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Hi, all
Those who requested for AI tools for data analyst role.

Here's is the 2 min yt video link for 10+ tools.

https://youtu.be/TR8zXEQixvo?si=Fq3Mex_d2sI-BdYr


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Resume key words for data scientist role explained in points:

1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.

2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.

3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.

4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.

5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.


Resume key words for a data analyst role

1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.

2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.

3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.

4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.

5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

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𝐄𝐘 for the Power BI role, these were some of the questions asked during the interview.

𝟏. Can you explain the difference between duplicating and referencing a query in Power Query Editor? How do these operations impact data transformation and query dependencies?

𝟐. What is the distinction between DirectQuery and Live Connection in Power BI? How do these connectivity options affect data retrieval and report performance?

𝟑. Describe the difference between UserPrincipalName (UPN) and UserName in Power BI. How are these identifiers used for user authentication and access control within the platform?

𝟒. What is a Key Performance Indicator (KPI) in the context of Power BI? How do you define and visualize KPIs to monitor business performance effectively?

𝟓. How can you enable clients to modify visualizations in a report after it has been shared or published in Power BI? Explain the approach to empower end-users to customize visuals dynamically.

𝟔. What is the Power Query Editor in Power BI, and how does it facilitate data transformation tasks? Discuss its role in shaping data for use in reports and dashboards.

𝟕. What is a Composite Model in Power BI, and how does it enhance data modeling flexibility? Explain how it allows combining imported data with DirectQuery sources within a single report.

𝟖. Can you highlight significant updates or features introduced in the 2024 version of Power BI that impact data analysis and visualization capabilities?

𝟗. Is it feasible to schedule a report refresh on a monthly basis in Power BI? Describe the available options for scheduling report refresh and any constraints related to monthly refresh cycles.

𝟏𝟎. What are the file formats available to save a Power BI file (e.g., PBIX, PBIT, PBIP )? How do these file formats differ in terms of portability, sharing, and collaboration capabilities within the Power BI ecosystem? Please explain the advantages and use cases for each format.

I am sharing real interview questions asked in companies nowadays to help you prepare more practically for interviews.

7 Data Science Project with Github 🔗 Link


https://www.instagram.com/reel/C7I4P9grvOB/?igsh=cWFuZ3diZjFzYjh1

Comment ❤️ on this video for more resources like this





1. Home Price Predictions - Dataset
https://www.kaggle.com/code/ashydv/housing-price-prediction-linear-regression

2. Credit Card Approval Prediction - You can use the dataset on Kaggle
Kaggle - https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction
Blog -
https://medium.com/analytics-vidhya/machine-learning-steps-explained-using-credit-card-approval-dataset-b18555c48b5a

3. Text Summarization -
Github - https://github.com/dipanjanS/text-analytics-with-python
Kaggle - https://www.kaggle.com/code/mallaavinash/text-summarization

4. Uber Data Analysis
Blog -
https://www.analyticsvidhya.com/blog/2021/10/end-to-end-predictive-analysis-on-ubers-data/
Dataset -
https://www.kaggle.com/datasets/fivethirtyeight/uber-pickups-in-new-york-city


5. Forest Fire Detection.
Github - https://github.com/ErtugrulKusva/Forest_Fire_Detector


6. Video Classification
Github - https://github.com/topics/video-classification


7. Language Detection
Dataset - https://www.kaggle.com/datasets/basilb2s/language-detection
Blog -
https://www.analyticsvidhya.com/blog/2021/03/language-detection-using-natural-language-processing/



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Hi all, 😊
Check out the link For tableau resources.

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Amazon is hiring!
Position: Data Analyst, Analytics
Qualifications: Bachelor’s/ Master’s Degree
Salary: 5 - 8 LPA (Expected)
Experience: Freshers/ Experienced

https://www.amazon.jobs/en/jobs/2616762/data-analyst-abcs-analytics?cmpid=SPLICX0248M&ss=paid&utm_campaign=cxro&utm_content=job_posting&utm_medium=s

Honeywell is hiring!
Position: Data Scientist II
Qualifications: Bachelor’s/ Master’s/ MBA
Salary: 7- 11 LPA (Expected)
Experience: Fresher
Location: Bengaluru

📌Apply Now: https://careers.honeywell.com/us/en/job/HONEUSHRD225742EXTERNALENUS/Data-Scientist-II

Here's how you can create
Unique projects for data analyst portfolio.
I have explained each and every step here in this video.
Go check it out 👍

https://youtu.be/XoU2u9H-hmk?si=iY88Bhrv_FU25i2R

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This repository contains a list of awesome open-source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning


https://www.linkedin.com/posts/akansha-yadav24_machinelearning-deployment-activity-7198283838471479296-0zxp?utm_source=share&utm_medium=member_android


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Save it and share it with your fellow friends...!


