90-Day Data Analytics Beginner Guide

A 90-day plan helps you start learning data analytics by giving a clear sequence of skills, tools, and projects to become job-ready.

Why this matters: structured learning prevents overwhelm and turns practice into tangible portfolio pieces you can show employers. This data analytics beginner guide emphasizes hands-on work, measurable milestones, and real datasets so you reliably build skills instead of collecting scattered tutorials.

What you'll learn: a month-by-month schedule covering statistics and Excel, SQL and Python for exploratory data analysis, then dashboards and deployment; plus project ideas, resources, and assessment checkpoints to track progress in 90 days.

90-Day Roadmap Overview for Data Analytics Beginners

90-day plan breakdown: goals for Month 1, Month 2, Month 3

Month 1: Foundations — learn descriptive statistics, Excel-based cleaning, and basic visualizations. Aim for 30–45 minutes daily plus two 2-hour weekend deep dives. Example milestone: clean and visualize a 10k-row CSV in Excel with pivot tables and charts.

Month 2: Databases & coding — master SQL SELECTs, JOINs, GROUP BY, and start Python (pandas, matplotlib). Time commitment: 1 hour weekdays, 3 hours on one weekend. Example milestone: build a repeatable SQL report and a 500-line Jupyter EDA notebook.

Month 3: Visualization & deployment — learn Tableau or Power BI, create dashboards, and publish a project. Milestone: a dashboard that answers a business question and a short case-study write-up.

Time commitment, daily schedule, and learning milestones

Daily schedule example: 30–60 minutes reading/concepts, 30–60 minutes hands-on practice. Weekly routine: two practice days focused on projects, one mock interview or write-up. Milestones include:

• End of Week 4: two cleaned datasets + 3 charts
• End of Week 8: SQL report + Python EDA notebook
• End of Week 12: public dashboard + portfolio case study

Key outcomes: beginner data analyst skills, portfolio-ready projects

Expected outcomes after 90 days: you can clean messy data, run SQL queries, perform EDA in Python, build dashboards, and document two case studies. Example skill metrics: 80% accuracy on basic SQL joins quiz, 3 reproducible notebooks on GitHub, one interactive dashboard hosted online.

Month-by-Month and Week-by-Week Plan (Data Analytics Beginner Guide)

Month 1: Foundations — statistics basics, Excel for data cleaning & visualization

Week 1: Statistics basics — mean, median, variance, distributions, and null handling. Practice: summarize three datasets and write a one-paragraph insight per dataset.

Week 2: Excel essentials — formulas, pivot tables, conditional formatting, and data validation. Practice: clean a CSV, remove duplicates, standardize dates, and create pivot summaries.

Week 3: Visualization principles — chart choice, color, and storytelling. Deliverable: 5-slide mini-report with trends, outliers, and recommendations.

Week 4: Mini project — combine statistics and Excel to produce an insights memo. Example: analyze 6 months of sales data, show seasonality, and recommend two actions.

Month 2: Databases & coding — SQL mastery and Python for EDA (pandas, matplotlib)

Week 5–6: SQL fundamentals — SELECT, WHERE, GROUP BY, HAVING, JOINs, and subqueries. Practice: daily 20-30 SQL problems on a sandbox like Mode or SQLZoo.

Week 7: Python basics — pandas DataFrame operations, merging, filtering, and plotting with matplotlib/seaborn. Deliverable: a reproducible EDA notebook.

Week 8: Integrate SQL + Python — pull data via SQL, perform joins in pandas, and automate a weekly report.

Month 3: Visualization, dashboards, and advanced workflows (Tableau, Power BI, deployment)

Week 9–10: Dashboard tools — build interactive views, filters, and KPI tiles in Tableau or Power BI. Practice: recreate a public dashboard and add a business question filter.

Week 11: Advanced workflows — scheduling, simple ETL with scripts, and version control for notebooks. Deliverable: a scripted pipeline that refreshes a CSV and updates visuals.

Week 12: Final project and presentation — assemble a case study: problem, data source, methodology, insights, and business recommendations; publish to GitHub and a dashboard host.

Core Technical Skills, Tools, and Concepts for Beginners

Statistics & data thinking: descriptive stats, probability, hypothesis testing

Ask: what story does the data tell? Learn to summarize distributions, compute confidence intervals, and run basic hypothesis tests (t-test, chi-square). Example: test whether email open rates differ between two campaigns using a t-test and report p-value and effect size.

Data cleaning & wrangling: Excel best practices, pandas, missing data strategies

Best practices: keep raw copies, use consistent date formats, and document assumptions. In pandas, use .dropna(), .fillna(), and forward/backward fill thoughtfully. Example strategy table below compares common missing-data approaches.

Databases, SQL, and basic data engineering: joins, window functions, query optimization

Key SQL skills: inner/left/right joins for combining tables, window functions like ROW_NUMBER() and AVG() OVER() for running calculations, and indexing strategies for faster queries. Example optimization: replace SELECT * with explicit columns and add WHERE filters to reduce scanned rows.

Hands-on Projects, Portfolio, and Beginner Data Analyst Job Prep

Project ideas for 90 days: mini EDA, dashboard, A/B test report — scope and deliverables

Project 1 (Weeks 1–4): Mini EDA — clean data, show key metrics, and write 1-page recommendations. Project 2 (Weeks 5–8): SQL + Python pipeline — automated weekly sales report. Project 3 (Weeks 9–12): Interactive dashboard with a business question and an A/B test analysis.

• Scope each project: objective, dataset, methods, deliverables.
• Deliverables: notebook, dashboard link, one-page case study.
• Quality bar: reproducible steps and clear business recommendations.

Building a portfolio and GitHub: case study structure, documentation, README, visuals

Portfolio checklist:

• Clear README with project goal and steps
• Data provenance and cleaning notes
• Code organized and documented
• Screenshots and embedded dashboards
• One-paragraph business impact statement

Job-ready artifacts: resume bullets, interview questions, sample take-home tasks

Resume example bullet: "Built an automated SQL + Python reporting pipeline that reduced reporting time by 60% and increased accuracy of weekly KPIs." Prepare answers for questions like: "How did you handle missing data?" or "Walk me through your dashboard decisions." Practice take-home tasks under timed conditions.

Resources, Practice Datasets, Assessment, and Next Steps

Curated learning resources: courses, books, cheat sheets, free tutorials

Recommended mix: one structured course (Coursera/edX), a short book (Practical Statistics or Python for Data Analysis), and cheat-sheets for pandas and SQL. Use free tutorials from community blogs and video walkthroughs to reinforce practice. Research-backed plans (community posts and tutorials) consistently recommend starting with Excel and SQL before moving to Python and visualization tools.

Practice datasets and tools: Kaggle, public datasets, sandbox environments, SQL practice

Where to practice: Kaggle datasets, UCI Machine Learning Repository, Google Dataset Search, and public government data. For SQL, use Mode Analytics, SQLBolt, or LeetCode Database problems. Sandbox tools like Google Colab and GitHub make sharing easy.

Measuring progress and next steps: assessments, certifications, specialization paths

Track progress with weekly checklists and monthly capstone deliverables. Suggested assessments: timed SQL quizzes, peer code review, and a mock interview. Certifications (e.g., Google Data Analytics Certificate) are optional but useful for structured validation. Next steps after 90 days: specialize in analytics engineering, ML basics, or a domain vertical like marketing analytics.

Now it's your turn: pick one project from this data analytics beginner guide, set a start date, and commit to the 90-day checkpoints. Share your progress, and iterate—learning compounds when you practice and publish your work.