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Python Programming

  • Presentation
  • Methodology
  • Your classes
  • Online platform
  • Contents overview​
  • Digital teachers
  • Internships
  • Certifications
  • We are Tokio
  • Contact
  • Presentation
  • Methodology
  • Your classes
  • Online platform
  • Contents overview​
  • Digital teachers
  • Internships
  • Certifications
  • We are Tokio
  • Contact
  • Presentation
  • Methodology
  • Your classes
  • Online platform
  • Contents overview​
  • Digital teachers
  • Internships
  • Certifications
  • We are Tokio
  • Contact
  • Presentation
  • Methodology
  • Your classes
  • Online platform
  • Contents overview​
  • Digital teachers
  • Internships
  • Certifications
  • We are Tokio
  • Contact

COURSE

Python

Programming

Discover Tokio School

Presentation

It is one of the most widely used programming languages in the world. Why? Because of its simplicity and speed when creating programs. In addition, it contains a large number of libraries, which makes it easy to carry out many tasks without having to program from scratch. 63% of programmers recommend learning this language, which is currently used by giants such as Google, Spotify and Youtube.

Specialise!

At Tokio School we give you all the options. Train in Python Programming and choose one, two or the three specialisations we offer you.

You decide!

Artificial Intelligence
Machine Learning
Deep Learning

Objectives

To learn general programming and the basics of object-oriented programming
Use the syntax of the Python language
Work with libraries specialised in data management
Make connections with databases
Manipulate data structures and files
Implement projects as web or desktop applications

Salary

East Europe (Poland, Bulgaria and Romania) is by far the most popular IT outsourcing location for organisations in West Europe. Salaries are between $2500 (€2220) and $5000 (€4442) per month or $30,000 (€26,654.40) and $60,000 (€53,308.80) a year.

According to Glassdoor and PayScale, we have found out the following numbers:

  • The Python engineers get paid $192,639 in Switzerland.
  • The average software engineer salary in Germany is $58,932 (junior Python developers can start from €44,000 a year) while $55,190 in the United Kingdom.
  • In Belgium, the salary is around $45,065.
  • The average software engineer salary in the Netherlands is $49,520 per year.
  • In Denmark, the salary is around $76,526.
Netherlands
  • Average salary: $53,889 (£42,213).
  • Amsterdam ranks as the #4 top European city for IT startups.
  • 29% of developers changed jobs in last year.
  • Junior Python developer’s annual salary is $32,386 (€30,000).

 

The top 3 cities for Python developer salaries in the Netherlands are:

  1. Amsterdam, $50,002 (€46,930).
  2. Rotterdam, $44,749 (€42,000).
  3. Utrecht, $43,850 (€41,156).
Republic of Ireland
  • Average salary: $66,522 (£52,145).
  • Dublin ranks the #8 top European city for IT startups.
  • 37% of developers changed jobs in the last year.

 

The top 3 cities for Python developer salaries in Ireland are:

  1. Galway, $57,624 (€54,084).
  2. Limerick, $55,670 (€52,250).
  3. Dublin, $52,298 (€49,085).
United Kingdom
  • Average salary: $70,500 (£55,263).
  • London ranks as the #1 top European city for IT startups, and it’s the most popular destination for developers looking to work abroad.
  • 32% of developers changed jobs in the last year.
  • Junior Python developer’s annual salary is $39,072 (£30,000).

 

The top 3 cities for Python developer salaries in UK are:

  1. London, $53,023 (£41,756).
  2. Cambridge, $49,745 (£39,175).
  3. Oxford, $45,514 (£35,843).
Denmark
  1. Average salary: $75,758 (£59,285).
  2. Copenhagen ranks as the #9 top European cities for IT startups.
  3. 31% of developers changed jobs in the last year.
Belgium
  • The average pay for a Python Developer is €71,239.
  • Junior Python developer’s annual salary is $53,660 (€49,728).

Career opportunities

Python programmer
Python programmer
Web developer with Python
Web developer with Python
Desktop and graphical user interface developer
Desktop and graphical user interface developer
General programmer: database, file, network...
General programmer: database, file, network...
I have finished my training in Python and I am going to specialise in the profession of the future: Artificial Intelligence. I repeat with Tokio because my level of satisfaction, especially with the teacher, is 120%.
Oriol MarcoPython Programming
Oriol Marco
I look for two things in training: content and attention and I have had that in Tokio. After two engineering degrees and a master's degree in data analysis, in just two months I have completed the training that will allow me to return to my passion: programming.
Nayra BlancoPython Programming
Nayra Blanco
I am a web developer but I wanted to evolve professionally so I came to Tokio. Cristian, the teacher, is a great professional and he doesn't limit himself to the syllabus. The school and he go out of their way to always give us more and the feeling is that you leave very satisfied.
Jorge Alejandro DíazPython programming
Jorge Alejandro Díaz

Metodología

Tailor-made method
Digital teachers
Personalised tutoring
Practical training
Online classes
Tailor-made method

Our courses do not have a start and end date. With Tokio’s 100% online training programme, you decide your pace, circumstances and capabilities and we follow you. Ours is «tailor-made» learning.

Digital teachers

They are your teachers, experts with real knowledge that will help you to improve your knowledge of this profession.

Personalised tutoring

Our educational advisors will accompany you throughout your training. They will help you achieve your goals through realistic objectives, organisation and motivation for tokiers!

Practical training

Self-assessment questionnaires, final exams, exercises, case studies… Learning by doing! You will learn by doing. In addition, you will have up to 300 hours of quality professional internships in companies in the sector.

Online classes

You will have live classes. And if you have not been able to attend, no problem! We’ll upload them to the virtual platform so you can watch them as many times as you want.

Final project
Soft Skills
Job Orientation
Employment Observatory
Final project

You’re almost there! To conclude your training, you’ll have to demonstrate everything you’ve learned through a project.

Soft Skills

You will receive extra training to improve your skills (communication, leadership, teamwork…) thanks to our short courses.

Job Orientation

We will give you all the keys to succeed in any selection process.

Employment Observatory

We put at your disposal, on the student platform, an Employment Observatory where you will find the best job opportunities according to your preferences and your sector.

Your classes

Live
You can connect live to the classes with your specialist teacher. The online classes will follow the syllabus and raise new questions and information that goes beyond the theoretical content of the books. At the end of each class, you can ask your questions so that the teacher can answer them live.
On a recorded basis
If you can’t attend a class live, don’t worry! All classes are recorded and uploaded to your platform so that you can access them whenever you want.
Doubt resolution
The digital teachers will dedicate the whole class to solving your doubts, exercises or practical cases. It is an excellent opportunity to interact with the specialist teacher, ask your questions and learn from the doubts of other classmates.
Masterclass

You will be able to attend online masterclasses given by renowned professionals in the sector who collaborate with Tokio School by sharing their experiences. These sessions will also be participative and you will be able to ask them your questions.

Online platform

Our methodology is designed so that you become the protagonist of the learning process.

Content overview

Module 1: Python the new unkown

Unit 1: Introduction to Python

  • What is Python?
  • The history of Python
  • Versions of Python

Unit 2: Why choosing Python?

  • Python’s evolution and goals

Unit 3: Python’s features

Unit 4: Develop Environments with Python

  • Official Python interpreter and its IDE
  • Anaconda Jupyter
  • PyCharm
  • Visual Studio Code
Module 2: Basic features of the language

Unit 1: Basic data types 

  • Reserved words
  • Comments
  • Python’s basic data types
  • Variables in Python
  • Constants in Python

Unit 2: Python operators

  • Assignment
  • Arithmetic
  • Logical

Unit 3: Input and output

  • Keyboard input
  • Screen output

Unit 4: Advanced data types

  • Lists in Python
  • Tuples in Python
  • Dictionaries in Python
  • Sets in Python

Unit 5: Flow Control – Decision structures and iteration structures

  • Conditionals in Python IF – ELSE
  • Loops in Python WHILE / FOR

Unit 6: Functions

  • Concept of Python Functions
  • Functions implementation
  • Arguments and parameters
  • Integrated functions in Python
  • Good practices with functions
Module 3: Object-oriented programming (OOP)

Unit 1: Object-Based Methodology

  • Structures programming VS OOP (Object-oriented programming)
  • The four principles of OPP

Unit 2: Classes, objects, attributes, and methods

  • Classes, objects, methods and attributes definition

Unit 3: Practising with classes and objects

  • Constructors definition

Unit 4: Inheritance

  • Inheritance definition
  • Uses
  • Implementation
Module 4: OOP and applied methods

