Data Analyst Jobs
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๐Ÿ† Be the first one to know about the latest data analyst, data scientist, data engineer & business analyst job openings.

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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.

Here you can find essential SQL Interview Resources๐Ÿ‘‡
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Like this post if you need more ๐Ÿ‘โค๏ธ

Hope it helps :)
โค3
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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โค3
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

๐Ÿ’ฌ Tap โค๏ธ for more!
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๐—ฃ๐—ฎ๐˜† ๐—”๐—ณ๐˜๐—ฒ๐—ฟ ๐—ฃ๐—น๐—ฎ๐—ฐ๐—ฒ๐—บ๐—ฒ๐—ป๐˜ - ๐—™๐˜‚๐—น๐—น๐˜€๐˜๐—ฎ๐—ฐ๐—ธ๐——๐—ฒ๐˜ƒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ช๐—ถ๐˜๐—ต ๐—š๐—ฒ๐—ป๐—”๐—œ ๐Ÿ˜

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โค3
๐Ÿ”ฐ Arithmetic operations in numpy arrays
โค4
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
โค3
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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โค9๐Ÿ”ฅ1
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

Like for more job opportunities โค๏ธ
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
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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โค6
๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐Ÿญ๐Ÿฌ๐Ÿฌ+ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ, ๐—”๐—œ, ๐—–๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† & ๐— ๐—ผ๐—ฟ๐—ฒ ๐Ÿš€

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