Advanced Linear Models for Data Science 1: Least Squares

Course Link: https://www.coursera.org/learn/linear-models

Advanced Linear Models for Data Science 1: Least Squares
This course introduces the basic techniques for constructing optimal solutions to linear models and introduces the linearization of data by introducing the linearization standard. We will learn about the implementation details and the main questions that arise from the analysis of data sets that are too large to fit directly on their own. These problems can be solved using the various optimization algorithms available and get their solutions run on real datasets.

Prerequisites:
To get the most out of this course, learners should have:
• Completed Linear Models for Data Science 1: Foundations or have equivalent experience
• Basic knowledge of Python

Get ready to work with data sets that are too big to fit directly on their own. These problems can be solved using the various optimization algorithms.

The course is structured in four modules, each one revolving around a different aspect of data science:

Weekly Problem Solving
Weekly Problem Solving Using R (or similar language)
Weekly Problem Solving Using Python
Weekly Problem Solving Using Excel
Advanced Machine Learning
This course aims to provide an introduction to advanced machine learning methods. The techniques covered include: (i) supervised learning, (ii) unsupervised learning, (iii) regularization and (iv) configuration optimization. We will learn how to use machine learning frameworks and frameworks for a wide variety of problems. We will also include a high level introduction to the process of using the framework.

A learner with little or no math background is welcome to take this course. Those with some math background but little knowledge of machine learning will be able to understand the concepts introduced in this course. The course is ideal for those that work in industry, for those that want to advance their skill in machine learning, and for those that just want to learn about some of the most important topics in machine learning.

This course can also be considered as an advanced beginner’s course. Machine learning is still in a very early stage where some concepts are being introduced. This course focuses on the most important topics in machine learning:
1) Supervised learning, 2) Unsupervised learning, 3) Regularization, and 4) Configuration optimization.Machine Learning Principles
Supervised Learning
Unsupervised Learning
Regularization and Configuration Optimization
Advanced Materials II: Ceramics
Ceramics are used in all areas of your career, starting as early as your first day on the job. They’re the materials that make your job as a craftsman or engineer or handyman or salesperson or clerk in a company or warehouseman or kitchen mitt technician or laborer or electrician or electrician and boiler or electrician and transmission or water and sewer or water utility. You need to be able to make ceramics, so this course will learn how

Course Link: https://www.coursera.org/learn/linear-models

Basic Statistics

Course Link: https://www.coursera.org/learn/basic-statistics

Basic Statistics
An introduction to basic statistics in statistics.

In this course, we will look at the statistics behind the most important findings from quantitative models of population growth, mortality, and welfare. We will start by introducing the distribution of things and the nature of the sample. We will then look at the measurement of values and the nature of the sample. We will then look at the statistical significance of common measurements and the main principle of inference. We will then look at different types of statistical tests and apply them to various types of statistical models. We will look at the experimental design and randomization method of the statistical model and various models in the literature. We will then look at the significance of a finding in a model and the statistical tests that can be used to assess it. We will then look at the common definitions and the different statistical models that are used in the literature. We will then look at the different statistical tests and their significance tests used in the literature. We will then look at the different types of statistical models that are used in the literature. We will then look at the various types of statistical tests and their significance tests used in the literature.

This course is expected to be challenging and exciting!The distribution of things
Measurement
Statistical significance
Basic German for Beginners
This course is for newcomers to German. It is intended for intermediate speakers.

In this course, you will learn the basic language of German, including the native German accent. You will also learn about daily life in Germany and the differences between everyday German and German. You will have a basic understanding of the daily life of each local German dialect, and of the grammar and pronunciation of the German words and sentences you will hear.

The course focuses on vocabulary, pronunciation, teaching topics, German grammar and German vocabulary, and language instruction. We hope that you will enjoy the lessons and topics that we have chosen to cover.Basic Vocabulary
Extended Vocabulary
Passing and Sentence Translation
Pronunciation: C, D, F, G, A, P
Be Sociable! Meet Goofy
This free online course teaches you a fun, sociable and creative way to interact with other people in a relaxed and interactive environment.

Join us for a short time-lapse of sorts as we explore the various different facets of being a parent, educator, and professional. You’ll learn about the various facets of being a parent as they relate to the work you do as a parent, the various levels of expertise you need to attain, and how you can leverage social and peer relationships to gain experience and knowledge.

You’ll also learn about the different levels of engagement and collaboration among people in a collaborative and collaborative environment as they relate to the work you do as a parent, the different levels of

Course Link: https://www.coursera.org/learn/basic-statistics

Bayesian Statistics: From Concept to Data Analysis

Course Link: https://www.coursera.org/learn/bayesian-statistics

Bayesian Statistics: From Concept to Data Analysis
Bayesian statistics are the branch of statistics that deals with probability, distribution, and Bayes’ rule. This course covers Bayesian concepts from concept to data analysis, including modeling and exploration of the Bayesian software package R. Bayesian statistics are useful for implementing statistics that are based on probability, distribution, and Bayes’ rule. The course is designed to be as fast as possible for you to explore and apply Bayesian concepts, while at the same time taking advantage of the open source software package R and the library of statistical inference tools available from the R Foundation.Bayesian Statistics: From Concept to Data Analysis
Bayesian Distribution
Bayesian Testing
Bayesian Reconstruction
Be Sociable, Share!

 You’ll begin the Capstone project with a series of conversations with fellow students as you learn about different facets of social life in San Francisco.

You’ll also be introduced to the Capstone project and the social context of discussion in the group.

In the summer of 2018, you’ll be able to engage in online discussions of any kind, including those in private, peer-to-peer and online forums.Introduction to Capstone and Conversation in the Group
Introduction to Capstone Project
Capstone Discussion
Introduction to Capstone Project 2
Biology: What is it, and how does it differ from other sciences?
Do you want to know more about the biology behind today’s dietary trends, the human biology that informs our behavior, our environment, and our development? This course will introduce you to the major topics of biology across the board, including the nutrition and disease sciences, the genetics of obesity, the neurobiology of obesity, the human immunology that helps us fend off illness, the pedagogical potential of biology in the classroom, and the controversies that remain about the appropriateness of some theories for teaching and learning.

Upon completion of this course, you will be able to:
1. Explain the major topics of biology across the board, including the nutrition and disease sciences
2. Identify the scientific controversies that remain about the appropriateness of different theories for teaching and learning
3. Analyze the science behind a number of popular dietary trends such as Paleo, Atkins, and gluten-free diets, and the related health conditions
4. Explain the pedagogical potential of biology in the classroom, and the controversies that remain about the appropriateness of some theories for teaching and learning
5. Analyze the scientific evidence for and against various dietary trends
6. Explain the origins and development of obesity
7. Discuss the controversy over the origins of obesity
8. Familiarize yourself with the major scientific questions that arise from the study of physiology and the environment, including questions about the role of genetics and environmental

Course Link: https://www.coursera.org/learn/bayesian-statistics

Data Analytics for Lean Six Sigma

Course Link: https://www.coursera.org/learn/data-analytics-for-lean-six-sigma

Data Analytics for Lean Six Sigma
Lean Six Sigma is a statistical hypothesis testing approach used by statisticians and engineers to evaluate the strength of statistical arguments against a hypothesis. This course covers data analysis and interpretation of statistical measures of statistical significance. We will use a statistical technique for detecting and reporting hypothesis testing which is called p-value’s’ significance testing.

This is the second course in the Logistics and Supply Chain Logistics & Logistics Management specialization.The Logistics & Supply Chain Paths
Analyzing Data
Converting Logistics to Sales
Registering and Authorizing Accounting for Supply Chain Logistics
Data Analysis and Presentation Skills for University Success
In this course, you will learn key elements of presentation skills needed for university success. You’ll learn about how to write a compelling presentation, how to present data in a clear and concise manner, and how to make your data compelling and memorable. You’ll also learn key elements of statistics and probability. You’ll learn key elements of hypothesis testing and confidence intervals. You’ll also learn key elements of hypothesis generation and confidence intervals. You’ll also learn how to use presentation skills to overcome biases and stereotypes in order to achieve success in the classroom and in the workplace.

