edx MOOC-Supply Chain Analytics; MITx

edx MOOC-Supply Chain Analytics; MITx

Master and apply the core methodologies used in supply chain analysis and modeling, including statistics, regression, optimization and probability ​– part of the MITx Supply Chain Management MicroMasters Credential.

Supply Chain Analytics

About this course

Supply chains are complex systems involving multiple businesses and organizations with different goals and objectives. Many different analytical methods and techniques are used by researchers and practitioners alike to better design and manage their supply chains. This business and management course introduces the primary methods and tools that you will encounter in your study and practice of supply chains. We focus on the application of these methods, not necessarily the theoretical underpinnings.

We will begin with an overview of introductory probability and decision analysis to ensure that students understand how uncertainty can be modeled. Next, we will move into basic statistics and regression. Finally, we will introduce optimization modeling from unconstrained to linear, non-linear, and mixed integer linear programming.

This is a hands-on course. Students will use spreadsheets extensively to apply these techniques and approaches in case studies drawn from actual supply chains.

What you’ll learn

  • Basic analytical methods
  • How to apply basic probability models
  • Statistics in supply chains
  • Formulating and solving optimization models

Syllabus

WEEK 1: INTRODUCTION TO SUPPLY CHAINS AND BASIC ANALYSIS

WEEK 2: PRESCRIPTIVE MODELING I: CONSTRAINED AND UNCONSTRAINED OPTIMIZATION

WEEK 3: PRESCRIPTIVE MODELING II: IPS, MILPS, AND NETWORK MODELS

WEEK 4: ALGORITHMS AND APPROXIMATIONS

WEEK 5: PREP-WEEK

WEEK 6: MIDTERM EXAM

WEEK 7: MANAGING UNCERTAINTY: DISTRIBUTIONS AND PROBABILITY

WEEK 8: STATISTICAL TESTING

WEEK 9: REGRESSION AND SIMULATION MODELS

WEEK 10: QUEUEING THEORY AND DISCRETE EVENT SIMULATION

WEEK 11: PREP-WEEK

WEEK 12: FINAL EXAM

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Đăng ký (free): link

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DataCamp Open Course-Hướng dẫn cơ bản về R; Hoang Duc Anh

DataCamp Open Course-Hướng dẫn cơ bản về R; Hoang Duc Anh

Trong loạt bài giảng này, bạn sẽ học được cách làm chủ những kiến thức cơ bản của ngôn ngữ lập trình thông kê R, bao gồm factors, list và data frame. Những kiến thức này sẽ giúp bạn bắt đầu công việc của một nhà phân tích dữ liệu. Với hơn 2 triệu người sử dụng R trên toàn thế giới, R đang nhanh chóng trở thành ngôn ngữ lập trình số một trong giới thống kê và khoa học số liệu. Hàng năm, số lượng người dùng R tăng hơn 40% và ngày càng có nhiều cơ quan và tổ chức sử dụng R trong hoạt động phân tích thường nhật. Hãy bắt đầu khám phá sức mạnh và học cách sử dụng R ngay từ ngày hôm nay.

CHƯƠNG TRÌNH HỌC

Giới thiệu căn bản về R

Trong chương này, chúng ta sẽ bắt đầu tìm hiểu về R. Bạn sẽ được học cách sử dụng màn hình tương tác (console) để tính toán và gán biến. Thêm vào đó, bạn sẽ làm quen với các loại dữ liệu cơ bản trong R. Ta cùng bắt đầu nào!

Véc-tơ

Trong chương tiếp theo trong khóa học này, chúng ta sẽ cùng đến thăm Vegas, tại đây bạn sẽ được học cách sử dụng vec-tơ trong R để phân tích kết quả bài bạc của bản thân! Sau khi hoàn thành chương này, bạn sẽ học được cách khởi tạo, đặt tên, lọc các yếu tố và so sánh các véc-tơ trong R.

Ma trận

Trong chương này, bạn sẽ được học cách sử dụng ma trận trong R. Sau khi học xong, bạn sẽ thành thạo trong việc thiết lập ma trận và biết cách thực hiện những tính toán cơ bản trong ma trận. Để minh họa cho những điều trên, bạn sẽ phân tích doanh thu bán vé của Star Wars. Chúc bạn may mắn!

Factors

Trong rất nhiều trường hợp, dữ liệu có giá trị nằm trong một nhóm hữu hạn các giá trị cho trước. Ví dụ, giới tính có giá trị nam hoặc nữ. Trong R, các biến có loại này được gọi là factor. Các biến factor đóng vai trò rất quan trọng trong quá trình phân tích dữ liệu. Do đó, chúng ta sẽ cùng học cách tạo và xử lý các biến factor trong bài giảng dưới đây.

Data frames

Phần lớn dữ liệu sử dụng để phân tích được lưu dưới dạng data frame. Đến cuối chương này, bạn sẽ có thể tạo được data frame, lựa chọn data frame và sắp xếp thứ tự của data frame theo biến xác định.

Lists

Lists, không giống như véc-tơ, có thể lưu trữ các kiểu dữ liệu khác nhau. Trong chương này, chúng ta sẽ học cách khởi tạo, đặt tên và lọc các thành phần trong list.

