1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
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10 commonly asked data science interview questions
1️⃣ What is the difference between supervised and unsupervised learning?
2️⃣ Explain the bias-variance tradeoff in machine learning.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
4️⃣ Describe the process of feature selection and why it is important in machine learning.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
6️⃣ What is regularization and why is it used in machine learning?
7️⃣ How do you handle missing data in a dataset?
8️⃣ What is the difference between classification and regression in machine learning?
9️⃣ Explain the concept of cross-validation and why it is used.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Answers for these questions are posted here: https://news.1rj.ru/str/DataScienceInterviews/2
ENJOY LEARNING 👍👍
1️⃣ What is the difference between supervised and unsupervised learning?
2️⃣ Explain the bias-variance tradeoff in machine learning.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
4️⃣ Describe the process of feature selection and why it is important in machine learning.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
6️⃣ What is regularization and why is it used in machine learning?
7️⃣ How do you handle missing data in a dataset?
8️⃣ What is the difference between classification and regression in machine learning?
9️⃣ Explain the concept of cross-validation and why it is used.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Answers for these questions are posted here: https://news.1rj.ru/str/DataScienceInterviews/2
ENJOY LEARNING 👍👍
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Essential Topics to Master Data Science Interviews: 🚀
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
👍31❤10🔥5
150 ChatGPT Money Making Prompts
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-just-got-an-upgrade-and-so-will-activity-7141796382746599424-Mv-t
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-just-got-an-upgrade-and-so-will-activity-7141796382746599424-Mv-t
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Data Science & Machine Learning
150 ChatGPT Money Making Prompts 👇👇 https://www.linkedin.com/posts/sql-analysts_chatgpt-just-got-an-upgrade-and-so-will-activity-7141796382746599424-Mv-t
Sent to all who liked and commented on the post 😄
👍10❤5👏5
🥰1
SQL Complete Study Material Giveaway
👇👇
https://www.linkedin.com/posts/sql-analysts_sql-dataanalytics-sqlqueries-activity-7143156922639196160-cQvF?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_sql-dataanalytics-sqlqueries-activity-7143156922639196160-cQvF?utm_source=share&utm_medium=member_android
😢2
Forwarded from Startup & Business Ideas
STRONG PERSONALITIES HAVE THEIR PRINCIPLES
I am telling you a must have trait if you want to build your personality.
You must live with your STRONG PRINCIPLES!
If you decided to not to smoke or drink, NEVER DO IT whatever the condition is.
THIS IS YOUR PRINCIPLE.
If you decided not to eat non-veg, then never do it.
YOU CAN HAVE YOUR OWN STRONG PRINCIPLES IN LIFE!
The thing that matter is to follow them no matter what the condition is!!!
I am telling you a must have trait if you want to build your personality.
You must live with your STRONG PRINCIPLES!
If you decided to not to smoke or drink, NEVER DO IT whatever the condition is.
THIS IS YOUR PRINCIPLE.
If you decided not to eat non-veg, then never do it.
YOU CAN HAVE YOUR OWN STRONG PRINCIPLES IN LIFE!
The thing that matter is to follow them no matter what the condition is!!!
👍25❤5🔥5👏1
What are predictive algorithms in the context of the stock market?
https://news.1rj.ru/str/stockmarketingfun/277
https://news.1rj.ru/str/stockmarketingfun/277
👍4
Every Data Scientist should know this
👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-cheatsheet-activity-7144556047448391680-ir90?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-cheatsheet-activity-7144556047448391680-ir90?utm_source=share&utm_medium=member_android
❤4
Mastering Shortcuts for Data Scientists
👇👇
https://www.linkedin.com/posts/sql-analysts_mastering-shortcuts-for-data-scientists-activity-7145277074553970688-hVkJ?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_mastering-shortcuts-for-data-scientists-activity-7145277074553970688-hVkJ?utm_source=share&utm_medium=member_android
👍7
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👍7❤1
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
👍12❤8
Additional Resources To Assist Research
https://www.reddit.com/r/MachineLearning/
• https://www.reddit.com/r/deeplearning/
• https://paperswithcode.com/
• https://www.datasimplifier.com/
• https://papers.nips.cc/
• https://icml.cc/
• https://iclr.cc/
• https://www.researchgate.net/
ENJOY LEARNING 👍👍
https://www.reddit.com/r/MachineLearning/
• https://www.reddit.com/r/deeplearning/
• https://paperswithcode.com/
• https://www.datasimplifier.com/
• https://papers.nips.cc/
• https://icml.cc/
• https://iclr.cc/
• https://www.researchgate.net/
ENJOY LEARNING 👍👍
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Data Science Interview Questions
👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-interview-questions-activity-7151094128284479489-YvbU?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-interview-questions-activity-7151094128284479489-YvbU?utm_source=share&utm_medium=member_android
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