Generative AI for Business Leaders
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دوراتنا
Math for MBA and GMAT Prep
This course gives participants a basic understanding of statistics as they apply in business situations. A fair share of students considering MBA programs come from backgrounds that do not include a large amount of training in mathematics and statistics. Often, students find themselves at a disadvantage when they apply for or enroll in MBA programs. This course will give you the tools to understand how these business statistics are calculated for navigating the built-in formulas that are included in Excel, but also how to apply these formulas in an range of business settings and situations.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Creating Features for Time Series Data
This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis. In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Remote Sensing Image Acquisition, Analysis and Applications
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Probability & Statistics for Machine Learning & Data Science
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.
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Course by
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Self Paced
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29 ساعات
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الإنجليزية
Managing, Describing, and Analyzing Data
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
NumPy Tutorial
Start learning NumPy with the w3schools course and improve your statistics and math programming skills. NumPy is a Python library for using arrays in statistics and math. This is a structured and interactive version of the W3Schools NumPy Tutorial. The course is self-paced with text based modules, practical interactive examples and exercises to check your understanding as you progress. W3Schools is the world's largest web developer learning site. Start learning with our proven tutorials used by millions of learners!
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Building Statistical Models in R: Linear Regression
Welcome to this project-based course Building Statistical Models in R: Linear Regression. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn the basics of building statistical models in R. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour long project, you will understand how to build and interpret the result of simple linear regression models 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
Trees, SVM and Unsupervised Learning
"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of each. The course will also cover how and when to apply them to different scenarios, including binary classification and K > 2 classes.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Algebra: Elementary to Advanced - Polynomials and Roots
This course is the final course in a three part algebra sequence, In this course, students extend their knowledge of more advanced functions, and apply and model them using both algebraic and geometric techniques. This course enables students to make logical deductions and arrive at reasonable conclusions. Such skills are crucial in today's world. Knowing how to analyze quantitative information for the purpose of making decisions, judgments, and predictions is essential for understanding many important social and political issues.
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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 Bayesian Statistics
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Algebra: Elementary to Advanced - Equations & Inequalities
This course is intended for students looking to create a solid algebraic foundation of fundamental mathematical concepts from which to take more advanced courses that use concepts from precalculus, calculus, probability, and statistics. This course will help solidify your computational methods, review algebraic formulas and properties, and apply these concepts model real world situations. This course is for any student who will use algebraic skills in future mathematics courses.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Hypothesis Testing in Public Health
Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you'll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you'll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Introduction to Business Analysis Using Spreadsheets: Basics
In this 1-hour 30-mins long project-based course, you will learn the responsibilities of a Business Analyst such as Learn the basic concepts of data analysis and descriptive statistics. Learn how to manipulate, analyze, and visualize data in Google Sheets using functions, aggregation functions, and logical aggregation functions.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Research Design: Inquiry and Discovery
The main purpose of this course is to focus on good questions and how to answer them. This is essential to making considered decisions as a leader in any organization or in your life overall. Topics will include the basis of human curiosity, development of questions, connections between questions and approaches to information gathering design , variable measurement, sampling, the differences between experimental and non-experimental designs, data analysis, reporting and the ethics of inquiry projects.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Data Analysis in Python with pandas & matplotlib in Spyder
Code and run your first Python script in minutes without installing anything! This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
The Measure 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 4 of 8 in this specialization dealing with topics in the Measure 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
Create a Custom Marketing Analytics Dashboard in Data Studio
Google Data Studio is a powerful tool that turns data into reports that easy decision-making tools that lead to better business outcomes. Google Data Studio is easy to use, free and works seamlessly with dozens of applications within and outside of the Google Marketing Suite. You can use Google Studio to connect and integrate data from 100’s of applications including Facebook, Constant Contact, Google Ads, and more.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Descriptive and Inferential Statistics in R
In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
ANOVA and Experimental Design
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality.
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Course by
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Self Paced
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40 ساعات
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الإنجليزية
Research Instruments and Research Hypotheses
This course concentrates on the design and development of different research instruments. In this vein, the focus will be placed on the development of an instrument design strategy, scales of measurement and the components of the research report. The course begins by looking at the questionnaire development process with a focus on questionnaire design, question type and wording, pretesting and revising. We will consider the identification of scales of measurement and operationalisation, and the design of an online questionnaire.
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Course by
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Self Paced
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20 ساعات
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الإنجليزية
Project Scheduling: Estimate Activity Durations
In this guided project you will learn how estimate the durations of your project activities. You will estimate the duration of your project activities, using techniques such as Expert Judgment, Analogy, and Parametric Estimation. Once you have developed a WBS (see Guided Project: Creating a WBS, for how to create one), and decompose the work packages (lowest elements in the WBS) into the necessary activities (See Guided Project: Creating a Project Network Diagram), you can use the bottom up estimation technique.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Hypotheses Testing in Econometrics
In this course, you will learn why it is rational to use the parameters recovered under the Classical Linear Regression Model for hypothesis testing in uncertain contexts. You will: – Develop your knowledge of the statistical properties of the OLS estimator as you see whether key assumptions work. – Learn that the OLS estimator has some desirable statistical properties, which are the basis of an approach for hypothesis testing to aid rational decision making.
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Course by
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Self Paced
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27 ساعات
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الإنجليزية
Text file Input/Output in Java
By the end of this project, you will learn to use text files for input/output in Java. You will also learn to read and write structured data to and from a text file. Finally, we will create a student scorecard and learn to do some basic statistics on data present in a text file. Text File Input/Output is necessary to store data on the hard-disk in order to keep it even after your program execution ends or the computer switches off.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
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:
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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 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.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية