Fundamentals of Quantitative Modeling

Course Link: https://www.coursera.org/learn/wharton-quantitative-modeling

Fundamentals of Quantitative Modeling in Finance
Focusing on the most important general concepts in model-building, we will learn model building techniques that are practical for many-worlds modeling. The course aims to provide you with an intuitive understanding of model building and the general principles of modeling that are applicable to many disciplines.

Upon successful completion of this course, you will be able to:
• Describe the general structure of a model and the key principles of modeling
• Identify the major phases of a model
• Construct a basic model of a financial market
• Construct a simplified version of a financial model
• Construct a more complex model of a financial market

This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.Module 1: Modeling the Model
Module 2: Modeling the Model with a Complexity Component
Module 3: Modeling the Model with a Strength Component
Module 4: Modeling the Model with a Price Component
Fundamentals of Psychology
What is learning psychology? Why do we need to learn it? What does it teach us about ourselves and others? Why do some people have more success with others than with ourselves? What does it tell us about ourselves and others?

This course explains the nature of human learning and behavior, the structure and components of memory, learning, motivation, fear, pleasure, frustration, fear of abandonment, and social learning. Our understanding of learning has been enhanced by the development of evidence-based models and techniques. The course also introduces the critical concept of individual difference, and shows how individuals can overcome differences to achieve their goals.

Learning about learning, memory, motivation, and learning styles is important for all students, but especially for those who are struggling with mental illness, substance use, or any mental illness. Anyone can benefit from learning about learning, and anyone can benefit from learning about learning. This course is the first in a series on the subjects of learning, memory, motivation, and learning styles, and in this course, we will cover the individual differences that are characteristic of each of these characteristics.

Those who complete the course will understand how the individual differences are caused by specific characteristics of the environment, such as the way that we were raised, our environment, and the way that we interact with the environment. We will also look at individual differences that are characteristic of each of the four individual characteristics of learning: cognitive, linguistic, affective, and creative. We will also look at individual differences that are characteristic of each of the four individual characteristics of motivation: hard, soft, reasoned, and creative. We will also look at individual differences that

Course Link: https://www.coursera.org/learn/wharton-quantitative-modeling

Econometrics: Methods and Applications

Course Link: https://www.coursera.org/learn/erasmus-econometrics

Econometrics: Methods and Applications
We consider the most important questions in variance estimation:

• What are the appropriate analytical methods for determining variables in a sample?
• How can I interpret the difference between my observations and expected values in a sample?
• How can I know that the sample is representative of the population?
• How can I tell that the sample variance is not due to random variation?

Econometrics is the study of variance, and the study of measurement. We consider the question of how data collected using appropriate statistical methods can give rise to valid empirical estimates. In particular, we focus on the concepts of . . .

Measurement is the study of the measurement of variance, and the study of variance estimation. We consider the question of how data collected using appropriate statistical methods can give rise to valid empirical estimates. In particular, we focus on the concepts of variance, normal distribution, normal distribution with random variance, and normal distribution with . . .

Our main interest lies in the application of these concepts to the actual empirical measurement of variance, and the study of variance estimation in the real world. We consider the following questions:

• How can I know that my observations are valid?
• How can I tell that the sample variance is not due to random variation?
• How can I tell that my observations are representative of the population?
• How can I tell that my observations are valid?
• How can I tell that my observations are valid?

Measurement is the study of variance, and the study of variance estimation. We consider the question of how data collected using appropriate statistical methods can give rise to valid empirical estimates. In particular, we focus on the concepts of variance, normal distribution, normal distribution with random variance, and normal distribution with . . .

Our main interest lies in the application of these concepts to the actual empirical measurement of variance, and the study of variance estimation in the real world. We consider the following questions:

• How can I know that my observations are valid?
• How can I tell that the sample variance is not due to random variation?
• How can I tell that my observations are valid?
• How can I tell that my observations are valid?

Measurement is the study of variance, and the study of variance estimation. We consider the question of how data collected using appropriate statistical methods can give rise to valid empirical estimates. In particular, we focus on the concepts of variance, normal distribution, normal distribution with random variance, and normal distribution with . . .

Our main interest lies in the application of these concepts to the actual empirical measurement of variance, and the study of variance estimation in the real world. We consider the following questions:

• How can I know that my observations are valid?
• How can I tell that the sample variance is not due to random variation

Course Link: https://www.coursera.org/learn/erasmus-econometrics

Accounting Data Analytics with Python

Course Link: https://www.coursera.org/learn/accounting-data-analytics-python

Accounting Data Analytics with Python
This course is all about data analytics in the financial and business context. We will learn about the foundations of data analytics and how to construct analytic projects. We will start with modeling social and individual behaviors and then dive into the different data analytics techniques available. We will learn about the different ways to extract meaningful insights from data and the techniques that are used to translate those insights into insights that can be used to make decisions. We will learn about how data is transformed when it is analyzed, and how to extract relevant information from data that can help you make decisions. We will also learn the basics of how analytics is integrated with business processes so that you can leverage insights from multiple disciplines at once.

At the end of this course, you will be able to:
– construct models of data using Big Data and derive insights from them
– construct a data pipeline from a data lake
– construct a pipeline that includes multiple-track processing
– utilize multiple-track processing for data mining and mining of unstructured data
– utilize multiple-track processing for data mining and mining of unstructured data
– construct a data pipeline from a data lake

This course is all available on Coursera.Getting Started
Modeling
Data Algorithms
Analyzing Unstructured Data
Accounting: Principles of Financial Accounting
Financial Accounting is the area of accounting that focuses on the most important elements of revenue recognition. This is the area of accounting that students should consider taking an introductory accounting course.

This introductory course in Financial Accounting is designed to help you understand the most important elements of revenue recognition, including financial statements, income statements, balance sheets, and statements of cash flows. You will begin your journey in a series of videos that address topics such as asset valuation, liabilities, and contingencies. From there, you will be guided through a series of short reading exercises, followed by a larger-scale project assignment. You will work with a series of videos that illustrate the concepts and techniques learned in the course. In addition, you will listen to the course overview and take the final project assignment. You will have access to all of the information and resources that are necessary to complete the project. You will also have access to the project management tools and a variety of project management training options that will help you complete the project in a timely and efficient manner.

Upon completing this course, you will be able to:
1. Describe the main financial statements for a project
2. Determine the financial position of a project
3. Cash flow information for a project
4. Summarize the main techniques used to determine the financial position of a project
5. Understand various project management topics
6. Apply the concepts of project risk and project schedule to evaluate projectsWeek 1: Introduction to Financial Statements
Week 2: Project Income Statement
Week 3:

