Data Analyst Jobs
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Data Analyst Interview Questions

1. What is a Self-Join?

A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.


2. What is OLTP?

OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.


3. What is the difference between joining and blending in Tableau?

Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.


4. How to prevent someone from copying the cell from your worksheet in excel?

If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.

By entering password you can prevent your worksheet from getting copied.


5. What are the different integrity rules present in the DBMS?

The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.
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๐Ÿš€ ๐—ง๐—ผ๐—ฝ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ป ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜! ๐Ÿ’ผ๐Ÿ”ฅ

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Preparing for a SQL interview?

Focus on mastering these essential topics:

1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!

2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.

3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.

4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.

5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.

6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.

7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.

8. Indexing: Understand how proper indexing can significantly boost query performance.

9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.

10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.

11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.

12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.

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https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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Hope it helps :)
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๐—”๐—ฐ๐—ฐ๐—ฒ๐—ป๐˜๐˜‚๐—ฟ๐—ฒ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ณ๐—ผ๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐˜„๐—ถ๐˜๐—ต ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ ๐Ÿ“Š

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โœ… Basic SQL Commands Cheat Sheet ๐Ÿ—ƒ๏ธ

โฆ  SELECT โ€” Select data from database
โฆ  FROM โ€” Specify table
โฆ  WHERE โ€” Filter query by condition
โฆ  AS โ€” Rename column or table (alias)
โฆ  JOIN โ€” Combine rows from 2+ tables
โฆ  AND โ€” Combine conditions (all must match)
โฆ  OR โ€” Combine conditions (any can match)
โฆ  LIMIT โ€” Limit number of rows returned
โฆ  IN โ€” Specify multiple values in WHERE
โฆ  CASE โ€” Conditional expressions in queries
โฆ  IS NULL โ€” Select rows with NULL values
โฆ  LIKE โ€” Search patterns in columns
โฆ  COMMIT โ€” Write transaction to DB
โฆ  ROLLBACK โ€” Undo transaction block
โฆ  ALTER TABLE โ€” Add/remove columns
โฆ  UPDATE โ€” Update data in table
โฆ  CREATE โ€” Create table, DB, indexes, views
โฆ  DELETE โ€” Delete rows from table
โฆ  INSERT โ€” Add single row to table
โฆ  DROP โ€” Delete table, DB, or index
โฆ  GROUP BY โ€” Group data into logical sets
โฆ  ORDER BY โ€” Sort result (use DESC for reverse)
โฆ  HAVING โ€” Filter groups like WHERE but for grouped data
โฆ  COUNT โ€” Count number of rows
โฆ  SUM โ€” Sum values in a column
โฆ  AVG โ€” Average value in a column
โฆ  MIN โ€” Minimum value in column
โฆ  MAX โ€” Maximum value in column

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๐Ÿ”ฐ Arithmetic operations in numpy arrays
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๐Ÿณ 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—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ๐Ÿ˜ 

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โค2
Citi
Position: Business Analytics Analyst
Qualification: Bachelorโ€™s/ Masterโ€™s Degree
Experienc๏ปฟe: Freshers/ Experienced
Location: Bengaluru, India (Hybrid)

๐Ÿ“ŒApply Now: https://jobs.citi.com/job/bengaluru/business-analytics-analyst-aml/287/96604341728
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Data Analytics Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Descriptive Statistics
|   |   |-- Inferential Statistics
|   |   |-- Probability Theory
|   |
|   |-- Programming
|   |   |-- Python (Focus on Libraries like Pandas, NumPy)
|   |   |-- R (For Statistical Analysis)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
|   |-- Data Sources
|   |   |-- APIs
|   |   |-- Web Scraping
|   |   |-- Databases
|   |
|   |-- Data Storage
|   |   |-- Relational Databases (MySQL, PostgreSQL)
|   |   |-- NoSQL Databases (MongoDB, Cassandra)
|   |   |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
|   |-- Handling Missing Data
|   |-- Data Transformation
|   |-- Data Normalization and Standardization
|   |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
|   |-- Data Visualization Tools
|   |   |-- Matplotlib
|   |   |-- Seaborn
|   |   |-- ggplot2
|   |
|   |-- Identifying Trends and Patterns
|   |-- Correlation Analysis
|
|-- Advanced Analytics
|   |-- Predictive Analytics (Regression, Forecasting)
|   |-- Prescriptive Analytics (Optimization Models)
|   |-- Segmentation (Clustering Techniques)
|   |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
|   |-- Visualization Tools
|   |   |-- Power BI
|   |   |-- Tableau
|   |   |-- Google Data Studio
|   |
|   |-- Dashboard Design
|   |-- Interactive Visualizations
|   |-- Storytelling with Data
|
|-- Business Intelligence (BI)
|   |-- KPI Design and Implementation
|   |-- Decision-Making Frameworks
|   |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
|   |-- Tools and Frameworks
|   |   |-- Hadoop
|   |   |-- Apache Spark
|   |
|   |-- Real-Time Data Processing
|   |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
|   |-- Industry Applications
|   |   |-- E-commerce
|   |   |-- Healthcare
|   |   |-- Supply Chain
|
|-- Ethical Data Usage
|   |-- Data Privacy Regulations (GDPR, CCPA)
|   |-- Bias Mitigation in Analysis
|   |-- Transparency in Reporting

Free Resources to learn Data Analytics skills๐Ÿ‘‡๐Ÿ‘‡

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.me/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.me/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://datacamp.pxf.io/vPyB4L

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://t.me/Data_Visual/2

https://www.tableau.com/learn/training/20223

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

6. Excel

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

https://t.me/excel_data

https://www.w3schools.com/EXCEL/index.php

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๐Ÿ“Š ๐—ง๐—–๐—ฆ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€

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โค2
HP is hiring
Position: Financial Analyst
Qualification: Bachelorโ€™s Degree
Experienc๏ปฟe: 1-2 (Years)
Location: Bangalore, India

๐Ÿ“ŒApply Now: https://apply.hp.com/careers/job/38952213?domain=hp.com&hl=en

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โค1
Colgate
Position: Analyst, Business Analytics
Qualification: Bachelorโ€™s/ Masterโ€™s Degree/ MBA
Experienc๏ปฟe: Freshers/ Experienced
Location: Remote/ Office

๐Ÿ“ŒApply Now: https://jobs.colgate.com/job/Analyst-%2C-Business-Analytics/174376-en_US

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โค2
๐ŸŽ“ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ 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Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ ๐Ÿš€

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โค2
JPMorgan is hiring
Position: Analyst, Financial Reporting
Qualification: Bachelor's Degree
Experienc๏ปฟe: Freshers/ Experienced
Location: Bengaluru, India

๐Ÿ“ŒApply Link: https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/210747001/?keyword=Analyst-+Financial+Reporting&mode=location

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