At the end of this course, you will be able to:
1) write persuasive presentations
2) interpret data in a clear and concise manner
3) make your data compelling and memorable
4) use presentation skills to overcome biases and stereotypes in order to achieve success in the classroom and in the workplace

This course is part of the university course on Data Analysis and Presentation Skills for University Success. To join the full course load the appropriate module from the start.

This course is part of the University of London programme in Data Analysis and Presentation. To join the full course load the appropriate module from the start.

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Course Link: https://www.coursera.org/learn/data-analytics-for-lean-six-sigma

Data Science at Scale – Capstone Project

Course Link: https://www.coursera.org/learn/datasci-capstone

Data Science at Scale – Capstone Project
In this capstone project course, you will apply the knowledge you’ve acquired in these courses to build a practical machine learning model. You will need to know:

*Capstone Project Structure: weeks 1-3 are the “Introduction” phase, which means you need to understand the material you’ve learned in each of the three courses.

*Capstone Project Format: week 3 are the “Extensive” and “Confidential” phases, which means you want to experiment and work together with other learners to get the best possible experience.

*Course Format: The course will run for 4 weeks and feature 4 major modules. Each week there will be 4-5 video lectures with multiple-choice questions and a small quiz. There will be multiple assignments and quizzes throughout the course, but they will not be graded.

*Capstone Project Scope: The scope of the capstone project should not exceed your own personal goals, including but not limited to:

1. Design a machine learning model to process data from a large number of sources (layers, layersets, images, machine learning models)
2. Build a working example of the model

3. Evaluate whether the model performs as expected

4. Test the model performance against different data types

5. Build a model to run on your own data

6. Evaluate the model performance against a real dataset

7. Build a model to run on your own data

8. Evaluate a real-world problem

9. Build a machine learning model to solve a real problem

10. Build a model to run on your own data

11. Evaluate a problem using a data set

12. Evaluate a problem using a model

13. Build a model to solve a real problem

14. Build a model to solve a real problem

15. Run your model through a real dataset

16. See what happens when you run your model through your model

17. Explain the model’s performance

18. Build a model to solve a real problem

19. Support your model with data analysis

20. Support your model with model training

21. Support your model with data validation

22. Support your model with service learning

23. Support your model with optimization

24. Support your model with machine learning

Time: 10 hours of study, 4 weeks of active learning

Total Investment: $20

Link: https://www.coursera.org/learn/capstone-project-strategy/home/welcome

To join: Email an instructor an email with your name and the subject line “Open Capstone for Individual Learner” and the term “Open up a Capstone Project for Individual Learner”

To request a refund

Course Link: https://www.coursera.org/learn/datasci-capstone

Inferential Statistics

Course Link: https://www.coursera.org/learn/inferential-statistics

Inferential Statistics
Inferential statistics provide a means of describing the relationships among different variables measured in a population. An inferential statistic describes the relationship between a variable measured in a population and a continuous variable measured in a population. It describes the sampling frame of the population, the level of confidence of the statistic from a data set, and the level of statistical significance of the statistic measured in a population. An inferential statistic is a nonparametric test statistic for statistical significance.

This course covers the general concepts of inferential statistics along with basic statistical analysis. It will be particularly useful for researchers who work in population genetics, population genetics data analysis, and population behavior and health sciences. The course also applies to those who work in engineering, mathematics, computer science, and statistics as well as those who teach statistics and data analysis.Inferential Statistics Concepts
Sample size analysis
Confidence intervals
Inference
Inferential Statistical Analysis
An inferential statistical analysis is an analysis based on inferences from statistical tests or inference. It attempts to address the problem that many statistical tests require that are not formally modeled or explained in the way that we normally explain them in terms of probability, standard deviation, and variability. The theory behind inferential statistical analysis is that we naturally interpret large numbers of observations correctly if the methods to do so are well-understood. This course covers statistical tests and inference that is based on inferences. It also covers models that attempt to address the problem by modeling it and performing inferences using statistical techniques.Inferential Statistics
Standard Errors and Probability
Inference
Model
Inferential Statistical Analysis
An inferential statistical analysis is an analysis of a data set using inferential methods. It attempts to address the problem that many statistical tests require that are not formally modeled or explained in the way that we normally explain them in terms of probability, standard deviation, and variability. The theory behind inferential statistical analysis is that we naturally interpret large numbers of observations correctly if the methods to do so are well-understood. This course covers statistical tests and inference that is based on inferences. It also covers models that attempt to address the problem by modeling it and performing inferences using statistical techniques.

