Our Courses

Process Documents with Python Using the Document AI API

This is a self-paced lab that takes place in the Google Cloud console.

• Course by
• Self Paced
• 1 hour
• English

IBM AI Enterprise Workflow

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production.

• Course by
• English

Scrape and analyze data analyst job requirements with Python

In this project, you’ll help a recruitment agency improve its job vacancy sourcing by using Python’s web-scraping capabilities to extract job postings from multiple sites. This task will require you to write a Python script to extract job posting data from the source site and save it to a comma separated values (CSV) file.

• Course by
• Self Paced
• 2 hours
• English

AI Workflow: Business Priorities and Data Ingestion

This is the first course of a six part specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.  Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and ma

• Course by
• Self Paced
• 8 hours
• English

Visualizing Filters of a CNN using TensorFlow

In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework.

• Course by
• Self Paced
• 1 hour
• English

Form Parsing Using Document AI

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will gain hands-on experience using Document AI by parsing forms from within a Jupyter Notebook so that you can automatically extract information from digitally scanned paper forms.

• Course by
• Self Paced
• 1 hour
• English

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

Fundamentals of Machine Learning in Finance

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.

• Course by
• Self Paced
• 18 hours
• English

Web Applications and Command-Line Tools for Data Engineering

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale.

• Course by
• Self Paced
• 15 hours
• English

Statistics for Data Science with Python

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis.

• Course by
• Self Paced
• 14 hours
• English

Interactive Geospatial Visualization:Kepler GL & Jupyter Lab

In this 1-hour long project-based course, you will learn how to easily create beautiful data visualization with Kepler inside Jupyter Notebooks and effectively design different geospatial data visualizations.

• Course by
• Self Paced
• 4 hours
• English

Digital Signal Processing

This Specialization provides a full course in Digital Signal Processing, with a focus on audio processing and data transmission. You will start from the basic concepts of discrete-time signals and proceed to learn how to analyze data via the Fourier transform, how to manipulate data via digital filters and how to convert analog signals into digital format. Finally, you will also discover how to implement real-time DSP algorithms on a general-purpose microcontroller.

• Course by
• Self Paced
• English

IBM Data Analytics with Excel and R

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months. Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language.

• Course by
• Self Paced
• English

Introduction to Data Science

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions.

• Course by
• English

Guided Project: Create Engaging Reports using Jupyter Book

• Course by
• Self Paced
• English

SQL for Data Science with R

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning.

• Course by
• Self Paced
• 28 hours
• English

Introduction to Computational Statistics for Data Scientists

The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST).

• Course by
• Self Paced
• English

Create Machine Learning Models in Microsoft Azure

Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.

• Course by
• Self Paced
• 13 hours
• English

Unsupervised Text Classification for Marketing Analytics

Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.

• Course by
• Self Paced
• 13 hours
• English

IBM Data Science

Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 4 months. No prior knowledge of computer science or programming languages is required. Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes.

• Course by
• Self Paced
• English

Google Advanced Data Analytics

Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹ Advanced data professionals are responsible for collecting, analyzing, and interpreting extremely large amounts of data.

• Course by
• Self Paced
• English

Select Topics in Python

This specialization is intended for people who are interested in furthering their Python skills. It is assumed that students are familiar with Python and have taken the Programming in Python: A Hands-On Tutorial. These four courses cover a wide range of topics. Learn how to create and manage Python package. Use Jupyter notebooks to visualize data with Matplotlib. The third course focuses on the basics of the Django web framework. Finally, learn how to leverage Python for natural langauge processing.

• Course by
• 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

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

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