Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics.

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๐Ÿ”ฐ 6 PYTHON WAYS TO REVERSE A LIST
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๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ง๐—–๐—ฆ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—™๐—ฟ๐—ฒ๐˜€๐—ต๐—ฒ๐—ฟ ๐— ๐˜‚๐˜€๐˜ ๐—ง๐—ฎ๐—ธ๐—ฒ ๐˜๐—ผ ๐—š๐—ฒ๐˜ ๐—๐—ผ๐—ฏ-๐—ฅ๐—ฒ๐—ฎ๐—ฑ๐˜†๐Ÿ˜

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Master SQL step-by-step! From basics to advanced, here are the key topics you need for a solid SQL foundation. ๐Ÿš€

1. Foundations:
- Learn basic SQL syntax, including SELECT, FROM, WHERE clauses.
- Understand data types, constraints, and the basic structure of a database.

2. Database Design:
- Study database normalization to ensure efficient data organization.
- Learn about primary keys, foreign keys, and relationships between tables.

3. Queries and Joins:
- Practice writing simple to complex SELECT queries.
- Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables.

4. Aggregation and Grouping:
- Explore aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Understand GROUP BY clause for summarizing data based on specific criteria.

5. Subqueries and Nested Queries:
- Learn how to use subqueries to perform operations within another query.
- Understand the concept of nested queries and their practical applications.

6. Indexing and Optimization:
- Study indexing for enhancing query performance.
- Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes.

7. Transactions and ACID Properties:
- Understand the basics of transactions and their role in maintaining data integrity.
- Explore ACID properties (Atomicity, Consistency, Isolation, Durability) in database management.

8. Views and Stored Procedures:
- Create and use views to simplify complex queries.
- Learn about stored procedures for reusable and efficient query execution.

9. Security and Permissions:
- Understand SQL injection risks and how to prevent them.
- Learn how to manage user permissions and access control.

10. Advanced Topics:
- Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries.
- Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types).

11. Real-world Projects:
- Apply your knowledge to real-world scenarios by working on projects.
- Practice with sample databases or create your own to reinforce your skills.

12. Continuous Learning:
- Stay updated on SQL advancements and industry best practices.
- Engage with online communities, forums, and resources for ongoing learning and problem-solving.

Here are some free resources to learn & practice SQL ๐Ÿ‘‡๐Ÿ‘‡

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

SQL For Data Analysis: https://t.iss.one/sqlanalyst

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

SQL Learning Series: https://t.iss.one/sqlspecialist/567

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

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ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
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๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—”๐—ช๐—ฆ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—”๐—ฏ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ฒ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€๐Ÿ˜

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Here are some of the amazing Websites to Learn Python from Beginning to Advanced. ๐Ÿ‘‡๐Ÿ‘‡

1. LearnPython
๐Ÿ”— Playlist Link

2. W3Schools
๐Ÿ”— Playlist Link

3. Khan Academy
๐Ÿ”— Playlist Link

4. FreeCodeCamp
๐Ÿ”— Playlist Link

5. Sololearn
๐Ÿ”— Playlist Link
๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ปโ€™๐˜ ๐— ๐—ถ๐˜€๐˜€๐Ÿ˜

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These self-paced learning modules are designed by industry experts and cover everything from Python and ML to Microsoft Fabric and Azure๐ŸŽฏ

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

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Job-ready content that gets you resultsโœ…๏ธ
For data analysts working with Python, mastering these top 10 concepts is essential:

1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.

2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.

3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.

4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.

5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.

6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.

7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.

8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.

9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.

10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.

Give credits while sharing: https://t.iss.one/pythonanalyst

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
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๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ง๐—ผ ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ 

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1. Microsoft Azure Fundamentals
2. Power BI Data Analyst Associate
3. Azure Enterprise Data Analyst Associate
4. Introduction to Data Analysis Using Excel (edX)
5. Analyzing & Visualizing Data with Excel (edX)

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Start learning today and transform your career! ๐Ÿš€
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Tuple Slicing in Python ๐Ÿ‘†
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๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐˜€ ๐—œ๐—ป ๐—ง๐—ผ๐—ฝ ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€๐Ÿ˜

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Data Analyst Interview Questions & Preparation Tips

Be prepared with a mix of technical, analytical, and business-oriented interview questions.

1. Technical Questions (Data Analysis & Reporting)

SQL Questions:

How do you write a query to fetch the top 5 highest revenue-generating customers?

Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.

How would you optimize a slow-running query?

What are CTEs and when would you use them?

Data Visualization (Power BI / Tableau / Excel)

How would you create a dashboard to track key performance metrics?

Explain the difference between measures and calculated columns in Power BI.

How do you handle missing data in Tableau?

What are DAX functions, and can you give an example?

ETL & Data Processing (Alteryx, Power BI, Excel)

What is ETL, and how does it relate to BI?

Have you used Alteryx for data transformation? Explain a complex workflow you built.

How do you automate reporting using Power Query in Excel?


2. Business and Analytical Questions

How do you define KPIs for a business process?

Give an example of how you used data to drive a business decision.

How would you identify cost-saving opportunities in a reporting process?

Explain a time when your report uncovered a hidden business insight.


3. Scenario-Based & Behavioral Questions

Stakeholder Management:

How do you handle a situation where different business units have conflicting reporting requirements?

How do you explain complex data insights to non-technical stakeholders?

Problem-Solving & Debugging:

What would you do if your report is showing incorrect numbers?

How do you ensure the accuracy of a new KPI you introduced?

Project Management & Process Improvement:

Have you led a project to automate or improve a reporting process?

What steps do you take to ensure the timely delivery of reports?


4. Industry-Specific Questions (Credit Reporting & Financial Services)

What are some key credit risk metrics used in financial services?

How would you analyze trends in customer credit behavior?

How do you ensure compliance and data security in reporting?


5. General HR Questions

Why do you want to work at this company?

Tell me about a challenging project and how you handled it.

What are your strengths and weaknesses?

Where do you see yourself in five years?

How to Prepare?

Brush up on SQL, Power BI, and ETL tools (especially Alteryx).

Learn about key financial and credit reporting metrics.(varies company to company)

Practice explaining data-driven insights in a business-friendly manner.

Be ready to showcase problem-solving skills with real-world examples.

React with โค๏ธ if you want me to also post sample answer for the above questions

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)
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๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ง๐—ผ ๐—ฆ๐—ธ๐˜†๐—ฟ๐—ผ๐—ฐ๐—ธ๐—ฒ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ๐Ÿ˜

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