Many people interested in Data Science have heard or read somewhere that in order to learn how to analyze data you have to learn how to program in Python. This is a half-truth. Python is a necessary but not sufficient condition to be a Data Scientist.
Data Science is a very broad professional field. The reader who wants to know more about it can access Learn Data Science where you will find the different professions that fall within this area of professional and scientific knowledge.
At Ubiqum, of all the professions that fall within the broad field of Data Science, we focus on Business Data Analytics.
This process is described in the steps shown in the following image.
Python is a high-level, versatile, interpreted and easy-to-learn programming language. It stands out for its clear and readable syntax, which makes it suitable for a wide range of applications in software development, data analysis, artificial intelligence, scripting, among other fields.
Python features and highlights:
Python has gained popularity due to its ease of use, versatility and focus on developer productivity. It is a common choice for beginners and professionals due to its ability to solve a wide range of programming problems efficiently and effectively.
At Ubiqum we use Python from the perspective of the data analyst and not the back-end developer. Therefore, our students learn to use in depth the following libraries, which offer a good amount of ready-made programs on logic and mathematical functions that are ready to use:
Scikit-learn is an open source machine learning library for the Python programming language that provides simple and efficient tools for predictive data analysis. This library is designed to be accessible and easy to use, while offering a wide range of machine learning algorithms and tools for preprocessing, model evaluation and more.
Some of the key features and functionalities of scikit-learn include:
This library is widely used in both the academic community and industry due to its ease of use, power and ability to implement machine learning solutions in a variety of contexts and applications. It is a valuable tool for machine learning professionals and enthusiasts looking to implement predictive models and analyze data efficiently and effectively in Python.
Pandas is a powerful Python library designed specifically for structured data manipulation and analysis, providing flexible data structures and efficient tools for processing, cleaning, transforming and exploring datasets. This library is central to the Python ecosystem for data science and data analysis.
Key features and functionalities of Pandas:
Pandas is widely used in industry and academic environments due to its versatility, efficiency and ability to perform complex data analysis and manipulation in a simple manner. It is an essential tool in the Python data analysis process and has contributed significantly to the development of data science, machine learning and data analysis applications in general.
NumPy (Numerical Python) is a powerful Python library used primarily for performing numerical operations and working with multidimensional data structures, such as matrices and arrays. This library is fundamental in the field of scientific computing and data analysis, providing efficient structures for storing and manipulating numerical data.
NumPy’s key features and functionality:
NumPy is a fundamental tool in the field of scientific computing and data analysis in Python. Its ability to work with numerical arrays efficiently, perform advanced mathematical operations and offer optimal performance makes it an essential library for tasks involving intensive numerical computations and multidimensional data manipulation.
At Ubiqum we offer three programs focused on three different student profiles. In each of them the student gets a solid foundation in Python programming and in the use of the libraries mentioned above.
The three options are:
In this option, students with less technical background (mathematics and programming) but with business experience, learn solid fundamentals of Python and SQL, the use of the main machine learning algorithms for the creation of models and the advanced use of the Power BI tool. (Three-module, 480-hour course).
In this option, students with a good technical background (STEM), in addition to Python and SQL learn R, a very efficient language for data processing with mathematical and statistical formulas that perfectly complements Python. In this course the student delves into machine learning algorithms and advanced modeling (three-module course of 480 hours).
This advanced modality, designed for students with an excellent technical background (STEM), adds a fourth module that includes advanced operations with machine learning algorithms and time series analysis.
To decide which course fits your profile and career plan we offer you a free coaching session. Remember that you have a two-week free trial to have a real first-hand experience, with your personal coach, before making the decision to formalize the course.
When we started Ubiqum in 2016, Data Science was a very new concept and today there is still some confusion. So let’s start by clarifying which professional roles have a role in the field of Data Science.
One of the trendy concepts nowadays is Artificial Intelligence in its Data Science and Machine Learning aspects. However, there are few non-specialists who know how to clearly define what we are talking about when we talk about a Machine Learning course.
Data Science. Just 10 years ago nobody was talking about this topic and today it is one of the areas in which the demand for expert professional profiles is growing the most.
Are you interested in data science and want to know which tools are used? In this article we will introduce you from the most used tools that every analyst should know to the most advanced ones.