Unit 1: Other OOP Tools

  • Multiple inheritance
  • Polymorphism

Unit 2: Applied Methods

  • Strings methods
  • Lists methods
  • Arrays methods
  • Dictionaries methods

Unit 3: Errors and Exceptions

  • Errors
  • Exceptions

Unit 4: Temporal Data

  • Dates and hours
  • Problems with time zones
  • We use different time zones
Module 5: Data manipulation

Unit 1: Files

  • Python file handling

Unit 2: Excel – CSV

  • EXCEL: XLS
  • CSV

Unit 3: JSON

  • What is JSON?
  • JSON vs XML
  • Components
  • Practical example
  • Minified JSON and JSON View
  • JSON in Python

Unit 4: Database

  • What is a database?
  • How data is stored in a database
  • SQL language
  • Databases examples
  • SQLite 3

Unit 5: Libraries specialising in data management

  • NumPy
  • Pandas
  • Other libraries
Module 6: Learning through practice

Unit 1: Creating a web app with Flask and SQLite3 database

Unit 2: Creating a desktop app with Tkinter and SQLite3 database

Sukiru: habilidades para samuráis digitales

Tu formación incluye nuestro Curso Scrum Manager para que te conviertas en todo un experto en la aplicación de esta metodología de trabajo a nivel de equipos y puedas conseguir la certificación oficial Scrum Master

Conoce todos los detalles de este curso

Sukiru: soft skills for digital samurai

You will receive extra training to improve your skills (communication, leadership, teamwork…) thanks to our short courses.

#alwaysforward

Students' work

Sales application, by Oriol Marco

Oriol puts himself in the shoes of the person responsible for the development of an application requested by a computer supply company. In this sense, the application must meet certain requirements for the company’s sales and purchasing management. The project consists of developing a web application for business management, better known as ERP (Enterprise Resource Planning).

Digital teachers

IT and Data Science enthusiast with a Bachelor’s degree in Telecommunications Engineering. More than 4 years of experience in Data Management and Remote Customer Support.
Navil NoorSensei
Navil-Noor

Time to get on the tatami

Do you want to show what you’re worth? At Tokio School we have agreements with more than 3,000 companies in the technology and digital sector. You can do up to 300 hours of optional internships while expanding your network and your CV. Where would you like to do an internship? Suggest companies! You will be part of Tokio Net, our network of students and alumni.

Certifications

Once you have finished your training you will receive the following qualifications:

diploma-python
Python Programming Course

*Training not officially recognised for academic purposes.

We are Tokio

We’re not the kind of people who like to pin medals on themselves, but if others do…

excellence-2022_A-_Tokio_best_training_center_esports-2

TOP Educational Agreements

Contact

Do you have any questions? We are at your disposal for whatever you need.

+353 (1) 9026926

+31 (20) 3694593

+44 (20) 38079342

+32 (2) 7810204

+45 (7) 0890272

The content of this catalogue is subject to change at the discretion of the centre's management. The information not related to the centre contained in this catalogue is subject to the decision of the administration or competent authority.

Training is not approved for official academic purposes.

#alwaysforward

Artificial Intelligence

Get online training in Artificial Intelligence, a very versatile area that is already part of our daily lives. Python is a programming language that facilitates the creation of programmes, and together they form an explosive combination that can be applied in various sectors.
Specialise in Tokio and learn how to integrate new AI developments into existing computer systems and how to design, develop and implement its techniques. Learn the uses of different AI and data analysis libraries and master data mining to become an AI systems architect or an AI technology consultant, among other opportunities.

MODULE 1: GETTING TO KNOW THE ARTIFICIAL INTELLIGENCE

Unit 1: Introduction to Artificial Intelligence

  • What is AI?
  • How do we know that a machine is intelligent?
  • History of AI
  • Sectors of AI
  • AI Technologies
  • AI vs ML vs DL
  • AI examples we use every day
  • Has anyone tried the Turing Test?
  • Revolutions
  • AI categories

Unit 2: AI, search and games

  • Informed and uninformed search
  • AI and games

Unit 3: Science and data mining

  • Data science
  • The Data Science process
  • Data Mining
MODULE 2: LEARNING AND ITS LIBRARIES

Unit 1: Reasoning and learning

  • Reasoning
  • Learning
    • Machine Learning
    • Learning paradigms
    • Learning and classification
    • Classification algorithms
    • Neural networks and Deep Learning

Unit 2: Data analytics libraries and Machine Learning

  • Data analytics libraries: Pandas
  • Machine learning libraries: Scikit-learn
    • Training, test and validation sets
    • Overfitting and underfitting
    • Assessing the performance: confusion matrix
    • Putting it all into practice

Unit 3: Advanced AI libraries

  • TensorFlow and Keras. Example with CIFAR-10 (images classification)
  • SHAP
  • spaCy: Natural Language Processing (NLP)
MODULE 3: KAGGLE PLATFORM

Unit 1: What Kaggle is and some of its projects

  • What is Kaggle?
  • Kaggle’s structure
  • Kaggle: some of its projects

Unit 2: Project

Machine Learning

Machine Learning was born from pattern recognition, but today it allows us to develop applications that improve their performance by «learning» from data collected in past situations. In this Python specialisation you will be able to apply Machine Learning to real projects, including preparation and related tasks, deployment in production and the lifecycle of a model.

MODULE 1: INTRODUCTION TO MACHINE LEARNING

Unit 1: Introduction to Big Data and Machine Learning

  • Introduction to Machine Learning
    • The theory of gravity
    • The scientific method
    • Mathematical models
    • Scientific method applications
    • Data science
    • Introduction to Big Data
    • Introduction to Machine Learning
    • The equation of the straight line
    • Model training
    • Working with Machine Learning models
    • Machine Learning applications
    • AlphaGo
  • Linear algebra
    • Relationship to the areas of big data, machine learning and artificial intelligence
    • Elements
    • Operations and properties

Unit 2: Work environment

Unit 3: Python and Scikit-learn numeric libraries

MODULE 2: SUPERVISED LEARNING

Unit 1: Linear regression 

  • Simple
    • Model equation
    • Graphical representation
    • Types of variables
  • Multivariable
    • Data modelling
    • Curve modelling
    • Analytical resolution
    • Cost function
    • Solving by iterative methods
    • Resolution algorithm

Unit 2: Gradient descent optimisation

  • Gradient descent
  • Convergence
  • Local and global minima
  • Learning ratio
    • Learning ratio choice
  • Training algorithm

Unit 3: Standardisation, regularisation and validation

  • Standardisation
    • Problem
    • What is standardisation?
    • Updated training algorithm
  • Regularisation
    • Deviation and variance
    • Regularisation
    • Regularised cost function
  • Cross-validation
    • Resolution methods
    • Dataset subdivision
    • K-fold
    • Updated training algorithm

Unit 4: Bayesian models and model evaluation

  • Example: carcinogenic cells’ classification
  • Sensitivity and specificity

Unit 5: Classification

  • Decision trees
    • Representation
    • Main concepts
    • Categorical and continuous target variables
    • Node splitting
    • Advantages and disadvantages of decision trees
    • Limitations on tree size
    • Tree pruning
    • Decision trees vs. linear models
    • Bootstrapping
    • Training algorithm
  • Logistic regression
    • Data modelling
    • Binary and multi-class classification
    • Hypothesis
    • Activation function: sigmoid
    • Cost function
    • Training algorithm: binary classification
    • Training Algorithm: multiclass classification
  • Classification by SVM
    • Logistic regression vs. SVM
    • Hypothesis
    • Kernels and landmarks
    • Hypothesis transformation
    • Types of kernels available
    • Cost functions
    • Regularisation parameter
    • Training algorithm: multiclass classification

Unit 6: Introduction to neural networks 

  • Natural neurons
  • Artificial neurons
  • Perceptron
  • Multi-layer or deep neural networks
    • Propagation of predictions
    • Cost function
    • Training
    • Multi-class classification
    • Training algorithm: binary classification
MODULE 3: UNSUPERVISED LEARNING

Unit 1: Optimisation by randomisation

  • Problem: local minima
  • Multiple initialisations
  • Implementation

Unit 2: Clustering

  • Differences between clustering and classification
  • K-means 
  • Other clustering algorithms
MODULE 4 – SEMI-SUPERVISED LEARNING

