Python Programming Learning Data Science and Analysis:
Introduction:
1- quickly discover the essential concepts of Python programming.
2- to learn to master the libraries most used for data analysis (Data Science).
3- and familiarize yourself with the basic concepts of machine learning.
This course is therefore mainly aimed at people who want to quickly get their foot in the stirrup in this exciting field, or at anyone whose professional activity, related to the data sciences, requires a concrete overview of this area and what it does.
Data Analysis – Basics of Python Programming: This course is intended for beginners who wish to learn the basics of data analysis and machine learning.
1- Understanding the basics of big data, machine learning, and data science.
2- Learn the basics of Python programming, NumPy multidimensional data management, and Pandas data preprocessing.
3- Understand how to manage different data formats in Python.
4- Learn effective data visualization methods and the key concepts of machine learning, to reach the intermediate level in data science.
Aptude has served as expert data consultants for some of the world’s best-known companies, some of which we cannot name.
Our clients cover almost all sectors and ask us to help them in a variety of projects, including full stack development; IT services management, data dashboard and UX/UI.
And while we can do almost anything, some of our best work involves deep data science expertise, especially when it comes to Python plus Data Science.
We love data science and Python so much that we have developed a Python Centre of Excellence in Mexico City, Mexico, to attract, train and place experienced talent in local and remote projects.
In this article, we will explore why we love Python so much for data science, especially compared to other languages such as R or Scala.
The languages of data science:
First, it is useful to understand why programming languages are necessary for data science. The first thing to know is that data has always used some kind of programming language to work.
Relational databases, for example, use SQL forms (including T-SQL) to tell the database what to do with the 0 and 1 that make up the database data. Because data is just that - static fields with (often) structured information.
That’s right. The second thing to know is that data science involves manipulating extremely large datasets ('big data') using complex mathematical algorithms.
Where SQL involves simple commands to join data lines, add or delete data and create simple «views», advanced data science programming languages manipulate data in a way that would It's so expensive, and it's really impossible to do it manually or even in a spreadsheet.
Some of the languages available for data science are:
R Python Matlab Java Hadoop Julia Scala Ruby For our money, Python is where he is. And it’s not just because, survey after survey, Python is the most sought-after, popular and beloved language.
Python is used for 1:
Desktop GUI Scripting Web development Game development Machine Learning Data science Data analysis Artificial intelligence Internet of Things (IoT) Computer vision Web Scraping Natural language processing Scientific and numerical computation Development of software applications Network programming Data Science From Love Things We Work On In Python Programming Language Python is an excellent programming language for data science work.
This is why we love it…
Python is easy to learn.
From a programming point of view, Python is one of the easiest languages to learn, This means it’s also one of the best at building larger teams of experienced developers and it’s easier for our customers to maintain those teams once our core work is complete. We are also likely to find Python already used in client organizations because of this learning facility.
(Of course, learning and working on data in terms of forecasting and analysis is a different issue.)
Python is flexible. It works on almost all platforms, including Windows and MacOS.
As a language, it works quite well for a variety of uses, making it versatile and flexible.
Python programmers are more affordable. Although you can do a lot with Java, R and the Hadoop framework…
This does not mean that work has an affordable price. Industry leaders trust Python.
Google, Youtube, Instagram, NASA, IBM, Netflix, Spotify, Uber, Pinterest, Reddit and many others use Python.
Python is an effective code. For what you can accomplish in R, you will use much less code by writing it in Python.
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