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Đăng kí (free): link


Ghi chú.

Ưu điểm của khóa học (miễn phí) này:

  • Được hướng dẫn bằng tiếng Việt.
  • Hình dung được cách học & lập trình trên nền tảng “đám mây” (cloud computing), không cần cài đặt R.

Nhược điểm:

  • Hướng dẫn bằng tiếng Việt.
  • Học trên nền tảng “đám mây” (cloud computing).

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edx MOOC-Calculus Applied; HarvardX

edx MOOC-Calculus Applied; HarvardX

Apply tools of single-variable calculus to create and analyze mathematical models used by real practitioners in social, life, and physical sciences.

Syllabus

  • Introduction Section (Section 0)
  • Section 1: What Makes a Good Test Question? Mathematical Models to Measure Knowledge and Improve Learning
  • Section 2: Economic Applications of Calculus: Price and Demand in a Tale of Two Cities
  • Section 3: From X-Rays to CT-Scans: Mathematics and Medical Imaging
  • Section 4: What is Middle Income? Thinking about Income Distributions with Statistics and Calculus
  • Section: 5 Population Dynamics Part I: the Evolution of Population Models
  • Section 6: Population Dynamics II: A Biological Puzzle OR How Fishing Affects a Predator-Prey System
  • Section 7: Extinction, Chaos and other Bifurcation Behavior
  • Section 8: Outbreak! Budworm Populations and Bifurcations
  • Section 9: Species in Competition: Coexistence or Exclusion

Đăng kí (free): link

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DataCamp Course-Quantitative Risk Management in R; Alexander J. McNeil

DataCamp Course-Quantitative Risk Management in R; Alexander J. McNeil

Quantitative Risk Management in R

Course Description

In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called “stylized facts” of these data – including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

CHAPTERS:

  1. Exploring market risk-factor data

  2. Real world returns are riskier than normal

  3. Real world returns are volatile and correlated

  4. Estimating portfolio value-at-risk (VaR):

  • Value-at-risk and expected shortfall
  • Computing VaR and ES for normal distribution
  • International equity portfolio
  • Examining risk factors for international equity portfolio
  • Historical simulation
  • Estimating VaR and ES
  • Option portfolio and Black Scholes
  • Compute Black-Scholes price of an option
  • Equity and implied volatility risk factors
  • Historical simulation for the option example
  • Historical simulation of losses for option portfolio
  • Estimating VaR and ES for option portfolio
  • Computing VaR for weekly losses
  • Wrap-up

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Đăng ký (free): link

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FutureLearn MOOC-Big Data: Mathematical Modelling; Queensland University of Technology

FutureLearn MOOC-Big Data: Mathematical Modelling; Queensland University of Technology

Learn how to apply selected mathematical modelling methods to analyse big data in this free online course.

Learn how mathematics underpins big data analysis and develop your skills.

Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets. We begin by explaining how maths underpins many of the tools that are used to manage and analyse big data. We show how very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools. We then introduce three such tools, based on a linear algebra framework: eigenvalues and eigenvectors for ranking; graph Laplacian for clustering; and singular value decomposition for data compression.

What topics will you cover?

  • Introduction to key mathematical concepts in big data analytics: eigenvalues and eigenvectors, principal component analysis (PCA), the graph Laplacian, and singular value decomposition (SVD)
  • Application of eigenvalues and eigenvectors to investigate prototypical problems of ranking big data
  • Application of the graph Laplacian to investigate prototypical problems of clustering big data
  • Application of PCA and SVD to investigate prototypical problems of big data compression

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Đăng kí (free): link

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Coursera MOOC-Machine Learning Foundations: A Case Study Approach; University of Washington [with Python]

Coursera MOOC-Machine Learning Foundations: A Case Study Approach; University of Washington

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About this course: Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

Learning Outcomes: By the end of this course, you will be able to:

  • -Identify potential applications of machine learning in practice.
  • -Describe the core differences in analyses enabled by regression, classification, and clustering.
  • -Select the appropriate machine learning task for a potential application.
  • -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
  • -Represent your data as features to serve as input to machine learning models.
  • -Assess the model quality in terms of relevant error metrics for each task.
  • -Utilize a dataset to fit a model to analyze new data.
  • -Build an end-to-end application that uses machine learning at its core.
  • -Implement these techniques in Python.

Syllabus

WEEK 1. Welcome
WEEK 2. Regression: Predicting House Prices (Linear Regression)
WEEK 3. Classification: Analyzing Sentiment (Logistic Regression)
WEEK 4. Clustering and Similarity: Retrieving Documents (k-means, Nearest Neighbors)
WEEK 5. Recommending Products (Matrix factorization)
WEEK 6. Deep Learning: Searching for Images (Neural network, Nearest Neighbors)

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Đăng kí (free): link

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Coursera MOOC-Practical Machine Learning; Johns Hopkins University [with R]

Coursera MOOC-Practical Machine Learning; Johns Hopkins University

Johns Hopkins University

About this course: One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Syllabus

Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.

Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.

Week 3: Predicting with trees, Random Forests, & Model Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.

Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.

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Đăng kí (free): link

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