AI generating dashboards, answering questions, and building "autonomous data agents." This is a positioning choice; treat it as Deepnote's stated direction rather than a benchmark against Jupyter or Colab.

When each one fits

Choose Deepnote if: you work in a team, need shared notebooks with versioning and reviews, want to publish dashboards or data apps, and prefer a managed workspace over running your own servers. The site cites 600,000+ data pros and lists use cases across fintech, biotech, gaming, enterprise, startups, and research.

Choose Jupyter if: you need full control over the environment, want to avoid vendor lock-in, work with non-Python kernels, or already run your own infrastructure. It's the open standard many tools build on.

Choose Google Colab if: you want to start fast with free access to GPUs, your work is individual or lightweight, and you're comfortable inside Google's ecosystem. It's less oriented toward team workspaces and app deployment.

A concrete way to decide

If your task is "explore a dataset, then share a live dashboard with the revenue team," Deepnote's page shows exactly that flow — a PLG dashboard published and shared. If your task is "run a one-off training job on a GPU without setup," Colab is the lower-friction path. If your task is "build a reproducible pipeline on infrastructure I control," Jupyter (or a Jupyter-based platform) is the fit.

For a direct feature-by-feature view, Deepnote hosts comparison pages against Jupyter, Google Colab, Databricks, AWS SageMaker, Looker, Vertex AI, and others under its "Compare Deepnote..." section. Pricing details are on its pricing page; check current terms there rather than assuming a free tier.

Deepnote is a collaborative data science notebook platform for exploring data with Python and SQL, working with a team, and sharing insights. It is positioned as "the open data workspace built for humans agents," and its own site describes it as bringing "all your data work in one workspace." If you need a shared environment where analysts, data scientists, and increasingly AI agents work on the same notebooks and data apps, Deepnote is built for that use case. If you only need a single-user notebook with no collaboration or app-sharing requirements, a simpler local setup may be enough.

What Deepnote does

The platform covers the full arc from quick exploration to production-style outputs:

• Data analytics — Python and SQL for advanced analytics, plus an AI feature ("Deepnote AI") aimed at improving productivity.
• Data apps & dashboards — turn notebook work into shareable apps and dashboards rather than static reports.
• Alerts — monitor metrics and get notified when something changes.
• Semantic layer — support for Modules, LookML, and dbt, so metrics can be defined once and reused.
• Data engineering — ETL/ELT pipelines, data transformation, and a data catalog for documenting data assets.
• High-performance computing — Spark and Snowpark support, plus GPUs for machine learning workloads.
• Machine learning — model training, serving from a notebook, and model monitoring.
• Governance — code reviews, history and versioning, and security/auditing including SOC2 and HIPAA.

How teams actually use it

The site's example workflow shows the intended pattern: a dashboard or data app is generated and published, then shared with a team (in the demo, the revenue team). A SQL cell queries data, and Deepnote AI surfaces an insight — for example, flagging that treatment users skipped onboarding more often and suggesting a segment-by-first-session-length follow-up before shipping. The same environment supports autonomous agents, such as a fraud-detection agent that summarizes and cancels fraudulent transactions by country and generates SQL blocks.

That combination — human analysis, AI-generated insight, and agent-run tasks in one workspace — is the core of the "humans agents" positioning.

Integrations and scale

Deepnote advertises hundreds of integrations so you can work with any data source. It reports that 600,000+ data professionals use it, and it lists customers across fintech and finance, biotechnology, gaming, enterprise, startups, and research, plus a stated use case for education.

Where it fits compared to alternatives

Deepnote's own site offers direct comparisons against Jupyter, Google Colab, Hyperquery, Databricks, AWS SageMaker, Looker, and Vertex AI. The meaningful differences to check when choosing:

If your priority is a free, single-user notebook for learning, Colab or local Jupyter may cover it. If your priority is a governed, collaborative workspace that also hosts data apps and agents, Deepnote is aimed squarely at that.

Getting started

Deepnote offers a free "Get started" entry point, a separate Pricing page, and an Enterprise tier with a "Book a demo" option. Documentation, guides, webinars, a blog, a changelog, and a public roadmap are all available from the site. Pricing details and any login requirements are not specified in the available material, so check the Pricing page directly before assuming a plan or access level.

Deepnote connects to hundreds of data sources and ships with enterprise-grade security controls, including SOC2 and HIPAA compliance, security auditing, and version history. That combination makes it suitable for teams that need to pull data from many systems and still satisfy internal or regulatory review. The sections below cover what each capability actually does and where it fits.

Integrations: hundreds of data sources

Deepnote describes support for "100s of integrations," so you can work with any data source rather than being locked into one warehouse or file format. In practice this means a notebook can query a database, pull in a file, and combine results in the same workspace.

The platform's stated capabilities that depend on integrations include:

• Python & SQL analytics — run SQL against connected sources and continue analysis in Python.
• ETL/ELT pipelines — move and transform data as part of a data engineering workflow.
• Data transformation and a data catalog — document data assets alongside the work that uses them.
• Semantic layer support — works with Modules, LookML, and dbt.
• Spark and Snowpark — high-performance computing on large datasets.
• Machine learning workflows — model training, model serving from a notebook, and model monitoring, with GPUs available for heavy compute.

Because the source lists integration breadth but not a specific connector list, check the Deepnote docs for the exact source you need before committing. The relevant question is whether your primary database, warehouse, or file store is covered — if it is, the rest of the workflow stays in one place.

Security and compliance features

Deepnote's security page highlights SOC2 and HIPAA among its compliance credentials, and the platform lists security & auditing as a core capability. For teams handling sensitive data, the practical features are:

Two of these are easy to overlook but matter day to day. History & versioning means an accidental overwrite or a bad edit isn't permanent — you can return to a prior state. Code reviews bring the same review discipline used in software engineering to notebook code, which reduces the chance that an error reaches a shared dashboard or report.

Who this fits

The integration breadth and compliance set point to a few clear situations:

• Enterprise and regulated teams that need SOC2 or HIPAA coverage before adopting a tool.
• Teams with data spread across many systems who want one workspace instead of stitching tools together.
• Data engineering and ML groups running pipelines, Spark/Snowpark jobs, or model training and monitoring.
• Organizations that need an audit trail for who changed what and when.

If your work is a single small dataset in one format, the integration and compliance depth is more than you need. If you're coordinating across sources and stakeholders, it's the part that matters most.

How to verify before you commit

1. Open the Deepnote docs and confirm your specific data source appears in the integration list.
2. Check the security page for the compliance standard your organization requires (SOC2, HIPAA, or another).
3. Confirm that auditing, version history, and code review cover your team's review requirements.
4. For pricing and plan-level feature differences, see the pricing page — availability of specific features can vary by plan.

The main caveat: the source confirms these categories exist but doesn't detail every connector or every control. Treat the docs and security page as the authoritative check for your exact stack and compliance needs.