Our Courses

Introducción a Pandas para Data Science

En este proyecto guiado obtendrás experiencia práctica trabajando con la librería Pandas y creando tu propio cuaderno de Jupyter Lab.

• Course by
• Self Paced
• 3 hours
• Spanish

Introducción a la ciencia de datos aplicada

Este curso es una primera inmersión en el mundo de la ciencia de datos, en el cual el estudiante comprenderá los fundamentos de la ciencia de datos, las características de un científico de datos, las herramientas que utiliza, la metodología que se debe seguir para este estilo de proyectos, y estará en capacidad de aplicar técnicas estadísticas para la construcción e interpretación de modelos analíticos descriptivos. El curso consta de 4 módulos, cada uno de una semana, en los cuales al final del mismo, se tiene una lección dedicada al desarrollo del proyecto del curso.

• Course by
• Self Paced
• Spanish

Hierarchical Clustering using Euclidean Distance

By the end of this project, you will create a Python program using a jupyter interface that analyzes a group of viruses and plot a dendrogram based on similarities among them.

• Course by
• Self Paced
• 3 hours
• English

NLP Modelos y Algoritmos

Este curso te brindará los conocimientos necesarios para la implementación de algoritmos de NLP. Mediante el uso de los últimos algoritmos más populares en NLP se procederá a dar solución a un conjunto de problemas propios del área. Para realizar este curso es necesario contar con conocimientos de programación de nivel básico a medio, deseablemente conocimiento básico del lenguaje Python y es recomendable conocer los Jupyter Notebooks en el entorno Anaconda. Para desarrollar aplicaciones se va a utilizar Python 3.6 o superior.

• Course by
• Self Paced
• 11 hours
• Spanish

Intro to TensorFlow 日本語版

このコースの目的は、柔軟で手軽な TensorFlow 2.x と Keras を使用して、機械学習モデルを作成、トレーニング、およびデプロイすることです。TensorFlow 2.x API の階層について学び、TensorFlow の主要コンポーネントを実践演習で理解します。データセットと特徴列の扱い方について学びます。TensorFlow 2.x 入力データ パイプラインの設計と作成の方法について学びます。tf.data.Dataset を使用して csv データ、NumPy 配列、テキストデータ、および画像を読み込む実践演習を行います。数値、カテゴリ、バケット、およびハッシュの特徴列を作成する実践演習も行います。 Keras Sequential API と Keras Functional API を使用してディープ ラーニング モデルを作成する方法を学びます。活性化関数、損失、および最適化について学びます。Jupyter ノートブックの実践演習では、基本的な線形回帰、基本的なロジスティック回帰、および高度なロジスティック回帰の機械学習モデルを作成できます。Cloud AI Platform での大規模な機械学習モデルのトレーニング、デプロイ、および本稼働の方法について学びます。

• Course by
• Self Paced
• Japanese

Aprendiendo Python con circuitos digitales

En este curso basado en un proyecto, aprenderás a crear programas en Python para modelar y simular circuitos digitales, y explorarás objetos, sentencias, funciones y clases de Python para procesar valores lógicos y números enteros y binarios. Al finalizar este proyecto habrás desarrollado una biblioteca de clases y funciones que ayudará a los estudiantes y profesores a abordar los circuitos digitales desde una perspectiva algorítmica y estructural del hardware digital.

• Course by
• Self Paced
• 3 hours
• Spanish

Analiza tu mercado con Python

En este curso aprenderás a realizar un análisis de reseñas de un restaurante utilizando la técnica de análisis de sentimientos en Python, trabajando en el entorno de Jupyter en la nube y utilizando técnicas de procesamiento de texto y algoritmos de machine learning que te permitirán clasificar la opinión de las personas.

• Course by
• Self Paced
• 3 hours
• Spanish

Machine Learning for Accounting with Python

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course.

• Course by
• Self Paced
• 64 hours
• English

Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized.

• Course by
• Self Paced
• 35 hours
• English

Visualizing & Communicating Results in Python with Jupyter

Code and run your first Python program in minutes without installing anything! This course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook. This course helps learners describe and make inferences from data, and better communicate and present data. The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data.

• Course by
• Self Paced
• 11 hours
• English

Introduction to Bayesian Statistics

The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html.

• Course by
• Self Paced
• 13 hours
• English

Python for Data Science, AI & Development

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types.

• Course by
• Self Paced
• 27 hours
• English

Omnibond: Creating an HPC Environment in Google Cloud with CloudyCluster

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you create a complete turn-key High Performance Computing (HPC) environment in Google Cloud. This environment will provide the familiar look and feel of on-prem HPC systems but with the added elasticity and scalability of Google Cloud. In this lab you see how CloudyCluster can easily create HPC/HTC jobs that will run on-prem or in CloudyCluster on Google Cloud. You can rely on the familiar look and feel of a standard HPC environment while embracing the capabilities and elasticity of Google Cloud.

• Course by
• Self Paced
• 2 hours
• English

Data Science Methodology

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies.

• Course by
• Self Paced
• 15 hours
• English

Data Engineering and Machine Learning using Spark

NOTE: This course is currently replaced with IBM Machine Learning with Apache Spark.
Further your data engineering career with this self-paced course about machine learning with Apache Spark! Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.

• Course by
• Self Paced
• 8 hours
• English

Analyze Data in Azure ML Studio

Did you know that you can use Azure Machine Learning to help you analyze data? In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data.

• Course by
• Self Paced
• 3 hours
• English

Computer Vision - Image Basics with OpenCV and Python

In this 1-hour long project-based course, you will learn how to do Computer Vision on images with OpenCV and Python using Jupyter Notebook.

• Course by
• Self Paced
• 2 hours
• English

Guided Project: Create Engaging Reports using Jupyter Book

• Course by
• Self Paced
• English

AI and Public Health

In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with a case study featuring three Jupyter notebook labs where you’ll create an air quality monitoring application for the city of Bogotá, Colombia.

• Course by
• Self Paced
• 9 hours
• English

Guided Tour of Machine Learning in Finance

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures.

• Course by
• Self Paced
• 24 hours
• English

Where, Why, and How of Lambda Functions in Python

In this project we are going to learn about lambda expressions and it's application in python. We are going to start with what is Lambda expression and how we can define it, comparing lambda functions with regular functions in python and at the end we will learn how to use lambda functions for data manipulation and exploration in pandas. this guided-project is completely beginner friendly. you only need to have basic knowledge of python programming and some experience coding in Jupyter notebook environment.

• Course by
• 2 hours
• English

Classification Trees in Python, From Start To Finish

In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser.

• Course by
• Self Paced
• 2 hours
• English

Data Science and Analysis Tools - from Jupyter to R Markdown

This specialization is intended for people without programming experience who seek an approachable introduction to data science that uses Python and R to describe and visualize data sets. This course will equip learners with foundational knowledge of data analysis suitable for any analyst roles. In these four courses, you will cover everything from data wrangling to data visualization.

• Course by
• Self Paced
• English

Python Project for Data Science

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard.

• Course by
• Self Paced
• 9 hours
• English

IBM Data Analyst

Prepare for a career in the high-growth field of data analytics. In this program, you’ll learn in-demand skills like Python, Excel, and SQL to get job-ready in as little as 4 months. Data analysis is the process of collecting, storing, modeling, and analyzing data that can inform executive decision-making, and the demand for skilled data analysts has never been greater. This program will teach you the foundational data skills employers are seeking for entry-level data analytics roles.

• Course by
• Self Paced
• English