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دوراتنا
Introduction to Computational Statistics for Data Scientists
The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST).
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Course by
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Self Paced
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الإنجليزية
Introduction to Recommender Systems: Non-Personalized and Content-Based
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Data Understanding and Visualization
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication. Learning Objectives: 1.
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Course by
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25 ساعات
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الإنجليزية
Business Statistics and Analysis
The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. Informed by our world-class Data Science master's and PhD course material, you’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions.
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Course by
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Self Paced
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الإنجليزية
Machine Learning and Reinforcement Learning in Finance
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance. The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3
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Course by
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Self Paced
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الإنجليزية
Pre-MBA Statistics
Welcome to the Pre-MBA Statistics course! By the end of this course, you will be able to describe how statistics can be used to summarize, analyze, and interpret data. This course introduces you to some aspects of descriptive and inferential statistics. You will learn to distinguish between various data types and describe the operations that you can execute with each type of data and the right tools to use. The course also discusses the concepts of probability, which form the backbone of statistical analysis.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية
Unsupervised Text Classification for Marketing Analytics
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
TensorFlow 2 for Deep Learning
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning mode
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Course by
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Self Paced
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الإنجليزية
Statistical Learning for Data Science
Statistical Learning is a crucial specialization for those pursuing a career in data science or seeking to enhance their expertise in the field. This program builds upon your foundational knowledge of statistics and equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. Additionally, you will gain an in-depth understanding of coefficient estimation and interpretation, which will be valuable in explaining and justifying your models to clients and companies.
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Course by
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Self Paced
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الإنجليزية
Sports Performance Analytics
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball.
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الإنجليزية
Business Analytics
Nowadays data is essential in the business environment to be gain a global view of an organization's situation and to be able to make better decisions. Against this background, Big Data is a booming sector in multiple areas that opens up different job opportunities, such as the role of Data Analyst. This course is an introduction to data analytics from a business perspective, focusing on the relevance of basing strategic decision-making on the knowledge provided by data.
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Course by
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الإنجليزية
Genomic Data Science
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments.
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Course by
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Self Paced
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الإنجليزية
Applied Software Engineering Fundamentals
If you want to enter the exciting world of software development, this Software Engineering Foundations Specialization is for you. No prior degrees or knowledge of programming or application development are necessary. Software Developers are in great demand earning a median salary of US$110,140 per year according to the US Bureau of Labor and Statistics. The field is growing at a rate of 22% making it a great time to start a career in software engineering.
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Self Paced
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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.
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Self Paced
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الإنجليزية
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets.
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Self Paced
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الإنجليزية
Mindware: Critical Thinking for the Information Age
Most professions these days require more than general intelligence. They require in addition the ability to collect, analyze and think about data. Personal life is enriched when these same skills are applied to problems in everyday life involving judgment and choice. This course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python.
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Course by
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Self Paced
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الإنجليزية
Data Science: Statistics and Machine Learning
Build models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data.
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الإنجليزية
Fundamentals of Data Analysis
This course is the first of a series that aims to prepare you for a role working in data analytics. In this course, you’ll be introduced to many of the primary types of data analytics and core concepts. You’ll learn about the tools and skills required to conduct data analysis. We’ll go through some of the foundational math and statistics used in data analysis and workflows for conducting efficient and effective data analytics.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization 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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الإنجليزية
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data.
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Self Paced
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الإنجليزية
Machine Learning for Supply Chains
This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
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Course by
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Self Paced
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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.
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Course by
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Self Paced
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45 ساعات
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الإنجليزية
Practical Data Science with MATLAB
Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data? Completing this specialization will give you the skills and confidence you need to achieve practical results in Data Science quickly.
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Course by
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Self Paced
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الإنجليزية
Six Sigma Tools for Analyze
This course will cover the Measure phase and portions of the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process. You will learn about lean tools for process analysis, failure mode and effects analysis (FMEA), measurement system analysis (MSA) and gauge repeatability and reproducibility (GR&R), and you will be introduced to basic statistics. This course will outline useful measure and analysis phase tools and will give you an overview of statistics as they are related to the Six Sigma process.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية