Data Literacy
Python Basics
Let's dive into the beautiful world of programming for data science. Python is a programming language that, by convention, has been one of the most used tools for analyzing, exploring, and designing machine learning models.
Here, I would like to leave you the bases and pieces of advice that can be useful for this initial journey toward the world of data. Data Structure PDF
Computational Thinking
Explores probability’s foundations. The advantages of computational thinking and how stochastic programming is a fundamental piece in both data science and software engineering. Check out the article here: Computational Thinking
Object Oriented Programming
How the philosophical paradigm converges in computer science to shape programming.
Retrieve here: Object-Oriented Programming
Descriptive statistics
It’s a fundamental tool in stages such as data management exploratory data analysis since it offers us a “photographic” approximation of the state and behavior of the data, what information it provides us, and what deeper statistical measures can be applied to. From this, what inference can be built? Retrieve this analysis here: Descriptive Journal.
Computational Statistics
Let’s check on the importance of computational statistics, dynamics, and stochastics simulations, which can be a guide when embarking on the journey of data science.
Retrieve this analysis here: Computational Statistics
Computational Thinking on Analytics
It’s based on fundamental principles in software engineering that favor its understanding of data science and other areas, such as systems administration and social engineering.
Let’s briefly examine how Python can use them for analytical thinking. Retrieve PDF: Computational Analysis
Intro to Machine Learning
A substantial area of development within Data Science is generated through machine learning models. In this article, as an introduction, I offer the bases and some fundamental models when evaluating the nature of the problem to be solved and which is best adapted to meet the objective(s).
Retrieve the PDF here: Intro to Machine Learning
Cyber Intelligence
It has become more relevant during this advent of artificial intelligence, which is here to stay. Knowing its most basic and relevant aspects helps us in good practice to avoid problems both in the business and personal spheres.
Retrieve the PDF file here: Cyber Intelligence.
Linux Server
In the ever-changing world of data science, mastering Linux servers is a crucial skill often overlooked. This knowledge is highly demanded in the industry and a cornerstone for any professional aspiring to stand out. But what does it take to venture into this fascinating world?
Retrieve the PDF file here: Linux Server
Introduction to Pentesting
Interactions with other computer science and system administration branches converge as you move on your data science path. Whether you assist a client, your company, or your own company, knowing "Pen-testing" will make you take the necessary precautions and execute protection tests to observe potential vulnerabilities. Retrieve the analysis here: Intro to Pentesting.
Social Engineering
More significant progress has been made in data collection. However, there are areas where moral and even ethical criteria are left aside to give way to a type of access to information in the social field that uses ingenuity through deception to achieve its objectives.
Its mission involves changing the opinion of one or a group of people or obtaining critical information to carry out extortion; this is what social engineering is all about. Retrieve the analysis here: Social Engineering.
Software Fundamentals
Whatever your background, allow yourself to study the fundamentals of software, the simple stuff that happens under the hood. Software fundamentals favor not only data science and analysis but also cybersecurity, development, and so on. Retrieve the analysis here: Software Fundaments.
