دوراتنا

Forecasting Techniques for Slow and Rapidly Changing Demand

Master quantitative, judgmental and causal models used to forecast seasonal, intermittent, and new product future demand. Choose the right forecasting method for all kinds of demand patterns and sales data. Learn how to deal with randomness and low forecastability; missing data, outliers, and overfitting. Separate forecasting myths from reality and mitigate the risk of inaccurate forecasts.

Part of the ISCEA CFDP - Certified Forecaster and Demand Planner - Internationally Recognized Certificate.

• مقدم بواسطة
• الإنجليزية

Dealing With Missing Data

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

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• التعلم الذاتي
• 18 ساعات
• الإنجليزية

Handling Missing Values in R using tidyr

Missing data can be a “serious” headache for data analysts and scientists. This project-based course Handling Missing Values in R using tidyr is for people who are learning R and who seek useful ways for data cleaning and manipulation in R. In this project-based course, we will not only talk about missing values, but we will spend a great deal of our time here hands-on on how to handle missing value cases using the tidyr package.

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• التعلم الذاتي
• 3 ساعات
• الإنجليزية

Classification Trees in Python, From Start To Finish

In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser.

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• التعلم الذاتي
• 2 ساعات
• الإنجليزية

Go Beyond the Numbers: Translate Data into Insights

This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.

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• التعلم الذاتي
• 33 ساعات
• الإنجليزية

Build and deploy a stroke prediction model using R

In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes.

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• التعلم الذاتي
• 3 ساعات
• الإنجليزية

Predictive Modeling with Logistic Regression using SAS

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors.

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• التعلم الذاتي
• 17 ساعات
• الإنجليزية

Data Engineering in AWS

Data Engineering in AWS is the first course in the AWS Certified Machine Learning Specialty specialization. This course helps learners to analyze various data gathering techniques. They will also gain insight to handle missing data. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30-3:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.

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• التعلم الذاتي
• 4 ساعات
• الإنجليزية

Multilevel Modeling

In this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. In addition, participants will learn how to run basic two-level model in R. The objective of this course is to get participants acquainted with multilevel models. These models are often used for the analysis of ‘hierarchical’ data, in which observations are nested within higher level units (e.g. repeated measures nested within individuals, or pupils nested within schools).

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• التعلم الذاتي
• 8 ساعات
• الإنجليزية

AI Workflow: Data Analysis and Hypothesis Testing

This is the second course in the IBM AI Enterprise Workflow Certification 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.   In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA).  Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work.  You will learn techniques of estimation

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• التعلم الذاتي
• 11 ساعات
• الإنجليزية

Power and Sample Size for Multilevel and Longitudinal Study Designs

Power and Sample Size for Longitudinal and Multilevel Study Designs, a five-week, fully online course covers innovative, research-based power and sample size methods, and software for multilevel and longitudinal studies. The power and sample size methods and software taught in this course can be used for any health-related, or more generally, social science-related (e.g., educational research) application. All examples in the course videos are from real-world studies on behavioral and social science employing multilevel and longitudinal designs.

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• التعلم الذاتي
• 19 ساعات
• الإنجليزية

AI for Medical Prognosis

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks.

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• 30 ساعات
• 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.

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• التعلم الذاتي
• 7 ساعات
• الإنجليزية

Data Processing and Feature Engineering with MATLAB

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.

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• التعلم الذاتي
• 20 ساعات
• الإنجليزية

Machine Learning: Classification

Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

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• التعلم الذاتي
• 21 ساعات
• الإنجليزية

Data Management and Visualization

Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before.

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• التعلم الذاتي
• 12 ساعات
• الإنجليزية