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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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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/

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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/

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Why NumPy is important ? To start data science journey with Python we must be armed with Numpy library. NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is essential for Data Science is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks. Numpy is incredibly fast, as it has bindings to C libraries. https://python-code.pro/python-numpy-for-datascience-cheat-sheet/

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Why should we use Python ? In last several years Python became a first-class tool for scientific analytical tasks, including the analysis and visualization of large data volumes. The effectiveness of Python for data science is obvious from the language usage itself, but also due to large and active ecosystem of third-party packages, which are really really helpful for data manipulation, common scientific computing tasks, high-quality visualizations, interactive execution and sharing of code, machine learning and many more use cases. https://python-code.pro/python-for-data-science-cheat-sheet/

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Once upon the time while learning Python... Modern world of hard and unpredictable challenges is pretty tough, but sometimes it gives us opportunities to apply and monetize your skills. Some useful things once discovered must be stored in secure place to be helpful in future when needed. So I collected information on Python coding/programming, Computer Science, Statistics and Probability theory, Data Science and Machine Learning, SEO and WEB development spiced up with fun resources below in case someone (even me) find them helpful in a form of a knowledgebase. https://python-code.pro/

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