If I had to start learning data analyst all over again, I'd follow this:

1- Learn SQL:

---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)

2- Learn Excel:

---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers

3- Learn BI Tools:

---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation

4- Learn Python (Pandas) Optional:

---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

Hope this helps you 😊

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📊 Core Data Analyst Interview Topics You Should Know ✅

1️⃣ Excel/Spreadsheet Skills
⦁ VLOOKUP, INDEX-MATCH, XLOOKUP (newer Excel fave)
⦁ Pivot Tables for summarizing data
⦁ Conditional Formatting to highlight trends
⦁ Data Cleaning & Validation with formulas like IFERROR

2️⃣ SQL & Databases
⦁ SELECT, JOINs (INNER, LEFT, RIGHT, FULL)
⦁ GROUP BY, HAVING, ORDER BY for aggregations
⦁ Subqueries & Window Functions (ROW_NUMBER, LAG)
⦁ CTEs for cleaner, reusable queries

3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel, Google Data Studio
⦁ Best practices: Choose charts wisely (bar for comparisons, line for trends)
⦁ Dashboards & Interactivity with slicers/drill-downs
⦁ Storytelling with Data to make insights pop

4️⃣ Statistics & Probability
⦁ Mean, Median, Mode, Standard Deviation for summaries
⦁ Correlation vs. Causation (correlation doesn't imply cause!)
⦁ Hypothesis Testing (t-test, p-value for significance)
⦁ Confidence Intervals to gauge reliability

5️⃣ Python for Data Analysis
⦁ Libraries: Pandas for dataframes, NumPy for arrays, Matplotlib/Seaborn for plots
⦁ Data wrangling & cleaning (handling nulls, merging)
⦁ Basic EDA: Describe stats, visualizations, correlations

6️⃣ Business Understanding
⦁ KPI identification (e.g., conversion rate, churn)
⦁ Funnel analysis for drop-offs
⦁ A/B Testing basics to validate changes
⦁ Decision-making support with actionable recommendations

7️⃣ Problem Solving & Case Studies
⦁ Product metrics (DAU/MAU, retention)
⦁ Customer segmentation (RFM analysis)
⦁ Market trend analysis with time-series

8️⃣ ETL Concepts
⦁ Extract from sources, Transform (clean/aggregate), Load to warehouses
⦁ Data pipeline basics using tools like Airflow or dbt

9️⃣ Data Cleaning Techniques
⦁ Handling missing values (impute or drop)
⦁ Duplicates, outliers detection/removal
⦁ Data formatting (standardize dates, text)

🔟 Soft Skills & Communication
⦁ Explaining insights to non-technical stakeholders simply
⦁ Clear visualization storytelling (avoid clutter)
⦁ Collaborating with cross-functional teams for context

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Skills Needed To Become Data Analyst

Useful websites to practice and enhance your data analytics skills
👇👇

1. Python
https://learnpython.org

2. SQL
https://www.sql-practice.com/

3. Excel
https://excel-practice-online.com/

4. Power BI
https://www.workout-wednesday.com/power-bi-challenges/

5. Quiz and Interview Questions
https://t.iss.one/sqlspecialist

Haven't shared lot of resources to avoid too much distraction

Just focus on the basics, practice learnings and work on building projects to improve your skills. Thats the best way to learn in my opinion 😄

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PowerBI Interview Questions 🚀🔥

Machine Learning Roadmap

📊 Data Analytics Basics Cheatsheet

1. What is Data Analytics?
Analyzing raw data to find patterns, trends, and insights to support decision-making.

2. Types of Data Analytics:
⦁ Descriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What might happen next?
⦁ Prescriptive: What should be done?

3. Key Tools & Languages:
⦁ Excel – Quick analysis & charts
⦁ SQL – Query and manage databases
⦁ Python (Pandas, NumPy, Matplotlib)
⦁ Power BI / Tableau – Dashboards & visualization

4. Data Cleaning Basics:
⦁ Handle missing values
⦁ Remove duplicates
⦁ Convert data types
⦁ Standardize formats

5. Exploratory Data Analysis (EDA):
⦁ Summary stats (mean, median, mode)
⦁ Data distribution
⦁ Correlation matrix
⦁ Visual tools: bar charts, boxplots, scatter plots

6. Data Visualization:
⦁ Use charts to simplify insights
⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions)

7. SQL Essentials:
⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY
⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN

8. Python for Analysis:
⦁ Pandas for dataframes
⦁ Matplotlib/Seaborn for plotting
⦁ Scikit-learn for basic ML models

*9. Metrics to Know:
⦁ Growth %, Conversion rate, Retention rate
⦁ KPIs specific to domain (finance, marketing, etc.)

*10. Real-World Use Cases:
⦁ Customer segmentation
⦁ Sales trend analysis
⦁ A/B testing
⦁ Forecasting demand

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