Unit 1: Anomalies detection

  • The problem
  • Anomalies in supervised vs. unsupervised and semi-supervised learning
  • Model representation
  • Choice of features
  • Normal or Gaussian multivariate distribution
  • Training algorithm

Unit 2: Recommendation systems

  • Linear regression recommendation systems
  • Recommendation systems approach
  • Cost function
  • Training algorithms
  • Prediction performance
  • Similarity between examples

Unit 3: Genetic algorithms

  • Natural evolution
  • Natural evolution of behaviour
  • Main concepts
  • Algorithms applied to optimisation
  • Examples
MODULE 5: AUTOMATIC LEARNING SYSTEMS DEVELOPMENT

Unit 1: ML systems approach

  • Initial approach
    • Data cleansing and transformation
    • Large-scale implementation

Unit 2: Feature engineering

  • Definition and characteristics
  • Creation of characteristics
  • Problems and solutions
  • Data quality

Unit 3: Principal Components Analysis (“PCA”)

  • Variables representation
  • Dimensionality reduction
  • Definition and applications
  • Visual representation

Unit 4: Assemblies

  • Definition and applications
  • Types of errors
  • Assembly techniques
  • Bagging
  • Max voting
  • Mean and weighted mean
  • Random forest
  • Boosting and adaptive boosting or AdaBoosting
  • Stacking

Unit 5: Models’ evaluation and improvement

  • Deviation and variance
  • Evaluation metrics: linear regression
  • Evaluation metrics: classification
  • Deviation and variance avoidance
  • Error analysis and evaluation of results 

Unit 6: Operations in ML

  • ML Engineering 
  • Operations in ML

 

Deep Learning

Deep Learning is one of the most advanced areas of Machine Learning and is applied in virtual assistants, autonomous cars or image information recognition applications. It is an area that is revolutionising the world of technology, aided by one of the fastest and most versatile languages, Python. By specialising in Deep Learning you will learn to work with standard and external libraries and frameworks, you will learn about the types of neural networks and acquire the knowledge for their practical application.

MODULE 1 – INTRODUCTION TO MACHINE LEARNING

Unit 1: Basic fundamentals 

  • Machine Learning basic fundamentals
    • Learning process
    • Types of models
      • Types of algorithms
      • Types of learning modes

Unit 2: Data preparation and tools 

  • Input information
    • Data Basics
    • Data preparation
  • Output information
    • Loss function
    • Model adjustment
  • Learning information
    • Optimization algorithms
  • TensorFlow
    • TensorFlow installation
    • Use of devices
    • Basic operations
    • Calculating gradients
    • Functions
    • Matrix operations
MODULE 2 – INTRODUCTION TO DEEP LEARNING

Unit 1: Neural networks 

  • Neural networks
    • Single Perceptron
    • Multi-layer perceptron
    • Network structure
    • Trigger functions
    • Back propagation
    • Layer initialisation

Unit 2: Classification

  • Classification
    • Loss functions
    • Metrics
    • Binary classification
    • Multi-class classification
    • Multi-label classification
    • Hyperparameter optimisation

Unit 3: Regression 

  • Mathematical linear regression
    • Conditions for linear regression
    • Development of a simple regression with Tensorflow
  • Loss functions
    • Mean Squared Error (MSE)
    • Root Mean Square Error (RMSE)
    • Mean absolute error (MAE)
    • Coefficient of determination
  • Simple linear regression
    • Data preparation
    • Network construction
    • Training phase
    • Visualisation phase
  • Multiple linear regression
    • Data preparation
    • Network construction
    • Training phase
    • Visualisation phase
  • Regularisation
    • Regularisation L1
    • Regularisation L2
    • Elastic net regularisation
MODULE 3 – ARTIFICIAL VISION THROUGH DEEP LEARNING

Unit 1: Convolutional Neural Networks (CNN) 

  • Machine vision using neural networks
    • Information representation
    • Information augmention
    • Layers of neurons
  • Convolutional neural netwroks (CNN)
    • Data preparation
    • Network creation
    • Network compilation
    • Training
    • Inference using external images

Unit 2: Residual Neural Networks (ResNet)

  • Residual neural networks (ResNet)
    • Library import
    • Residual block definition
    • Neuron network definition
    • Other ResNet block types
  • ResNet learning transfer
    • Importing libraries
    • Creating the network

Unit 3: Adversarial Generative Networks (GANs)

  • Adversarial generative networks
    • GAN logical structure
    • GAN operation
    • Loss function calculation
    • Competitive game
  • Construction of a DGAN
    • Data uploading
    • Generator network construction
    • Discriminating network construction
    • DGAN class creation
    • Seasonal training function creation
    • Visualisation and test function creation
    • Training function creation
    • Training our model
    • Using the model
  • Different types of GANs
    • Semi-supervised generative adversarial networks
    • Conditional generative adversarial networks
    • Cyclic generative adversarial networks (CycleGANs)
MODULE 4 – OTHER LEARNING ENVIRONMENTS

Unit 1: recurrent networks oriented to natural language processing

  • Natural language processing
    • Bag of Words (BOW)
    • Embeddings
  • Recurrent Neural Networks
    • Simple recurrent networks
    • Complex recurrent networks
  • Encoders – Decoders
  • Attention models
  • Transformers
  • Recurrent neural network construction
    • Library import
    • Data loading
    • Word coding system preparation
    • Recurrent network creation
    • Training
    • Model testing

Unit 2: Reinforcement learning 

  • Reinforcement learning
    • Markov chain
    • Markov decision process
    • Value functions
    • Bellman function
    • Traditional algorithms
    • Table-based algorithms
  • Deep Reinforcement Learning
  • Building an agent through a DQNN network
    • Libraries import
    • Agent construction
    • Main method construction

Unit 3: Model deployment

  • Models deployment through apification
    • APIs building
    • Building a REST-like API for a model
  • Models deployment through operations
    • Automatisation levels
    • Technologies
  • Models deployment on devices
    • Lite models construction

Módulo de Metodologías ágiles

Consigue la Certificación de Scrum Master

Al igual que evolucionan la tecnología y los lenguajes de programación, las metodologías de trabajo también cambian con el tiempo. Las organizaciones y empresas buscan constantemente formas de agilizar procesos, reducir costes y acelerar la producción para obtener mayores beneficios y es aquí donde la figura del Scrum Master es clave para gestionar de forma efectiva esos proyectos y alcanzar los resultados esperados. 

Nuestro curso en metodologías ágiles con Certificación Scrum te permitirá dominar esta metodología y convertirte en Scrum Master, una de las titulaciones y profesiones más demandadas actualmente. Además, te permitirá entender y aplicar otras metodologías Agile como Kanban, cada vez más en vigor en un mercado tan dinámico como el actual.

Salidas laborales

Scrum master
Scrum master
Consultor de proyectos ágiles
Consultor de proyectos ágiles
Consultor transformación Agile
Consultor transformación Agile
Módulo 1. Introducción a la gestión ágil

Agilidad

  • Gestión predictiva 
  • Gestión ágil
  • Manifiesto ágil: valores y principios
  • Scrum

Desmontando la gestión de proyectos

  • Desarrollo, Trabajo y Conocimiento
  • Ingeniería secuencial, concurrente y agilidad

Diferenciando las prácticas de los principios y valores Scrum

  • Scrum técnico
  • Scrum avanzado
Módulo 2. El ciclo Scrum I

Roles

  • Propietario del producto
  • Equipo
  • Scrum Master

Artefactos

  • Pila del producto
  • Pila del sprint
  • Incremento

Eventos

  • Sprint
  • Reunión de planificación del sprint
  • Scrum diario
  • Revisión del sprint
  • Retrospectiva del sprint

Medición y estimación

Módulo 3. Principios y valores de Scrum II

Principios y valores

  • Las personas y sus roles
  • Artefactos
  • Eventos

Prácticas para flexibilizar Scrum

  • Gráfico burn down
  • Estimación de póquer
  • Kanban
  • Técnicas a prueba de errores
  • …
Módulo 4. La certificación Scrum Máster

Certificación vs Acreditación

  • Scrum Manager y Scrum Máster

Puntos de Autoridad. PDAs

  • Actualización de la Certificación

El examen

Certificación

Una vez realices este curso troncal, podrás presentarte al examen oficial de Scrum Manager. Si lo apruebas, recibirás el certificado de Scrum Master: