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دوراتنا
The Improve Phase for the 6 σ Black Belt
This course is designed for professionals interested in learning the principles of Lean Sigma, the DMAIC process and DFSS. This course is number 6 of 8 in this specialization dealing with topics in the Improve Phase of Six Sigma Professionals with some completed coursework in statistics and a desire to drive continuous improvement within their organizations would find this course and the others in this specialization appealing. Method of assessment consists of several formative and summative quizzes and a multi-part peer reviewed project completion regiment.
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Course by
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Self Paced
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الإنجليزية
Probability and Statistics: To p or not to p?
We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events. To study, or not to study? To invest, or not to invest? To marry, or not to marry? While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise.
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Course by
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16 ساعات
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الإنجليزية
Explaining machine learning models
In this 2-hour long project-based course, you will learn how to understand the predictions of your model, feature relations, visualize and interpret feature & model relation with statistics and much more.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Introduction to Statistics in Python
In this project, learners will get a refresher of introductory statistics, learn about different python libraries that can be used to run statistical analysis, and create visualizations to represent the results. By the end of the project, the learners will import a real world data set, run statistical analysis to find means, medians , standard deviations, correlations, and other information of the data. The learners will also create distinct graphs and plots to represent the data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Data Analysis in R with RStudio & Tidyverse
Code and run your first R program in minutes without installing anything! This course is designed for learners with no prior coding experience, providing foundational knowledge of data analysis in R. The modules in this course cover descriptive statistics, importing and wrangling data, and using statistical tests to compare populations and describe relationships.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Analysis and Interpretation of Data
This course focuses on the analysis and interpretation of data. The focus will be placed on data preparation and description and quantitative and qualitative data analysis. The course commences with a discussion of data preparation, scale internal consistency, appropriate data analysis and the Pearson correlation. We will look at statistics that can be used to investigate relationships and discuss statistics for investigating relationships with a focus on multiple regression.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
The Analyze Phase for the 6 σ Black Belt
This course is designed for professionals interested in learning the principles of Lean Sigma, the DMAIC process and DFSS. This course is number 5 of 8 in this specialization dealing with topics in the Analyze Phase of Six Sigma Professionals with some completed coursework in statistics and a desire to drive continuous improvement within their organizations would find this course and the others in this specialization appealing. Method of assessment consists of several formative and summative quizzes and a multi-part peer reviewed project completion regiment.
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Course by
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Self Paced
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الإنجليزية
Statistical Analysis using Python Numpy
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
The T-Test is well known in the field of statistics. It is used to test a hypothesis using a set of data sampled from the population. To perform the T-Test, the population sample size, the mean, or average, of each population, and the standard deviation are all required. These will all be calculated in this project.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Introduction to EDA in R
Welcome to this project-based course Introduction to EDA in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions.
By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Statistics For Data Science
This is a hands-on project to give you an overview of how to use statistics in data science.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Reinforcement Learning for Trading Strategies
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
The Nature of Data and Relational Database Design
This course provides a comprehensive overview of data, various data types, design of databases for storage of data, and creation and manipulation of data in databases using SQL. By the end of this course, students will be able to describe what business intelligence is and how it’s different from business analytics and data science, conduct a basic descriptive statistical analysis and articulate the findings, and differentiate between types of statistics.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Relational Database Design
Have you ever wanted to build a database but don't know where to start? This course will provide you a step-by-step guidance. We are going to start from a raw idea to an implementable relational database. Getting on the path, practicing the real-life mini cases, you will be confident and comfortable with Relational Database Design. Let's get started! Relational Database Design can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform.
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Course by
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Self Paced
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71 ساعات
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الإنجليزية
Moneyball and Beyond
The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports, by showing that data analytics could be used to increase team winning percentage. This course shows how to program data using Python to test the claims that lie behind the Moneyball story, and to examine the evolution of Moneyball statistics since the book was published. The learner is led through the process of calculating baseball performance statistics from publicly available datasets.
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Course by
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Self Paced
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29 ساعات
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الإنجليزية
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Team Management for the 6 σ Black Belt
This course is designed for professionals interested in learning the principles of Lean Sigma, the DMAIC process and DFSS. This course is number 2 of 8 in this specialization dealing with topics in Team Management Professionals with some completed coursework in statistics and a desire to drive continuous improvement within their organizations would find this course and the others in this specialization appealing. Method of assessment consists of several formative and summative quizzes and a multi-part peer reviewed project completion regiment.
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Course by
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Self Paced
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الإنجليزية
Calculating Descriptive Statistics in R
Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
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
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Materials Data Sciences and Informatics
This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Tools for Exploratory Data Analysis in Business
This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA).
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Performing Confirmatory Data Analysis in R
Welcome to this project-based course Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
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.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Organization Planning and Development for the 6 σ Black Belt
This course is designed for professionals interested in learning the principles of Lean Sigma, the DMAIC process and DFSS. This course is number 1 of 8 in this specialization dealing with topics in Organization Planning and Development. Professionals with some completed coursework in statistics and a desire to drive continuous improvement within their organizations would find this course and the others in this specialization appealing. Method of assessment consists of several formative and summative quizzes and a multi-part peer reviewed project completion regiment.
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Course by
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Self Paced
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الإنجليزية
Probabilistic Deep Learning with TensorFlow 2
Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets.
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Course by
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Self Paced
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53 ساعات
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الإنجليزية