** Data Science with Python and Dask BOOK PDF FULL FREE 2022.**

**Introduction to Python for Data Science:**

Python is a general-purpose, high-level programming language.

Python is a high-level programming language. It allows you to focus on the basic functionality of the application by handling common programming tasks.

By taking this course, you have entered an exciting world of online learning at your own pace! We will use Jupiter Notebook where examples can be performed and practical tasks and code quizzes can be completed.

**Why learn Python for data science?**

**Before exploring how to learn Python for data science, we need to briefly explain why you should learn Python in the first place. In short, understanding Python is one of the valuable skills needed for a career in data science. Although it hasn’t always been, Python is the programming language of choice for data science. NumPy is a Python library commonly used for Data Science. Find out everything you need to know to master it. Data Science is based on highly complex scientific calculations. To perform these calculations, Data Scientists need powerful tools. The NumPy library for Python is one of these valuable resources.**

**What is NumPy?**The term NumPy is actually the abbreviation for » Numerical Python «. It is an open source library in Python language. This tool is used for scientific programming in Python, and especially for programming in Data Science, engineering, mathematics or science. This library is very useful for performing mathematical and statistical operations in Python. It works great for multiplying multidimensional matrices or arrays.

Integration with C/C++ and Fortran is very easy. Among the many libraries in Python, Numpy is one of the most used. Many Data Science techniques require large-scale tables and matrices and complex calculations to extract valuable information from the data. This process is simplified by NumPy through various mathematical functions. Although basic, it is one of the most important Python libraries for scientific computing.

In addition, other libraries rely heavily on NumPy arrays that they use as inputs and outputs (data input and output). Thus, TensorFlow and Scikit learn how to use NumPy arrays to calculate matrix multiplications.

Beyond that, NumPy also offers features that allow developers to perform basic or advanced mathematical and statistical functions on multi-dimensional arrays and matrices with few lines of code.

The “ndarray” or “n-dimensional array” data structure is the main functionality of NumPy. These tables have the particularity of being homogeneous All elements must therefore be of the same type. In general, NumPy arrays are faster than Python lists. However, knowing that it is only possible to store data of the same type in each column, Python lists are more flexible.

**Pandas data structures:**series objects, DataFrame and index Essential Features Data processing Data pre-processing Data Wrangling Data grouping Data aggregation Pivoting Use of Hierarchical Indexing Data Type Conversion Analysis of time series.

Who is this course designed for: Since this is an introduction to data science, you do not need to be an expert to understand the course.

Of course, having a basic knowledge in python could be a good thing, but it is not mandatory to understand this course.

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