# 📚 180-Day Free Roadmap to Become a Job-Ready Data Analyst

This roadmap is designed for **aspirants with zero experience**, focusing on **free learning paths**, **projects**, **interview prep**, and **tools** that are widely accepted in the industry.

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## 🎯 GOAL:
To become a **job-ready Data Analyst (Entry-Level)** within **6 months** using **only free resources**.

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## 🔧 TOOLS & SKILLS YOU WILL MASTER:

| Tool | Description |
|------|-------------|
| SQL | Querying databases, joins, aggregations |
| Python | Pandas, NumPy, Matplotlib, Seaborn |
| Excel | Pivot tables, charts, functions |
| Tableau Public | Dashboards, visual storytelling |
| Power BI | Interactive reports, DAX |
| Git/GitHub | Version control, portfolio building |

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# ✅ PHASE-WISE ROADMAP (Free Resources Only)

| Phase | Duration | Focus Area | Goals |
|-------|----------|------------|-------|
| Phase 1 | Day 1–30 | Basics + SQL + Excel | Learn SQL, Excel, and basics of data analysis |
| Phase 2 | Day 31–60 | Python + Stats | Learn Python, Pandas, basic stats |
| Phase 3 | Day 61–90 | Visualization + EDA | Master visualization tools (Tableau/Power BI), EDA |
| Phase 4 | Day 91–120 | Projects + GitHub | Build 3–5 real-world projects |
| Phase 5 | Day 121–150 | Interview Prep | DSA (SQL, Python), MCQs, Case Studies |
| Phase 6 | Day 151–180 | Mock Interviews + Hackathons | Final polish, mock interviews, hackathons |

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## 🔢 DAY-BY-DAY PLAN (FREE RESOURCES ONLY)

### 🟩 PHASE 1: BASICS + SQL + EXCEL (Day 1 – Day 30)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 1-3 | Intro to DA | What is DA? Roles, Responsibilities | [Google Data Analytics Certificate - Free](https://learndigital.withgoogle.com/data-analytics) |
| 4-7 | Excel Basics | VLOOKUP, INDEX-MATCH, Pivot Tables | [Excel Exposure](https://www.excelexposure.com/), [YouTube Tutorials](https://youtube.com/results?search_query=excel+beginners+tutorial) |
| 8-10 | Intermediate Excel | Charts, Conditional Formatting | Same as above |
| 11-15 | SQL Basics | SELECT, WHERE, GROUP BY, ORDER BY | [Mode SQL Tutorial](https://mode.com/sql-tutorial/), [W3Schools SQL](https://www.w3schools.com/sql/) |
| 16-20 | SQL Joins & Subqueries | INNER JOIN, LEFT JOIN, Nested Queries | Mode SQL, LeetCode Easy |
| 21-25 | SQL Aggregations & Window Functions | SUM, AVG, COUNT, RANK(), ROW_NUMBER() | Mode SQL, HackerRank |
| 26-30 | Practice SQL + Excel | Solve 20+ SQL problems, build dashboard in Excel | [LeetCode](https://leetcode.com/), [HackerRank](https://www.hackerrank.com/), [Kaggle datasets](https://kaggle.com/datasets) |

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### 🟦 PHASE 2: PYTHON + STATISTICS (Day 31 – Day 60)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 31-35 | Python Basics | Variables, Loops, Functions | [Python for Everybody - Coursera](https://www.coursera.org/specializations/python) |
| 36-40 | Numpy & Pandas | Arrays, Series, DataFrame | [Kaggle Python Course](https://www.kaggle.com/learn/python) |
| 41-45 | Data Cleaning | Missing Values, Outliers | [Kaggle Intro to ML](https://www.kaggle.com/learn/intro-to-machine-learning) |
| 46-50 | Descriptive Statistics | Mean, Median, Variance, SD | [Statistics How To](https://www.statisticshowto.com/) |
| 51-55 | Inferential Statistics | Hypothesis Testing, p-value, CLT | [Kaggle Intro to Statistics](https://www.kaggle.com/learn/statistics) |
| 56-60 | Correlation, Regression | Scatter Plots, Linear Reg | [Towards Data Science - Regression](https://towardsdatascience.com/) |

