Here are five of the most commonly used SQL queries in data science:

1. SELECT and FROM Clauses
- Basic data retrieval: SELECT column1, column2 FROM table_name;

2. WHERE Clause
- Filtering data: SELECT * FROM table_name WHERE condition;

3. GROUP BY and Aggregate Functions
- Summarizing data: SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;

4. JOIN Operations
- Combining data from multiple tables:

     SELECT a.column1, b.column2
     FROM table1 a
     JOIN table2 b ON a.common_column = b.common_column;
     

5. Subqueries and Nested Queries
- Advanced data retrieval:

     SELECT column1
     FROM table_name
     WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);

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1. AdventureWorks Sample Database:
- Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15)
- Denoscription: A sample database provided by Microsoft, containing sales, products, customers, and other related data.

2. Online Retail Dataset:
- Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail)
- Denoscription: Transactional data from an online retail store, suitable for customer segmentation and sales analysis.

3. Supermarket Sales Dataset:
- Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales)
- Denoscription: Sales data from a supermarket, useful for inventory management and sales performance analysis.

4. Yahoo Finance (Historical Stock Data):
- Link: [Yahoo Finance](https://finance.yahoo.com/)
- Denoscription: Historical stock data for various companies, suitable for financial analysis and visualization.

5. Human Resources Analytics: Employee Attrition and Performance:
- Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
- Denoscription: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis.

Bonus Open Sources Resources: https://news.1rj.ru/str/DataPortfolio/16

These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

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Free Datasets to work on Power BI + SQL projects 👇👇

1. AdventureWorks Sample Database:
- Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15)
- Denoscription: A sample database provided by Microsoft, containing sales, products, customers, and other related data.

2. Online Retail Dataset:
- Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail)
- Denoscription: Transactional data from an online retail store, suitable for customer segmentation and sales analysis.

3. Supermarket Sales Dataset:
- Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales)
- Denoscription: Sales data from a supermarket, useful for inventory management and sales performance analysis.

4. Yahoo Finance (Historical Stock Data):
- Link: [Yahoo Finance](https://finance.yahoo.com/)
- Denoscription: Historical stock data for various companies, suitable for financial analysis and visualization.

5. Human Resources Analytics: Employee Attrition and Performance:
- Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
- Denoscription: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis.

Bonus Open Sources Resources: https://news.1rj.ru/str/DataPortfolio/16

These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

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📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques

🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks

🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools

🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus

🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning

🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
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- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
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Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?

Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.

Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?

Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.

Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.

Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.

Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?

Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?

Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.

Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?

Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?

Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?

Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.

Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?

Python Interview Q&A: https://topmate.io/coding/898340

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🔹 Introduction to Cybersecurity
🔹 Networking Essentials
🔹 Introduction to Modern AI
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