Course Link: https://www.coursera.org/learn/accounting-data-analytics-python

Statistics with R Specialization

Course Link: https://www.coursera.org/specializations/statistics

Statistics with R Specialization
In this course you will explore the statistics that are just beginning to show up in R. We will introduce the reader to statistical concepts such as mean values, median values, standard deviations, and variance. We will then dive into the concepts of linear regression and inverse probability of means. We will also cover the concepts of normal distribution and the vector calculus of normal distributions. We will continue with logistic regression and the statistic of choice, the standard deviation. We will then introduce the linearization of variables and the likelihood that the null hypothesis is true. We will then discuss the sample size and confidence interval. We will then introduce the normal distribution and the vector calculus of normal distributions. We will then dive into linear regression and the vector calculus of linear regression. We will then dive into the normal distribution and the vector calculus of linear regression. We will then dive into the normal distribution and the vector calculus of linear regression. We will then dive into the normal distribution and the vector calculus of linear regression. We will then dive into the normal distribution and the vector calculus of linear regression. We will then dive into the normal distribution and the vector calculus of linear regression. You will need to know the concepts introduced in this course before taking any calculus.Statistical concepts
Normal distribution
Linear regression
Vectors and normal distribution
Synthesizing Data in R
Synthesizing data in R is easy: just copy and paste the contents of your favorite spreadsheet into the URL bar on RStudio. In this course we will learn how to use the free software package reStructuredText to preprocess text and use it to make a powerful machine-readable data management system. You will first learn how to use the freely available text in HTML format, a powerful text editor which you will also use to create your own templates. We will introduce the basics: how to use data sources such as Web pages, blogs, and documents; our working examples include working with Excel spreadsheets and quickly running some basic statistical analyses. We will then teach you how to use a text editor to create your own templates and then how to manage your data. We will then walk you through the basics of data analysis: how to identify data, prepare data for analysis, interpret data, and report data. We will then introduce the basic concepts in statistics: how to rank groups of data; how to compute mean and median values; and how to compute values from a group of data. We will then teach you how to utilize a spreadsheet to organize your data by using data sources. Finally, we will demonstrate how to perform basic statistical analyses of your data. You will use Excel for plotting, matrices, and working with data. You will also learn how to use the powerful R package to actually run your code. In this course, we assume that you have a basic understanding of Excel, that you have a working knowledge of R and basic math skills, and that you are comfortable with basic programming.Week 1: Read and write data in HTML
Week 2: Preprocess and organize your data in tables
Week 3: Display your data in a table using a grid
Week 4: Compute values from a group of data
Symmetric cryptography: Hash based
This course focuses on practical methods to implement symmetric cryptographic methods in C. We will learn what are known as hash functions, and how to use them to protect information. We will also learn how to perform operations on these algorithms and how to protect data by using a hash function.

This course is the second in a sequence that teaches how to use algorithms to implement symmetric cryptographic methods in C and assembly, and how to write a C program that uses these methods. A standalone course can be obtained for free from the C library http://libstdc.lib.c.rust-lang.org/.

The course assumes that you already have basic knowledge of computer science, is comfortable with basic math and programming, and is able to use common programming tools. It is also possible that you will be able to use some of the extensions and functions that are available in this course, but that this course is focussed on how to use C for cryptographic purposes.

Note that for the course syllabus, course outline and project requirements you will need to use the official R package: https://packages.r-project.org/?p=313

For the third and fourth course in this sequence, we will focus on the use of OpenPGP and RSA through OpenPGP-AES, which is a free and open source software package manager. If you do not have access to a computer, you can obtain the software from http://www.openspgp.org/. For the fifth and final course, we will focus on an actual implementation of one of the symmetric algorithms in the format of the hash table:

Course Link: https://www.coursera.org/specializations/statistics

Statistical Analysis with R for Public Health Specialization

Course Link: https://www.coursera.org/specializations/statistical-analysis-r-public-health

Statistical Analysis with R for Public Health Specialization
This course introduces statistical concepts for use in public health research, including exploratory data analysis, hypothesis testing, and confidence intervals. Weekly lecture videos will introduce statistical concepts and help learners to apply these concepts to real data sets. An emphasis is placed on statistical inference and inference of causal effects. A strong emphasis is placed on the use of continuous data in statistical analysis.

The purpose of this course is to acquaint the learner with the basic concepts and principles of statistics, including the use of inferential statistics. The course will introduce statistical models and inference techniques, and will help to familiarize the learner with the basic statistical concepts. The course will also introduce random-effects models and random-effects ANOVA. The course will also introduce the use of Cox regression and two-group ANOVA, as well as random-effects modeling of covariance and correlation. It will also introduce the SE method, which is used for continuous data analysis. The course will also introduce the use of random-effects modeling and random-effects ANOVA, as well as random-effects modeling of covariance and correlation. It will also introduce the SE method, which is used for continuous data analysis.

