Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Data Engineering Capstone Project
This Capstone Project is designed for you to apply and demonstrate your Data Engineering skills and knowledge in SQL, NoSQL, RDBMS, Bash, Python, ETL, Data Warehousing, BI tools and Big Data.
-
Course by
-
6
-
English
AI Applications with Watson
Turbocharge your chatbot's IQ and AI capabilities with IBM Watson Discovery and powered by Watson Assistant. Learn to use and program tools and assitant services such as Tone Analyzer and Personality Insights to build queries, extract information big data repositories.
-
Course by
-
Self Paced
-
45
-
English
Big Data, Hadoop, and Spark Basics
This course provides foundational big data practitioner knowledge and analytical skills using popular big data tools, including Hadoop and Spark. Learn and practice your big data skills hands-on.
-
Course by
-
11
-
English
Big Data Technology Capstone Project
The Big Data Technology Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this MicroMasters program to a medium-scale project.
-
Course by
-
9
-
English
Unix Tools: Data, Software and Production Engineering
Grow from being a Unix novice to Unix wizard status! Process big data, analyze software code, run DevOps tasks and excel in your everyday job through the amazing power of the Unix shell and command-line tools.
-
Course by
-
32
-
English
Big Data for Agri-Food: Principles and Tools
As the big data era unfolds, developments in sensor and information technologies are evolving quickly. As a result, science and businesses are yielding enormous amounts of data. Ideally this data provides valuable insights for decision-making in real time. But processing data the traditional way is no longer possible. Join Wageningen University & Research, #1 university Animal Sciences and Agriculture, and learn how to best handle big data sets. Enrol now.
-
Course by
-
Self Paced
-
English
IoT Programming and Big Data
Learn how to apply software solutions for different systems and Big Data needs to your IoT designs.
-
Course by
-
English
Command Line Tools for Genomic Data Science
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
-
Course by
-
Self Paced
-
12 hours
-
English
Digital Governance
Big data, artificial intelligence, machine learning, autonomous cars, chatbots, just a few terms that have become a part of our professional legal and political vocabulary. Emerging technologies and technological advancement have confronted us in our daily practice and will continue to do so in the future. Whether we’re buying something online, taking part in an election, or chatting with friends across the globe. Technology is here and it is here to stay.
-
Course by
-
Self Paced
-
28 hours
-
English
Foundations of mining non-structured medical data
Rare course alert! This course is very specific and very will give you the edge you need in your career! Learn all about the foundations of Big Data and the data that is being generated in the health domain and how the use of technology would help to integrate and exploit all those data to extract meaningful information that can be later used in different sectors of the health domain from physicians to management, from patients to caregivers, etc.
-
Course by
-
Self Paced
-
7 hours
-
English
Statistics for Genomic Data Science
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
-
Course by
-
Self Paced
-
9 hours
-
English
Scatter Plot for Data Scientists & Big Data Analysts-Visuals
This project gives you easy access to the invaluable learning techniques used by experts for visualization in statistics.
-
Course by
-
Self Paced
-
3 hours
-
English
Big data and Language 1
In this course, students will understand characteristics of language through big data. Students will learn how to collect and analyze big data, and find linguistic features from the data. A number of approaches to the linguistic analysis of written and spoken texts will be discussed. The class will consist of lecture videos which are approximately 1 hour and a quiz for each week. There will be a final project which requires students to conduct research on text data and language.
-
Course by
-
Self Paced
-
5 hours
-
English
Data and Statistics Foundation for Investment Professionals
Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.
In this course you will learn how to:
-
Course by
-
Self Paced
-
21 hours
-
English
Machine Learning with Apache Spark
Explore the exciting world of machine learning with this IBM course. Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos. Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.
-
Course by
-
Self Paced
-
15 hours
-
English
Computer Simulations
Big data and artificial intelligence get most of the press about computational social science, but maybe the most complex aspect of it refers to using computational tools to explore and develop social science theory. This course shows how computer simulations are being used to explore the realm of what is theoretically possible. Computer simulations allow us to study why societies are the way they are, and to dream about the world we would like to live in. This can be as intuitive as playing a video game.
-
Course by
-
Self Paced
-
13 hours
-
English
Machine Learning y Regresión con PySpark. Guía paso a paso
Es un curso práctico y efectivo para aprender a generar modelos de regresión (Machine Learning) con PySpark en un entorno de Big Data.
-
Course by
-
2 hours
-
Spanish
Big Data Fundamentals
Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
-
Course by
-
English
Apache Spark (TM) SQL for Data Analysts
Apache Spark is one of the most widely used technologies in big data analytics. In this course, you will learn how to leverage your existing SQL skills to start working with Spark immediately. You will also learn how to work with Delta Lake, a highly performant, open-source storage layer that brings reliability to data lakes. By the end of this course, you will be able to use Spark SQL and Delta Lake to ingest, transform, and query data to extract valuable insights that can be shared with your team.
-
Course by
-
Self Paced
-
14 hours
-
English
Python and Machine-Learning for Asset Management with Alternative Data Sets
Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications.
-
Course by
-
Self Paced
-
21 hours
-
English
Machine Learning Using SAS Viya
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.
-
Course by
-
Self Paced
-
34 hours
-
English
Fundamentals of Software Architecture for Big Data
The course is intended for individuals looking to understand the basics of software engineering as they relate to building large software systems that leverage big data. You will be introduced to software engineering concepts necessary to build and scale large, data intensive, distributed systems.
-
Course by
-
Self Paced
-
43 hours
-
English
Security and Privacy for Big Data - Part 1
Welcome to our comprehensive course focused on security within Big Data environments. This course aims to provide you with a deep understanding of cryptographic principles and equip you with the tools necessary to manage access controls effectively within any Big Data system.
-
Course by
-
Self Paced
-
1 hour
-
English
Leading Change in Health Informatics
Do you dream of being a CMIO or a Senior Director of Clinical Informatics? If you are aiming to rise up in the ranks in your health system or looking to pivot your career in the direction of big data and health IT, this course is made for you. You'll hear from experts at Johns Hopkins about their experiences harnessing the power of big data in healthcare, improving EHR adoption, and separating out the hope vs hype when it comes to digital medicine.
-
Course by
-
Self Paced
-
15 hours
-
English
Tools for Data Science
In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio.
-
Course by
-
Self Paced
-
18 hours
-
English