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What is SciPy ?

SciPy or a Scientific Python is a famous Python library that widely used in data world. It is uses NumPy underneath and together they help us to work with mathematical equations and algorithms.

SciPy is open source and due to this it has a large community of developers which create enormous number of modules for scientific applications. https://python-code.pro/scipy-for-data-science-cheat-sheet/

He: You are the ';' to my code.
She: I code in Python.

What is Pandas ?

Pandas - short for PANel DAta - is a special python library that can be used for data handling, manipulation, statistical analysis and much more. In simple words Pandas can be described as Excel for Python geeks.

It's statistics functions are very powerful and useful for generating insights from data. Pandas has large number of methods to compute descriptive statistics and other related operations on Series and DataFrames.



https://python-code.pro/pandas-for-data-science-cheat-sheet/

I can learn Python.

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SQL for Data Science essential cheat sheet.

SQL - structured query language - is a classical tool for data analysis. Together with Excel and Python it builds modern must have package for successful Data Science carrier.

SQL aka SEQUEL is a programming language designed for managing data in a relational database, it helps to build complex models and perform analyses quickly and offers a great ability for data manipulation.

Compared to Python using SQL to transform data inside the warehouse is usually much faster for many basic queries and aggregations as it moves code to data rather than data to code.
https://python-code.pro/sql-for-data-science-cheat-sheet/

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Data preparation with SQL.

After using SQL (structured query language) for data analysis let's dive deeper in data handling process and concentrate on dataset preparation task.

We will see how describe input data, work with attributes, handle missing data, find and filter outliers, encode primary data, split and combine datasets.

Although SQL is not considered as modern tool as Python for data handling, but it gives you nice to have additional instrumets in your data exploration journey.

https://python-code.pro/dataset-preparation-with-sql/

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What is Scikit Learn ?

If you’re dealing with Machine Learning in Python, Scikit Learn is considered as the gold standard.
Scikit-learn is an open source Python library that provides a wide selection of supervised and unsupervised learning algorithms. It implements a range of machine learning tools performing preprocessing, cross-validation and visualization using a unified interface. https://python-code.pro/scikit-learn-for-data-science-cheat-sheet/

Why does Python live on land?
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Python for Regression.

Python programming language and its libraries combined together form a powerful tool for solving Regression analysis tasks.

Regression study is a predictive modelling method that analyzes the relation between the target or dependent variable and features or independent variables in a dataset.

The different types of regression analysis methods are used when the target and independent features described by a linear or non-linear relationships between each other, and the target variable contains continuous values. The regression technique gets used mainly to determine the predictor strength, forecast trends, time series, and sometimes in case of cause & effect relation. Regression analysis is the basic technique to solve the regression problems in machine learning ML using data models. It consists of determining the best fit line, which is a line that passes through all the data points in such a way that distance of the line from each data point is optimal/minimized. https://python-code.pro/regression-models-python-cheatsheets/

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Data Science Classification use case.

Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Classification analysis tasks.

Classification is one of the most fundamental concepts in data science.

Classification is a task that requires the use of machine learning algorithms that learn how to assign a class label to examples from the problem domain. From a modeling perspective, classification requires a training dataset with many examples of inputs and outputs from which to learn.A model will use the training dataset and will calculate how to best map examples of input data to specific class labels. As such, the training dataset must be sufficiently representative of the problem and have many examples of each class label.

https://python-code.pro/classification-models-python-r-cheatsheets/

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Random rhetorical question:

Do you realize every time you put your glasses on you make a spectacle of yourself?

https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/