TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
#DataScienceWithDrAngshu #DataScience #Analytics #BigData #MachineLearning #ArtificialIntelligence #Python #SQL #Statistics #DataVisualisation #Experiments #Interview #Job
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
#DataScienceWithDrAngshu #DataScience #Analytics #BigData #MachineLearning #ArtificialIntelligence #Python #SQL #Statistics #DataVisualisation #Experiments #Interview #Job
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<p><strong>If you have decided to learn R as your data science programming language, you have made an excellent decision!</strong>
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TOP PYTHON PROJECTS WITH SOURCE CODE
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https://data-flair.training/blogs/python-project-ideas/
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DataFlair
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Channel name was changed to «Learn data science and Machine Learning»
👩🏻💻 Why should one study Linear Algebra for ML?
👉🏼 Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.
👉🏼 Learn Linear Algebra for Machine Learning with:
Khan Academy: https://www.khanacademy.org/math/linear-algebra
Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953
Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning
Here are some amazing freely available ebooks on the same topic:
Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf
An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/
Happy machine learning! 🎉
👉🏼 Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.
👉🏼 Learn Linear Algebra for Machine Learning with:
Khan Academy: https://www.khanacademy.org/math/linear-algebra
Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953
Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning
Here are some amazing freely available ebooks on the same topic:
Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf
An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/
Happy machine learning! 🎉
❤1👍1
Do you want more books recommendations?
Anonymous Poll
27%
No, only provide online courses
73%
Yes, books are great source for learning
Which step is done just after collecting data?
Anonymous Quiz
10%
Predictive data analysis
21%
Data visualization
69%
Data cleaning