Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras PDF 2023

 Python Data Analysis for Newbies: PDF 2023.

 Numpy/pandas/matplotlib/scikit-learn/keras 

Using Python to explore your data:

Python is a generalist language, easy to use and very powerful. Today many applications and websites like Dropbox, Youtube, Google run on code written in Python.

 It is also now the language that beats the records in terms of adoption and growth rates. Soon after its creation, the scientific community took an interest in it, which led to the writing of the numpy scientific library, From this day the future of language has changed because the writing of this library has laid the foundation on which the scientific and data science ecosystem runs. 

My goal in this article is to briefly introduce you to the Python data ecosystem by performing data analysis.

Analyze your data with Python:

Importing libraries: # We start by importing the libraries we will use throughout this analysis import pandas as pd # library for reading and manipulating data import numpy as np # our famous numpy for numerical calculation import matplotlib.pyplot as plt # for viewing In this article I will work on a database that deals with the Shanghai ranking of world universities. The database was freely distributed by the Classification Body and I got it through the Kaggle platform. Reading the database: classification = pd.read_csv("cwurData.csv") The database has just been read using the Pandas library. As usual I like to quickly look at the first lines of a database to make a visual representation when I work on it. ranking.head() The database therefore contains 2200 observations and 14 variables. Reading the first lines of the database gives us an indication that these rankings were made over several years, so it is likely that some universities come back several times. We go on to determine what are the unique occurrences of universities and how many universities in total we have in the base.

development of tools for data analysis:

Even if each project, the context of each company and the requirements of the customers differ from each other, the truth is that almost every time we talk about data analysis, the same programming language is evoked: Python. Over the years, it has established itself as the main programming resource for the development of tools for data analysis, processing and transformation. And it’s not surprising that in a world where Big Data is increasingly important to businesses, learning Python is becoming a priority for those looking to enter the world of data analytics. Although other programming languages have also emerged in the sector, there is no doubt that there are many arguments in favour of imposing Python in the data analysis industry. One of its main advantages is its simplicity of learning. Anyone with minimal programming knowledge can learn the principles of this language without any problems. And as you learn, you’ll see other benefits, such as versatility and reproducibility. Thus, not only does it allow you to perform a multitude of tasks, but a piece of code, a script written in Python, can be read on any platform. Add to this the fact that this programming language, which has dominated the Big Data sector, has a large development community, which allows it to progress very quickly in the development of new features and scripts. Since it is an open and free source, as well as JavaScript and many others, programmers are encouraged to look for different solutions, incorporate various improvements and develop new features, to include in new applications such as machine learning or Devops.

Learn Python for data analysis:

So, as we’ve already told you, it’s not just about learning Python, it’s about guiding him to the tasks that interest you. You have to be clear about the world you’re in. In this case, data analysis. If this is the case, as with any other programming language or technology, you can do your own research or opt for code schools where you will not only have more resources, but also more support for your learning and more options for finding work in the Big Data market. An alternative is the Ironhack data analysis bootcamp, where you will learn how to work with Python as well as libraries such as Pandas or NumPy, which will help you acquire the skills needed to work as a data analyst in this field.

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