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### 🟨 PHASE 3: VISUALIZATION + EDA (Day 61 – Day 90)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 61-65 | Matplotlib/Seaborn | Line, Bar, Pie, Histogram | [Kaggle Data Visualization](https://www.kaggle.com/learn/data-visualization) |
| 66-70 | Tableau | Connect Data, Dashboards, Filters | [Tableau Public](https://public.tableau.com/) |
| 71-75 | Power BI | Import Data, Reports, DAX | [Microsoft Learn Power BI](https://learn.microsoft.com/en-us/training/modules/power-bi/) |
| 76-80 | Exploratory Data Analysis (EDA) | Analyze Real Datasets | [Kaggle EDA Notebooks](https://kaggle.com/notebooks) |
| 81-85 | Storytelling with Data | Present Insights Visually | [Storytelling with Data Blog](https://www.storytellingwithdata.com/blog) |
| 86-90 | Practice Dashboard | Create Dashboard using any tool | Use Iris, Boston Housing, Superstore dataset |

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### 🟥 PHASE 4: PROJECTS + GITHUB (Day 91 – Day 120)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 91-95 | Project 1: Sales Analysis | Analyze sales trends, create dashboard | Use Walmart/Superstore dataset |
| 96-100 | Project 2: Customer Segmentation | RFM, Clustering | Mall Customers Dataset |
| 101-105 | Project 3: HR Attrition Analysis | Predict churn, visualize attrition factors | IBM HR Dataset |
| 106-110 | Project 4: Stock Market Trends | Visualize stock trends | Yahoo Finance API |
| 111-115 | Build GitHub Portfolio | Upload all code and dashboards | [GitHub Pages](https://pages.github.com/), README.md |
| 116-120 | Resume Building | Add Projects, Skills, Certifications | Canva Templates (Free), LinkedIn Profile |

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### 🟪 PHASE 5: INTERVIEW PREP (Day 121 – Day 150)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 121-125 | SQL Interview Questions | Solve 50+ questions | [LeetCode SQL](https://leetcode.com/problemset/database/), [StrataScratch](https://platform.stratascratch.com/) |
| 126-130 | Python Interview Questions | Pandas, Numpy, Strings | [HackerRank](https://www.hackerrank.com/), LeetCode |
| 131-135 | MCQs & Aptitude | Statistics, Probability, Business Cases | [Indiabix](https://www.indiabix.com/), [PrepInsta](https://prepinsta.com/) |
| 136-140 | Case Studies | Revenue Drop, User Growth | [Case in Point PDF (Free)](https://www.pdfdrive.com/case-in-point-e14252511.html), ProductX |
| 141-145 | Behavioral Interview | STAR Method, Tell me about yourself | YouTube videos, Glassdoor |
| 146-150 | Mock Interviews | Record and analyze responses | [Pramp](https://www.pramp.com/), Peer groups, Zoom recordings |

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### 🟫 PHASE 6: FINAL POLISH (Day 151 – Day 180)

| Day | Topic | Activities | Free Resources |
|-----|-------|------------|----------------|
| 151-155 | Hackathon Participation | Join Kaggle/Tableau/ML hackathons | [Kaggle Competitions](https://kaggle.com/competitions) |
| 156-160 | Apply Jobs | LinkedIn, Indeed, Glassdoor, AngelList | Update resume, apply daily |
| 161-165 | Debugging Errors | Fix project issues, improve dashboard | Review feedback |
| 166-170 | Soft Skills & Communication | Improve presentation skills | TED Talks, Toastmasters (Free online sessions) |
| 171-175 | Final Revision | All topics, notes, interview prep | Your own notes, flashcards |
| 176-180 | Full Mock Tests | Simulate full interview rounds | Take 3 full-length mocks |

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## 📌 BONUS: YOUR JOB APPLICATION CHECKLIST

✅ Completed 3–5 projects  
✅ GitHub profile with clean documentation  
✅ Updated LinkedIn profile with keywords  
✅ Tailored resume for each application  
✅ Mock interviews recorded and reviewed  
✅ Applied to at least 10 jobs per week  
✅ Attended at least 2 hackathons  
✅ Practiced 100+ SQL & Python questions  
✅ Read blogs/books on analytics and communication  

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## 📝 TIPS FOR SUCCESS:

1. **Stick to a schedule**: Wake up early, set daily goals.
2. **Build a GitHub profile**: Showcase all your work.
3. **Document everything**: Write blogs on Towards Data Science or Medium.
4. **Apply every day**: Don’t wait until Day 180 to start applying.
5. **Track progress**: Use Notion or Excel to track your daily tasks.

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