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دوراتنا
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Successful Presentation
Few kinds of communication can have the effect of a powerful presentation. Even a short speech can motivate people to change long-held beliefs or to take action, and a wonderfully delivered speech can transform a normal person into a leader. In this course, Prof. William Kuskin provides a series of pragmatic videos and exercises for successful public speaking and presentations.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية
Statistics for International Business
This course introduces core areas of statistics that will be useful in business and for several MBA modules. It covers a variety of ways to present data, probability, and statistical estimation. You can test your understanding as you progress, while more advanced content is available if you want to push yourself. This course forms part of a specialisation from the University of London designed to help you develop and build the essential business, academic, and cultural skills necessary to succeed in international business, or in further study.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Data Analysis and Representation, Selection and Iteration
This course is the second course in the specialization exploring both computational thinking and beginning C programming. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components. Most people have a better understanding of what beginning C programming means! This course assumes you have the prerequisite knowledge from the previous course in the specialization. You should make sure you have that knowledge, either by taking that previous course or from personal experience, before tackling this course.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
The Power of Statistics
This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.
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Course by
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Self Paced
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37 ساعات
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الإنجليزية
The Define 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 3 of 8 in this specialization dealing with topics in the Define 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
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Global Statistics - Composite Indices for International Comparisons
The number of composite indices that are constructed and used internationally is growing very fast; but whilst the complexity of quantitative techniques has increased dramatically, the education and training in this area has been dragging and lagging behind. As a consequence, these simple numbers, expected to synthesize quite complex issues, are often presented to the public and used in the political debate without proper emphasis on their intrinsic limitations and correct interpretations.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Get Familiar with ML basics in a Kaggle Competition
In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA).
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Social Science Approaches to the Study of Chinese Society Part 1
This course seeks to turn learners into informed consumers of social science research. It introduces concepts, standards, and principles of social science research to the interested non-expert. Learners who complete the course will be able to assess evidence and critically evaluate claims about important social phenomena. It reviews the origins and development of social science, describes the process of discovery in contemporary social science research, and explains how contemporary social science differs from apparently related fields.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Resampling, Selection and Splines
"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Specialized Models: Time Series and Survival Analysis
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Logistic Regression in R for Public Health
Welcome to Logistic Regression in R for Public Health! Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Pivot Tables in Google Sheets
This is a self-paced lab that takes place in the Google Cloud console. Create pivot tables to quickly summarize large amounts of data and reference data using named ranges. Use functions and formulas to calculate descriptive statistics.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Introduction to Predictive Modeling
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Advanced Linear Models for Data Science 1: Least Squares
Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
DFSS 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 8 of 8 in this specialization dealing with topics in Design for 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
Understanding Clinical Research: Behind the Statistics
If you’ve ever skipped over the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.
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Course by
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Self Paced
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27 ساعات
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الإنجليزية
RStudio for Six Sigma - Basic Descriptive Statistics
Welcome to RStudio for Six Sigma - Basic Description Statistics. This is a project-based course which should take approximately 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will learn to perform Basic Descriptive Analysis (Six Sigma) tasks hands-on using RStudio. Both R language and RStudio tools are Open Source and can be used for most Six Sigma analysis tasks without needing commercial software.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Writing and Editing: Revising
This fourth and final course in the “Good with Words: Writing and Editing” series will help you master perhaps the most important step in the writing process: revising. You’ll learn about the difference between editing and proofreading. You’ll practice “un-numbing the numbers” so that data and statistics you use are clear and compelling.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Take a Swing at Baseball Analytics: Explore Player Careers
Former Major League Baseball (MLB) player Matt Kata joins MathWorks to introduce you to data analysis using baseball statistics. By analyzing historic batting statistics, you will explore player careers and answer the question: When do great hitters peak in their career?
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Advanced Topics and Future Trends in Database Technologies
This course consists of four modules covering some of the more in-depth and advanced areas of database technologies, followed by a look at the future of database software and where the industry is heading. Advanced Topics and Future Trends in Database Technologies 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. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Build a Python GUI with Tkinter
A graphical user interface can be a nice alternative to using the command line for running programs, as there is no need to memorize how to execute a command with arguments. A label may be added to describe what is needed for the application, for example. There are many choices for building a graphical user interface in Python. Some of them require licensing for commercial use and each have their own sets of learning curves. Using Tkinter avoids the licensing issues and is quite simple to use as well.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية