Generative AI for Business Leaders
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How Google does Machine Learning
دوراتنا
Data Visualization and Dashboards with Excel and Cognos
Learn how to create data visualizations and dashboards using spreadsheets and analytics tools. This course covers some of the first steps for telling a compelling story with your data using various types of charts and graphs. You'll learn the basics of visualizing data with Excel and IBM Cognos Analytics without having to write any code. You'll start by creating simple charts in Excel such as line, pie and bar charts. You will then create more advanced visualizations with Treemaps, Scatter Charts, Histograms, Filled Map Charts, and Sparklines.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية
Big Data Emerging Technologies
Every time you use Google to search something, every time you use Facebook, Twitter, Instagram or any other SNS (Social Network Service), and every time you buy from a recommended list of products on Amazon.com you are using a big data system. In addition, big data technology supports your smartphone, smartwatch, Alexa, Siri, and automobile (if it is a newer model) every day. The top companies in the world are currently using big data technology, and every company is in need of advanced big data technology support.
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Course by
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Self Paced
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30 ساعات
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الإنجليزية
Data Science in Real Life
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Getting and Cleaning Data
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Exploratory Data Analysis with MATLAB
In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to use MATLAB to automatically generate code so you can learn syntax as you explore.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Data Processing and Feature Engineering with MATLAB
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Six Sigma Advanced Define and Measure Phases
This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Marketing Analytics Foundation
This course lays the foundation of marketing analytics.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Bioinformatic Methods II
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Process Mining: Data science in Action
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Foundations of Data Science: K-Means Clustering in Python
Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses.
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Course by
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Self Paced
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29 ساعات
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الإنجليزية
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. Some exercises require the use of R, a freely-available statistical software package.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Bioinformatic Methods I
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
R Programming
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
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Course by
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Self Paced
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57 ساعات
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الإنجليزية
Data Visualization in Excel
In an age now driven by "big data", we need to cut through the noise and present key information in a way that can be quickly consumed and acted upon making data visualization an increasingly important skill. Visualizations need to not only present data in an easy to understand and attractive way, but they must also provide context for the data, tell a story, achieving that fine balance between form and function. Excel has many rivals in this space, but it is still an excellent choice, particularly if it's where your data resides.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Data Analysis with R Programming
This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways.
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Course by
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Self Paced
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37 ساعات
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الإنجليزية
Launching into Machine Learning
The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Python Data Analysis
This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Introduction to Statistics & Data Analysis in Public Health
Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Foundations of strategic business analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Excel Fundamentals for Data Analysis
As data becomes the modern currency, so the ability to analyse the data quickly and accurately has become of paramount importance. Excel with its extraordinarily broad range of features and capabilities is one of the most widely used programs for doing this. In the first course of our Excel Skills for Data Analysis and Visualization Specialization, you will learn the fundamentals of Excel for data analysis.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
Introduction to Probability and Data with R
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية