Essential SQL Topics for Data Analysts

- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.

Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:

- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.

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Step-by-step guide to become a Data Analyst in 2025—📊

1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.

2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.

3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.

4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.

5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.

6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.

7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.

8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.

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Complete Roadmap to learn SQL in 2024 👇👇

1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).

2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.

3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.

4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.

5. Subqueries
- Use nested queries for complex data retrieval.

6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.

7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.

8. Indexes
- Understand how to create and use indexes to optimize queries.

9. Views
- Create and manage views for simplified data access.

10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.

11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.

12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.

Here are some free resources to learn  & practice SQL 👇👇

Udacity free course- https://imp.i115008.net/AoAg7K

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For Practice- https://stratascratch.com/?via=free

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Top 10 SQL Projects with Datasets: https://t.iss.one/DataPortfolio/16

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Roadmap to become a data analyst

1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.

2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.

3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.

4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.

5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.

6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.

7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.

8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.

9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.

10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.

11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.

Top companies currently hiring data analysts

Based on the current job market in 2025, here are the top companies hiring data analysts:

## Top Tech Companies

- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation

## Specialized Data Analytics Firms

- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence

## Emerging Opportunities

Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.

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A step-by-step guide to land a job as a data analyst

Landing your first data analyst job is toughhhhh.

Here are 11 tips to make it easier:

- Master SQL.
- Next, learn a BI tool.
- Drink lots of tea or coffee.
- Tackle relevant data projects.
- Create a relevant data portfolio.
- Focus on actionable data insights.
- Remember imposter syndrome is normal.
- Find ways to prove you’re a problem-solver.
- Develop compelling data visualization stories.
- Engage with LinkedIn posts from fellow analysts.
- Illustrate your analytical impact with metrics & KPIs.
- Share your career story & insights via LinkedIn posts.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Data Analyst Learning Plan in 2025

|-- Week 1: Introduction to Data Analytics
| |-- What is Data Analytics?
| |-- Roles & Responsibilities of a Data Analyst
| |-- Data Analytics Workflow
| |-- Types of Data (Structured, Unstructured, Semi-structured)
|
|-- Week 2: Excel for Data Analysis
| |-- Excel Basics & Interface
| |-- Data Cleaning & Preparation
| |-- Formulas, Functions, Pivot Tables
| |-- Dashboards & Reporting in Excel
|
|-- Week 3: SQL for Data Analysts
| |-- SQL Basics: SELECT, WHERE, ORDER BY
| |-- Aggregations & GROUP BY
| |-- Joins: INNER, LEFT, RIGHT, FULL
| |-- CTEs, Subqueries & Window Functions
|
|-- Week 4: Python for Data Analysis
| |-- Python Basics (Variables, Data Types, Loops)
| |-- Data Analysis with Pandas
| |-- Data Visualization with Matplotlib & Seaborn
| |-- Exploratory Data Analysis (EDA)
|
|-- Week 5: Statistics & Probability
| |-- Descriptive Statistics
| |-- Probability Theory Basics
| |-- Distributions (Normal, Binomial, Poisson)
| |-- Hypothesis Testing & A/B Testing
|
|-- Week 6: Data Cleaning & Transformation
| |-- Handling Missing Values
| |-- Duplicates, Outliers, and Data Formatting
| |-- Data Parsing & Regex
| |-- Data Normalization
|
|-- Week 7: Data Visualization Tools
| |-- Power BI Basics
| |-- Creating Reports and Dashboards
| |-- Data Modeling in Power BI
| |-- Filters, Slicers, DAX Basics
|
|-- Week 8: Advanced Excel & Power BI
| |-- Advanced Charts & Dashboards
| |-- Time Intelligence in Power BI
| |-- Calculated Columns & Measures (DAX)
| |-- Performance Optimization Tips
|
|-- Week 9: Business Acumen & Domain Knowledge
| |-- KPIs & Business Metrics
| |-- Understanding Financial, Marketing, Sales Data
| |-- Creating Insightful Reports
| |-- Storytelling with Data
|
|-- Week 10: Real-World Projects & Portfolio
| |-- End-to-End Project on E-commerce/Sales
| |-- Collecting & Cleaning Data
| |-- Analyzing Trends & Presenting Insights
| |-- Uploading Projects on GitHub
|
|-- Week 11: Tools for Data Analysts
| |-- Jupyter Notebooks
| |-- Google Sheets & Google Data Studio
| |-- Tableau Overview
| |-- APIs & Web Scraping (Intro only)
|
|-- Week 12: Career Preparation
| |-- Resume & LinkedIn for Data Analysts
| |-- Common Interview Questions (SQL, Python, Case Studies)
| |-- Mock Interviews & Peer Reviews

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