The course uses RStudio as its statistical software and as an IDE. The course is designed to run on Linux or Mac OS X. The course uses an open-source software package repository (https://github.com/statistics-in-r/rstudio) for each dataset, and each package contains a set of pre-built R packages that you can use directly. There are also included open-source statistical software packages (RStudio, R, and RStudio Extension), which you can install using the Software Configuration Utility (SCU) included with the course.Introduction to Stats and Networking
Covariance
Cox Regression
ANOVA
Statistical Model Checking in Practice
This course covers model-checking techniques for statistical inference of non-negative values. We will learn what type of model to use for our data and its goodness-of-fit. We will use statistical inference methods that are appropriate for our data set and some statistical modeling that we may use. We will also introduce a set of techniques for performing robust statistical modeling and inference that are not normally performed using machine learning methods. The course also covers model selection and optimization. We will learn how to implement robust statistical modeling and inference by using multiple data sets and different optimization methods.Intro to Model Checking
Non-negative values and model selection
Probability and conditional independence
Inference for Continuous Data
Statistical Model Checking for Machine Learning
This course covers model checking techniques for machine learning. We will learn the basics of how to evaluate non-negative numbers and how to construct confidence intervals. We will learn how to construct confidence intervals using the formatter and the construct vector calculus and the corresponding non-linear regression model. We will also explain the concept of a normal distribution and how to compute average values. We will also explain the concept of normal distribution over large numbers of variables. We will use machine learning techniques for evaluating large datasets, which will be covered in depth in later courses on supervised learning and reinforcement learning. We will also explain the concepts of a normal distribution over large numbers of variables and the concept of average over large numbers of variables. We will also introduce the tensor product and average over vectors using TensorFlow and explain how to perform average over large numbers of variables. We will then apply these concepts to a case study in machine learning used in the machine learning course of the specialization. We will see how the machine learning technique can be used to detect misclassified data in large datasets. We will see how the machine learning technique can be used to detect training errors even for datasets that have been cleaned and optimized. We will see how the machine learning technique can be used to detect errors even for datasets that have been cleaned and optimized. We will see how classification problems can be addressed with machine learning. We will see how to use the classification models that are most effective in detecting misclassified data. We will see how to evaluate the performance of a model by performing a variety of analysis methods and performing a variety of classification tasks. We will learn how to use the classification models that are most effective in detecting errors even for datasets that have been cleaned and optimized. We will see how to use the classification models that are most effective in detecting misclassified data. We will see how to evaluate the performance of a classifier by performing a variety of analysis methods and performing a variety of classification tasks. We will also introduce the random variables and random variables for non-linear regression models. We will see how to construct confidence intervals and evaluate non-linear regression models. We will learn how to implement the regression models and evaluate the performance of the models. We will also explain the concept

Course Link: https://www.coursera.org/specializations/statistical-analysis-r-public-health

Business Statistics and Analysis Specialization

Course Link: https://www.coursera.org/specializations/business-statistics-analysis

Business Statistics and Analysis Specialization
In this course you will learn about the main topics in statistics: sample size, confidence interval, hypothesis testing, and p-values. You will also learn about statistics that are specific to the business sector such as microeconomic variables, markets, industries, and prices. In addition, you will learn about the different file formats used in various statistical analyses, and how to save data in various formats so that you can present it graphically. You will also learn how to use Excel for statistical analysis. We’ll use weekly challenges that are designed to push you to the next phase of the specialization. These include the introduction to microeconomic variables, industry and price analysis/definitions, Industry X, and Expected Value Analysis. This course is designed to get you up to speed on the process of extracting value from data. We’ll also cover the basics of the Excel programming language and basic data analysis and visualization. We’ll also cover basic concepts and the basics of the challenge format, the weekly challenge to complete a level of statistical analysis, and the weekly challenge to use Excel for visualizations.

