Excel Basics for Data Analysis

‼️What you'll learn

Display working knowledge of Excel for Data Analysis.

Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

Employ data quality techniques to import and clean data in Excel.

Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables

📥https://imp.i384100.net/Qy9rYo

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#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

ادامه در پست بعد👇

#cheat_sheet #Python
🆔 @Python4all_pro

#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

1. arr[i] - Access ith element.
2. arr[i:j] - Access slice from ith to jth element.
3. arr[i:j:k] - Access slice with step k.

*Array Reshaping*

1. arr.reshape() - Reshape array.
2. arr.flatten() - Flatten array.
3. arr.ravel() - Flatten array.

*Array Manipulation*

1. numpy.concatenate() - Concatenate arrays.
2. numpy.split() - Split array.
3. numpy.transpose() - Transpose array.
4. numpy.flip() - Flip array.

*Mathematical Functions*

1. numpy.sin() - Sine.
2. numpy.cos() - Cosine.
3. numpy.tan() - Tangent.
4. numpy.exp() - Exponential.
5. numpy.log() - Natural logarithm.

*Statistical Functions*

1. numpy.min() - Minimum value.
2. numpy.max() - Maximum value.
3. numpy.percentile() - Percentile.
4. numpy.quantile() - Quantile.

*Random Number Generation*

1. numpy.random.rand() - Random numbers.
2. numpy.random.normal() - Normal distribution.
3. numpy.random.uniform() - Uniform distribution.

*Linear Algebra*

1. numpy.dot() - Dot product.
2. numpy.matmul() - Matrix multiplication.
3. numpy.linalg.inv() - Matrix inverse.

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Auto copy paste using Python

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https://github.com/TheAlgorithms/Python


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Automatically Generate Image CAPTCHAs with Python for Enhanced Security



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20 Pandas Functions for 80% of your Data Science Tasks👇

https://levelup.gitconnected.com/20-pandas-functions-for-80-of-your-data-science-tasks-b610c8bfe63c


Maximizing Pandas Efficiency: Top 10 Mistakes to Steer Clear of in Your Code 👇

https://levelup.gitconnected.com/maximizing-pandas-efficiency-top-10-mistakes-to-steer-clear-of-in-your-code-8623aff053cd

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Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)


Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included!
NOTE: Check description for updated Notebook links.

Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever.

In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports.
Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills.

https://www.youtube.com/watch?v=r-uOLxNrNk8&t=683s

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۱۵ چیت شیت machine learning و Data Science


https://blog.dailydoseofds.com/p/15-dsml-cheat-sheets



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