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Data Scientist Problems and Tools 🧵

🧹 Data Cleaning - Pandas
📊 Data Visualization - Matplotlib
📈 Statistical Analysis - SciPy
🤖 Machine Learning - Scikit-Learn
🧠 Deep Learning - TensorFlow
💾 Big Data Processing - Apache Spark
📝 Natural Language Processing - NLTK
🚀 Model Deployment - Flask
🔀 Version Control - GitHub
🗄️ Data Storage - PostgreSQL
☁️ Cloud Computing - AWS
🧪 Experiment Tracking - MLflow

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AI tools for data analyst role.

https://youtu.be/TR8zXEQixvo?si=Fq3Mex_d2sI-BdYr

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Check this video and craft the industry standard resume.

https://youtu.be/iAvuAAqu60U?si=w1GQ8REXUi3SgJQv


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Citi Hiring Fresher For Business Analyst
Location: Bangalore
Qualification: Bachelor's Degree
Work Experience: Fresher - 2 Years
Salary: Up to 10 LPA
Apply Link: https://jobs.citi.com/job/-/-/287/65497931696?utm_term=393693070&ss=paid&utm_campaign=apac_experienced&utm_medium=job_posting&source=linkedinJB&utm_source=linkedin.com&utm_content=social_media&dclid=CPO78YTpooYDFT-jZgIdsYYGVw

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All the best 👍👍🖇️🔗

Latest Jobs & Internship Opportunities 👇👇

📌Ascensus is hiring for Data Analyst
Expected Salary: 5 - 8 LPA
Apply here: https://careers.ascensus.com/jobs/analyst-tamil-nadu-india

📌Pinebridge is hiring for Data Scientist
Expected Salary: 6 - 10 LPA
Apply here: https://pinebridge.wd5.myworkdayjobs.com/PineBridge_Career_Site/job/Mumbai/Data-Scientist--Quantitative-Equity-Researcher-2_R-01726

📌TaskUs is hiring for Data Scientist
Expected Salary: 6 - 10 LPA
Apply here: https://jobs.eu.humanly.io/jobs/dc0f3ab1-f2e6-4da8-a803-2dcb52422ed7

📌Honeywell is hiring for Data Scientist II
Expected Salary: 20 - 40 LPA
Apply here: https://careers.honeywell.com/us/en/job/HONEUSHRD225742EXTERNALENUS/Data-Scientist-II

📌Successfactors is hiring for Data Scientist
Expected Salary: 20 - 40 LPA
Apply here: https://career10.successfactors.com/career?career_ns=job_listing&company=axtriaindiP&navBarLevel=JOB_SEARCH&rcm_site_locale=en_US&career_job_req_id=9599

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Complete topics & subtopics of hashtag #SQL for Data Analyst role:-

𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅:
SQL keywords
Data types
Operators
SQL statements (SELECT, INSERT, UPDATE, DELETE)

𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟):
CREATE TABLE
ALTER TABLE
DROP TABLE
Truncate table

𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟):
SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs)
INSERT statement
UPDATE statement
DELETE statement

𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
SUM, AVG, COUNT, MIN, MAX
GROUP BY clause
HAVING clause

𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀:
Primary Key
Foreign Key
Unique
NOT NULL
CHECK

𝟲. 𝗝𝗼𝗶𝗻𝘀:
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
Self Join
Cross Join

𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀:
Types of subqueries (scalar, column, row, table)
Nested subqueries
Correlated subqueries

𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER)
Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD)
Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD)
Conditional functions (CASE, COALESCE, NULLIF)

𝟵. 𝗩𝗶𝗲𝘄𝘀:
Creating views
Modifying views
Dropping views

𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀:
Creating indexes
Using indexes for query optimization

𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀:
ACID properties
Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT)
Transaction isolation levels

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:
Data integrity constraints (referential integrity, entity integrity)
GRANT and REVOKE statements (granting and revoking permissions)
Database security best practices

𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
Creating stored procedures
Executing stored procedures
Creating functions
Using functions in queries

𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
Query optimization techniques (using indexes, optimizing joins, reducing subqueries)
Performance tuning best practices

𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Recursive queries
Pivot and unpivot operations
Window functions (Row_number, rank, dense_rank, lead & lag)
CTEs (Common Table Expressions)
Dynamic SQL

𝗝𝗼𝗶𝗻 𝗺𝘆 𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗖𝗵𝗮𝗻𝗻𝗲𝗹 - https://t.me/codingdidi

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Identifying outliers in a data science project is an important step to ensure the quality and accuracy of your analysis. Outliers can be caused by measurement errors, data entry mistakes, or even intentional manipulation. Here are some approaches you can use to identify liars in your data science project:

1. Visual Exploration: Start by visualizing your data using plots such as histograms, box plots, or scatter plots. Look for any data points that appear significantly different from the majority of the data. Outliers may appear as points that are far away from the main cluster or exhibit unusual patterns.

2. Statistical Methods: Utilize statistical methods to identify outliers. One common approach is to calculate the z-score or standard deviation of each data point and flag those that fall outside a certain threshold (e.g., more than 3 standard deviations away). Another method is the interquartile range (IQR), where data points outside the range of 1.5 times the IQR are considered outliers.

3. Domain Knowledge: Leverage your domain expertise to identify potential outliers. If you have a good understanding of the data and the context in which it was collected, you may be able to identify values that are implausible or inconsistent with what is expected.

4. Machine Learning Techniques: You can use machine learning algorithms to detect outliers. Unsupervised learning algorithms like clustering or density-based methods (e.g., DBSCAN) can help identify unusual patterns or clusters in the data that may indicate outliers.

5. Data Validation: Cross-check your data with external sources or known benchmarks. If possible, compare your data with other reliable sources or conduct external validation to verify its accuracy and consistency.

6. Outlier Detection Models: Train outlier detection models on your dataset. These models can learn patterns from the majority of the data and flag any observations that deviate significantly from those patterns.

It's important to note that not all outliers are necessarily liars or errors; some may represent valid and interesting data points. It's crucial to carefully investigate and understand the reasons behind the outliers before making any decisions about their treatment or exclusion from the analysis.



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Nvidia has launched multiple GenAi, and AI courses.

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