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Identify the file format each assignment is in
3. Select and copy/paste files
4. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Identify the file format each assignment is in
3. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Use Excel for statistical analysis

2. Explain the purpose of each assignment

3. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Summarize the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the file format each assignment is in
4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

7. Summarize the level of statistical analysis used in each challenge

8. Explain the purpose of each assignment

9. Use Excel for statistical analysis

10. Use Excel for statistical analysis

11. Explain the purpose of each assignment

12. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the purpose of each assignment

4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

7. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the purpose of each assignment

4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the purpose of each assignment

4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

7. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the purpose of each assignment

4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

Upon successful completion of all assignments in this course, you will be able to:

1. Describe the purpose of each assignment
2. Use Excel for statistical analysis

3. Summarize the purpose of each assignment

4. Use Excel for statistical analysis

5. Explain the purpose of each assignment

6. Use Excel for statistical analysis

7. Use Excel for statistical analysis

Upon successful completion of all assignments in this

Course Link: https://www.coursera.org/specializations/business-statistics-analysis

Best Coursera Courses for Machine Learning

Here is a list of best coursera courses for machine learning.

1. Machine Learning

As the first machine learning mooc course, this machine learning course provided by Stanford University and taught by Professor Andrew Ng, which is the best machine learning online course for everyone who want to learn machine learning. The content include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

2. Machine Learning Specialization

This Machine Learning specialization provided by University of Washington, which provides a case-based introduction to the exciting, high-demand field of machine learning. Students will learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, students will apply their skills to solve an original, real-world problem through implementation of machine learning algorithms. The Machine Learning specialization include 4 sub related courses:

1) Machine Learning Foundations: A Case Study Approach

This is the first course of the Machine learning specialization, which will make students get hands-on experience with machine learning from a series of practical case-studies. By the end of the course, student 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.

2)Machine Learning: Regression

This is the second course of the Machine learning specialization. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to:

  • Describe the input and output of a regression model.
  • Compare and contrast bias and variance when modeling data.
  • Estimate model parameters using optimization algorithms.
  • Tune parameters with cross validation.
  • Analyze the performance of the model.
  • Describe the notion of sparsity and how LASSO leads to sparse solutions.
  • Deploy methods to select between models.
  • Exploit the model to form predictions.
  • Build a regression model to predict prices using a housing dataset.
  • Implement these techniques in Python.

3) Machine Learning: Classification

This is the third course of the Machine learning specialization. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier.

4) Machine Learning: Clustering & Retrieval

This is the fourth course of the Machine learning specialization. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.

3. Mathematics for Machine Learning Specialization

This Mathematics for Machine Learning Specialization provided by Imperial College London, which let students learn about the prerequisite mathematics for applications in data science and machine learning. The Machine Learning specialization include 3 courses:

1) Mathematics for Machine Learning: Linear Algebra

This is the first course of the Mathematics for Machine Learning Specialization. In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

2) Mathematics for Machine Learning: Multivariate Calculus

This is the second course of the Mathematics for Machine Learning Specialization, which intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

3) Mathematics for Machine Learning: PCA

This is the third course of the Mathematics for Machine Learning Specialization. This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We’ll cover some basic statistics of data sets, such as mean values and variances, we’ll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we’ll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you’ll be familiar with important mathematical concepts and you can implement PCA all by yourself.

4. Advanced Machine Learning Specialization

This Advanced Machine Learning Specialization provided by National Research University Higher School of Economics and Yandex, which let students Deep Dive Into The Modern AI Techniques, and teach computer to see, draw, read, talk, play games and solve industry problems. The Machine Learning specialization include 7 courses:

1) Introduction to Deep Learning
2) How to Win a Data Science Competition: Learn from Top Kagglers
3) Bayesian Methods for Machine Learning
4) Practical Reinforcement Learning
5) Deep Learning in Computer Vision
6) Natural Language Processing
7) Addressing Large Hadron Collider Challenges by Machine Learning

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