Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Expressway to Data Science: Essential Math
Data Science is growing rapidly, creating opportunities for careers across a variety of fields. This specialization is designed for learners embarking on careers in Data Science. Learners are provided with a concise overview of the foundational mathematics that are critical in Data Science. Topics include algebra, calculus, linear algebra, and some pertinent numerical analysis.
-
Course by
-
Self Paced
-
الإنجليزية
Gestión del análisis de datos
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed.
-
Course by
-
Self Paced
-
الإسبانية
Social Network Analysis
The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships within social networks, emphasizing the theoretical and practical applications of network analysis.
-
Course by
-
Self Paced
-
10 ساعات
-
الإنجليزية
Regression and Classification
Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! This course 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.
-
Course by
-
35 ساعات
-
الإنجليزية
Integral Calculus and Numerical Analysis for Data Science
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.
-
Course by
-
Self Paced
-
4 ساعات
-
الإنجليزية
Decision Tree Classifier for Beginners in R
Welcome to this project-based course Decision Tree Classifier for Beginners in R. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn how to build decision tree models using the tree and rpart libraries in R. We will start this hands-on project by importing the Sonar data into R and exploring the dataset. By the end of this 2-hour long project, you will understand the basic intuition behind the decision tree algorithm and how it works.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Essential Linear Algebra for Data Science
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use.
-
Course by
-
Self Paced
-
8 ساعات
-
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.
-
Course by
-
Self Paced
-
40 ساعات
-
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.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Algebra and Differential Calculus for Data Science
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Calculus concepts that you will need for a career in Data Science without a ton of unnecessary proofs and techniques that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Differential Calculus.
-
Course by
-
Self Paced
-
8 ساعات
-
الإنجليزية
Generalized Linear Models and Nonparametric Regression
In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools.
-
Course by
-
Self Paced
-
42 ساعات
-
الإنجليزية
Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
-
Course by
-
Self Paced
-
35
-
الإنجليزية
Modern Regression Analysis in R
This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison.
-
Course by
-
Self Paced
-
45 ساعات
-
الإنجليزية
SAS Statistical Business Analyst
This program is for those who want to enhance their predictive and statistical modeling skills to drive data-informed business outcomes. If modeling data for business outcomes is relevant in your job role or industry, this certificate is a valuable indication of your proficiency.
-
Course by
-
Self Paced
-
الإنجليزية
Data Science Fundamentals
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques.
-
Course by
-
Self Paced
-
الإنجليزية
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models.
-
Course by
-
Self Paced
-
الإنجليزية
Statistics with Python
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures.
-
Course by
-
Self Paced
-
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.
-
Course by
-
Self Paced
-
7 ساعات
-
الإنجليزية
Managing Data Analysis
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed.
-
Course by
-
Self Paced
-
9 ساعات
-
الإنجليزية
Bayesian Statistics: Techniques and Models
This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them.
-
Course by
-
Self Paced
-
30 ساعات
-
الإنجليزية
Statistical Inference
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.
-
Course by
-
54 ساعات
-
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.
-
Course by
-
Self Paced
-
15 ساعات
-
الإنجليزية