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diff --git a/Cheat-Sheets/Django/index.html b/Cheat-Sheets/Django/index.html
index e4ba176..dad5c7d 100644
--- a/Cheat-Sheets/Django/index.html
+++ b/Cheat-Sheets/Django/index.html
@@ -1 +1 @@
- Django - Data Science Interview preparation
\ No newline at end of file
diff --git a/Cheat-Sheets/Flask/index.html b/Cheat-Sheets/Flask/index.html
index d484c85..f31f8f2 100644
--- a/Cheat-Sheets/Flask/index.html
+++ b/Cheat-Sheets/Flask/index.html
@@ -1 +1 @@
- Flask - Data Science Interview preparation
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diff --git a/Cheat-Sheets/Hypothesis-Tests/index.html b/Cheat-Sheets/Hypothesis-Tests/index.html
index f592682..73a8463 100644
--- a/Cheat-Sheets/Hypothesis-Tests/index.html
+++ b/Cheat-Sheets/Hypothesis-Tests/index.html
@@ -1,4 +1,4 @@
- Hypothesis Tests in Python (Cheat Sheet) - Data Science Interview preparation
A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters.
Few Notes:
When it comes to assumptions such as the expected distribution of data or sample size, the results of a given test are likely to degrade gracefully rather than become immediately unusable if an assumption is violated.
Generally, data samples need to be representative of the domain and large enough to expose their distribution to analysis.
In some cases, the data can be corrected to meet the assumptions, such as correcting a nearly normal distribution to be normal by removing outliers, or using a correction to the degrees of freedom in a statistical test when samples have differing variance, to name two examples.
Normality Tests
This section lists statistical tests that you can use to check if your data has a Gaussian distribution.
A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters.
Few Notes:
When it comes to assumptions such as the expected distribution of data or sample size, the results of a given test are likely to degrade gracefully rather than become immediately unusable if an assumption is violated.
Generally, data samples need to be representative of the domain and large enough to expose their distribution to analysis.
In some cases, the data can be corrected to meet the assumptions, such as correcting a nearly normal distribution to be normal by removing outliers, or using a correction to the degrees of freedom in a statistical test when samples have differing variance, to name two examples.
Normality Tests
This section lists statistical tests that you can use to check if your data has a Gaussian distribution.
Tests whether a data sample has a Gaussian distribution/Normal distribution.
Assumptions
Observations in each sample are independent and identically distributed (iid).
Interpretation
H0: the sample has a Gaussian distribution.
H1: the sample does not have a Gaussian distribution.
Python Code
# Example of the Shapiro-Wilk Normality Testfromscipy.statsimportshapirodata=[0.873,2.817,0.121,-0.945,-0.055,-1.436,0.360,-1.478,-1.637,-1.869]stat,p=shapiro(data)
@@ -168,4 +168,4 @@
print('Probably the same variances')else:print('Probably at least one variance is different from the rest')
-
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diff --git a/Cheat-Sheets/Keras/index.html b/Cheat-Sheets/Keras/index.html
index 8739019..4d5535d 100644
--- a/Cheat-Sheets/Keras/index.html
+++ b/Cheat-Sheets/Keras/index.html
@@ -1 +1 @@
- Keras - Data Science Interview preparation
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diff --git a/Cheat-Sheets/NumPy/index.html b/Cheat-Sheets/NumPy/index.html
index 95fd17d..8d9cb97 100644
--- a/Cheat-Sheets/NumPy/index.html
+++ b/Cheat-Sheets/NumPy/index.html
@@ -1 +1 @@
- NumPy - Data Science Interview preparation
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diff --git a/Cheat-Sheets/Pandas/index.html b/Cheat-Sheets/Pandas/index.html
index 2f5df21..7fc198d 100644
--- a/Cheat-Sheets/Pandas/index.html
+++ b/Cheat-Sheets/Pandas/index.html
@@ -1 +1 @@
- Pandas - Data Science Interview preparation
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diff --git a/Cheat-Sheets/PySpark/index.html b/Cheat-Sheets/PySpark/index.html
index 0ae4d88..033e3ce 100644
--- a/Cheat-Sheets/PySpark/index.html
+++ b/Cheat-Sheets/PySpark/index.html
@@ -1 +1 @@
- PySpark - Data Science Interview preparation
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diff --git a/Cheat-Sheets/PyTorch/index.html b/Cheat-Sheets/PyTorch/index.html
index 925ce73..7a1aa15 100644
--- a/Cheat-Sheets/PyTorch/index.html
+++ b/Cheat-Sheets/PyTorch/index.html
@@ -1 +1 @@
- PyTorch - Data Science Interview preparation
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diff --git a/Cheat-Sheets/Python/index.html b/Cheat-Sheets/Python/index.html
index a326e4a..40001b4 100644
--- a/Cheat-Sheets/Python/index.html
+++ b/Cheat-Sheets/Python/index.html
@@ -1 +1 @@
- Python - Data Science Interview preparation
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diff --git a/Cheat-Sheets/SQL/index.html b/Cheat-Sheets/SQL/index.html
index a3d0df8..993a78e 100644
--- a/Cheat-Sheets/SQL/index.html
+++ b/Cheat-Sheets/SQL/index.html
@@ -1 +1 @@
- SQL - Data Science Interview preparation
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diff --git a/Cheat-Sheets/Sk-learn/index.html b/Cheat-Sheets/Sk-learn/index.html
index 33df924..90ae09b 100644
--- a/Cheat-Sheets/Sk-learn/index.html
+++ b/Cheat-Sheets/Sk-learn/index.html
@@ -1 +1 @@
- Scikit Learn - Data Science Interview preparation
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diff --git a/Cheat-Sheets/tensorflow/index.html b/Cheat-Sheets/tensorflow/index.html
index 4c8f81b..2fb6c84 100644
--- a/Cheat-Sheets/tensorflow/index.html
+++ b/Cheat-Sheets/tensorflow/index.html
@@ -1 +1 @@
- TensorFlow - Data Science Interview preparation
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diff --git a/Deploying-ML-models/deploying-ml-models/index.html b/Deploying-ML-models/deploying-ml-models/index.html
index b137648..06f3d83 100644
--- a/Deploying-ML-models/deploying-ml-models/index.html
+++ b/Deploying-ML-models/deploying-ml-models/index.html
@@ -1,4 +1,4 @@
- Home - Data Science Interview preparation
This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.
Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.
This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.
Contribute to the platform
Contribution in any form will be deeply appreciated. 🙏
Add questions
❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.
🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.
Add answers/topics
📝 These are the answers/topics that need your help at the moment
This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.
Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.
This platform is maintained by you! 🤗 You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews.
Contribute to the platform
Contribution in any form will be deeply appreciated. 🙏
Add questions
❓ Add your questions here. Please ensure to provide a detailed description to allow your fellow contributors to understand your questions and answer them to your satisfaction.
🤝 Please note that as of now, you cannot directly add a question via a pull request. This will help us to maintain the quality of the content for you.
Add answers/topics
📝 These are the answers/topics that need your help at the moment
🛠 You can also solve existing issues on GitHub and create a pull request.
Say Thanks
😊 If this platform helped you in any way, it would be great if you could share it with others.
Check out this 👇 platform 👇 for data science content:
👉 https://singhsidhukuldeep.github.io/data-science-interview-prep/ 👈
#data-science #machine-learning #interview-preparation
@@ -16,4 +16,4 @@
venv\Scripts\deactivate
To install the latest
pip3installmkdocs
pip3installmkdocs-material
-
Useful Commands
mkdocs serve - Start the live-reloading docs server.
mkdocs build - Build the documentation site.
mkdocs -h - Print help message and exit.
mkdocs gh-deploy - Use mkdocs gh-deploy --help to get a full list of options available for the gh-deploy command. Be aware that you will not be able to review the built site before it is pushed to GitHub. Therefore, you may want to verify any changes you make to the docs beforehand by using the build or serve commands and reviewing the built files locally.
mkdocs new [dir-name] - Create a new project. No need to create a new project
As much as this platform aims to help you with your interview preparation, it is not a short-cut to crack one. Think of this platform as a practicing field to help you sharpen your skills for your interview processes. However, for your convenience we have sorted all the questions by topics for you. 🤓
This doesn't mean that such feature won't be added in the future. "Never say Never"
But as of now there is neither plan nor data to do so. 😢
Why is this platform free? 🤗
Currently there is no major cost involved in maintaining this platform other than time and effort that is put in by every contributor. If you want to help you can contribute here.
If you still want to pay for something that is free, we would request you to donate it to a charity of your choice instead. 😇
😎 The full list of all the contributors is available here
Current Status
Last update: July 1, 2020
\ No newline at end of file
+
Useful Commands
mkdocs serve - Start the live-reloading docs server.
mkdocs build - Build the documentation site.
mkdocs -h - Print help message and exit.
mkdocs gh-deploy - Use mkdocs gh-deploy --help to get a full list of options available for the gh-deploy command. Be aware that you will not be able to review the built site before it is pushed to GitHub. Therefore, you may want to verify any changes you make to the docs beforehand by using the build or serve commands and reviewing the built files locally.
mkdocs new [dir-name] - Create a new project. No need to create a new project
As much as this platform aims to help you with your interview preparation, it is not a short-cut to crack one. Think of this platform as a practicing field to help you sharpen your skills for your interview processes. However, for your convenience we have sorted all the questions by topics for you. 🤓
This doesn't mean that such feature won't be added in the future. "Never say Never"
But as of now there is neither plan nor data to do so. 😢
Why is this platform free? 🤗
Currently there is no major cost involved in maintaining this platform other than time and effort that is put in by every contributor. If you want to help you can contribute here.
If you still want to pay for something that is free, we would request you to donate it to a charity of your choice instead. 😇
😎 The full list of all the contributors is available here
Current Status
\ No newline at end of file
diff --git a/Interview-Questions/Natural-Language-Processing/index.html b/Interview-Questions/Natural-Language-Processing/index.html
index 2e4ad9e..0c905c1 100644
--- a/Interview-Questions/Natural-Language-Processing/index.html
+++ b/Interview-Questions/Natural-Language-Processing/index.html
@@ -1 +1 @@
- NLP Questions - Data Science Interview preparation
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diff --git a/Interview-Questions/Probability/index.html b/Interview-Questions/Probability/index.html
index 4462d37..651c25f 100644
--- a/Interview-Questions/Probability/index.html
+++ b/Interview-Questions/Probability/index.html
@@ -1,4 +1,4 @@
- Probability Questions - Data Science Interview preparation
Consider a fair 6-sided dice. Your aim is to get the highest score you can, in at-most 3 roles.
A score is defined as the number that appears on the face of the dice facing up after the role. You can role at most 3 times but every time you role it is up to you to decide whether you want to role again.
The last score will be counted as your final score.
Find the average score if you rolled the dice only once?
Find the average score that you can get with at most 3 roles?
If the dice is fair, why is the average score for at most 3 roles and 1 role not the same?
The average score if you rolled the dice only once is 3.5
For at most 3 roles, let's try back-tracking. Let's say just did your second role and you have to decide whether to do your 3rd role!
We just found out if we role dice once on average we can expect score of 3.5. So we will only role the 3rd time if score on 2nd role is less than 3.5 i.e (1,2 or 3)
Possibilities
2nd role score
Probability
3rd role score
Probability
1
⅙
3.5
⅙
2
⅙
3.5
⅙
3
⅙
3.5
⅙
4
⅙
NA
We won't role
5
⅙
NA
3rd time if we
6
⅙
NA
get score >3 on 2nd
So if we had 2 roles, average score would be:
[We role again if current score is less than 3.4]
+ Probability Questions - Data Science Interview preparation
Consider a fair 6-sided dice. Your aim is to get the highest score you can, in at-most 3 roles.
A score is defined as the number that appears on the face of the dice facing up after the role. You can role at most 3 times but every time you role it is up to you to decide whether you want to role again.
The last score will be counted as your final score.
Find the average score if you rolled the dice only once?
Find the average score that you can get with at most 3 roles?
If the dice is fair, why is the average score for at most 3 roles and 1 role not the same?
The average score if you rolled the dice only once is 3.5
For at most 3 roles, let's try back-tracking. Let's say just did your second role and you have to decide whether to do your 3rd role!
We just found out if we role dice once on average we can expect score of 3.5. So we will only role the 3rd time if score on 2nd role is less than 3.5 i.e (1,2 or 3)
Possibilities
2nd role score
Probability
3rd role score
Probability
1
⅙
3.5
⅙
2
⅙
3.5
⅙
3
⅙
3.5
⅙
4
⅙
NA
We won't role
5
⅙
NA
3rd time if we
6
⅙
NA
get score >3 on 2nd
So if we had 2 roles, average score would be:
[We role again if current score is less than 3.4]
(3.5)*(1/6) + (3.5)*(1/6) + (3.5)*(1/6)
+
(4)*(1/6) + (5)*(1/6) + (6)*(1/6) [Decide not to role again]
@@ -10,4 +10,4 @@
(5)*(1/6) + (6)*(1/6) [[Decide not to role again]
=
17/6 + 11/6 = 4.66
-
The average score if you rolled the dice only once is 4.66
The average score for at most 3 roles and 1 role is not the same because although the dice is fair the event of rolling the dice is no longer independent. The scores would have been the same if we rolled the dice 2nd and 3rd time without considering what we got in the last roll i.e. if the event of rolling the dice was independent.
Last update: July 1, 2020
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+
The average score if you rolled the dice only once is 4.66
The average score for at most 3 roles and 1 role is not the same because although the dice is fair the event of rolling the dice is no longer independent. The scores would have been the same if we rolled the dice 2nd and 3rd time without considering what we got in the last roll i.e. if the event of rolling the dice was independent.
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diff --git a/Interview-Questions/System-design/index.html b/Interview-Questions/System-design/index.html
index f44cad1..cf0c555 100644
--- a/Interview-Questions/System-design/index.html
+++ b/Interview-Questions/System-design/index.html
@@ -1 +1 @@
- System Design - Data Science Interview preparation
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diff --git a/Interview-Questions/data-structures-algorithms/index.html b/Interview-Questions/data-structures-algorithms/index.html
index 8b2eef1..f9c7f7a 100644
--- a/Interview-Questions/data-structures-algorithms/index.html
+++ b/Interview-Questions/data-structures-algorithms/index.html
@@ -1,4 +1,4 @@
- Data Structure and Algorithms - Data Science Interview preparation
👀 This project is in early stages of development. 🤗Please contibute content if possible! 🤝You can submit simple text/markdown content, I will format it! 🙌