Learning Phyton

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.

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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.

The cycle always starts in a certain business context, be it costs, sales, marketing, logistics…
And within this context we identify a problem that requires analysis of the available data to shed more light on its solution.
The third and fourth steps are to see what data we have available in the Data Wharehouse and compose a Data Set with the data that we believe will be useful to solve the problem.
This is where the real work of a business data analyst begins, as we approach it at Ubiqum.
Data Understanding, Data Preparation and Modeling are the three fundamental steps in which Python plays a major role.
The Python libraries, explained below, are the tools that a Data Analyst uses to perform these three important tasks.

What is Python?

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:

Readability and Simplicity: Python’s clear and structured syntax favors code readability, making it easy to write and understand.
Multiparadigm: Supports multiple programming paradigms, including object-oriented, imperative and functional programming, allowing developers to choose the most appropriate approach for their needs.
Extensive Standard Library: Offers a comprehensive standard library ranging from basic operations to advanced modules, providing a wealth of ready-to-use tools and functions.
Portability and Multiplatform: It is compatible with a wide variety of platforms, including Windows, macOS and Linux, making it easy to use in different environments.
Active Community and Extensive Ecosystem: It has a large and active community of developers who contribute libraries, frameworks and tools that extend its functionality and applicability in various fields.
Diverse Applications: Used in a variety of applications, from web development and desktop application creation to data analysis, machine learning, data science, among others.
Scalability and Maintainability: It is used in small projects as well as in large-scale applications and systems due to its ability to manage projects efficiently and its ease of maintenance.

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

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:

Wide Variety of Algorithms: Offers implementations of a wide range of supervised and unsupervised machine learning algorithms, including regression, classification, clustering, dimensionality reduction, among others.
Consistent API: Provides a consistent and easy-to-use interface for different algorithms, allowing for rapid experimentation and model tuning.
Data Preprocessing: Includes tools for preprocessing and transforming data, such as imputation of missing values, feature scaling, coding of categorical variables, among others.
Model Selection and Evaluation: Provides functions for model selection through hyperparameter search, cross-validation and evaluation metrics to measure model performance.
Integration with NumPy and SciPy: Seamlessly integrates with other popular Python libraries such as NumPy and SciPy, facilitating data manipulation and the use of scikit-learn algorithms.
Complete documentation: Detailed documentation and examples are available for each algorithm, which facilitates its understanding and application in machine learning projects.
Adaptability: It is possible to extend the functionality of scikit-learn by implementing custom estimators or developing new algorithms.
Open Source License: Scikit-learn is distributed under an open source license, which allows its use, modification and distribution freely.

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

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:

Flexible Data Structures: pandas provides two main data structures: Series and DataFrames. Series are one-dimensional arrays with labels, while DataFrames are two-dimensional structures similar to database tables, with labeled rows and columns.
Data Manipulation: Enables efficient data manipulation, including operations such as selection, filtering, grouping, joining, concatenation and transformation of data sets, providing robust and flexible methods for these operations.
Data cleansing: Facilitates data cleaning and preparation through functions to handle missing values, duplicates, typos, and other anomalies in data sets.
Advanced Indexing and Selection: Provides advanced indexing and selection capabilities, allowing access to data through tags, integer indexes, Boolean conditions or complex expressions.
Date and Time Manipulation: Includes tools for working with time series data, facilitating date manipulation, period and frequency calculations, and time-based data analysis.
Integrated Data Visualization: Pandas integrates easily with other visualization libraries such as Matplotlib and Seaborn, enabling rapid generation of graphs and visualizations from data stored in pandas structures.
Efficient Operations: It is optimized to perform efficient operations on large data sets, which helps reduce processing time and resource consumption.
Compatibility and Flexibility: pandas supports a wide variety of data sources, including CSV files, Excel, SQL databases, JSON, HTML, among others. In addition, it is flexible and adaptable to different workflows and specific data analysis requirements.

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

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:

Numerical Arrays: Introduces the fundamental NumPy object, the ndarray, which represents homogeneous n-dimensional arrays and allows efficient storage of numerical data.
Efficient Numerical Operations: Provides a comprehensive set of mathematical functions and linear algebra operations, such as addition, subtraction, multiplication, division, exponentiation, trigonometry, among others, that are efficiently applied to large data sets.
Advanced Indexing and Selection: Provides advanced capabilities for indexing and selecting array elements, allowing access to data through indexes, ranges, Boolean masks and complex logical expressions.
Broadcasting: Allows you to perform operations between arrays of different shapes and sizes, applying rules to automatically extend the dimensions of the arrays to fit mathematical operations.
Data Manipulation: Includes functions to reshape, resize, split, merge and concatenate arrays, which facilitates the manipulation and transformation of multidimensional data.
Efficiency and Performance: It is implemented in C and optimized to perform operations quickly and efficiently, making it suitable for intensive numerical operations and handling large volumes of data.
Integration with other Libraries: NumPy easily integrates with other Python libraries, such as pandas, Matplotlib, SciPy and scikit-learn, enabling its use in complete data analytics and data science workflows.
Low-Level Data Manipulation: Allows data manipulation at a lower level, providing access to functions to manipulate pointers, memory and other low-level details, which can be useful in specific applications.

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.

Python at Ubiqum

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:

Business Analytics & Power BI

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).

Data Analytics & Machine Learning

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).

Data Science & Deep Learning

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.

Learning Phyton

What is Python?

Scikit-learn

Pandas

Numpy

Python at Ubiqum

Business Analytics & Power BI

Data Analytics & Machine Learning

Data Science & Deep Learning

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