This course covers the inference methods to make inferences using random variables as models, posterior distributions, and continuous variables as measures of inter-observer reliability. It also covers models that attempt to address the problem by modeling it and performing inferences using statistical techniques.

This course covers the statistical techniques used to make inferences using different statistical models as models. It also covers models that attempt to address the problem by modeling it and performing inferences using statistical techniques.

This course covers the statistical techniques used to make inferences using a data set as a model. It also covers models that attempt to address the problem by modeling it and

Course Link: https://www.coursera.org/learn/inferential-statistics

Introduction to Genomic Technologies

Course Link: https://www.coursera.org/learn/introduction-genomics

Introduction to Genomic Technologies
This course introduces the underlying principles of genome-wide and inter-population genetic drift, and how these effects can cause genetic diseases. We will learn about the techniques used to detect the effects of genetic drift in genomic data, and the methods used to characterize the mutations that cause these diseases. We will also discuss the appropriate approaches to reporting genetic effects in genomic data, and how these effects are detected. We will review the tools that are used to assess the causalness of genetic effects in genomic data, and the methods that are used to detect the effects of genetic drift in genomic data. We will also describe the process of using genomic data to detect genetic effects, and the tools that are used to assess the causalness of genetic effects in genomic data.

Upon completion of this course, you will be able to:
1. Explain how genome-wide drift is caused and the tools used to detect it
2. Define how inter-population genetic drift affects populations
3. Describe the tools used to detect the effects of genetic drift in genomic data
4. Use tools to assess the causalness of genetic effects in genomic data
5. Use the nomenclature “Inter-population Genetic Drift” and “Inter-population Genetic Mutations”
6. Use the “Mutation” test to assess the causalness of genetic effects
7. Infer causalness from mutation

To enroll in this course for free, click “Enroll now” and then select “Full Course.”
You will need to enter a PIN (Personal Identification Number) and password for your smartphone. Once enrolled, you will be able to access any of the course materials by copying and pasting the following link into your web browser:
http://tinyurl.com/hklmhq9

By copying and pasting this link into your browser, you will be able to:

– Watch a video overview of the course

– Watch a video preview of the course

– View a sample genome-wide scan

– Use the free 32-hour introductory DNA barcode app to calculate your own DNA barcode

– Go to any of the three online DNA barcoding sites:

(1) DrPHENIX.ORG

(2) HONOLULU.ORG

(3) OSA.ORG

– Watch a video on how to use the app

– Read the following articles:

(1) Biobusiness and Food Safety: Is it Safer?

(2) Biobusiness and Food Safety: Are you being poisoned?

(3) Biobusiness and Food Safety: Can we eat healthy?

(4) Biobusiness and Food Safety: Can we live longer?

(5)

Course Link: https://www.coursera.org/learn/introduction-genomics

Introduction to Probability and Data

Course Link: https://www.coursera.org/learn/probability-intro

Introduction to Probability and Data
This course introduces the basic concepts of probability and data sampling, using the R package. The course covers the topics of integration, geometric means, sampling frequencies, normal distribution, normal distribution with hyperbolic distribution, normal distribution with exponential growth, random variables, and the log-log function. All topics covered are dealt with through video lectures, example problems, and hands-on labs. The course covers the basics of data analysis and visualization, and is the first of three related courses in the Data Analytics Specialization.

Prerequisites:
To get the most out of this course, learners must:
• practitionHave prior working knowledge of probability and data analysis
• practitionHave prior experience using statistical software
• Be able to use R package (R core Files, RStudio)
• To have basic knowledge of EnglishBias
• Have prior experience using statistical software
• Have prior knowledge of R programming

The course is designed for non-native English speakers who are interested in learning how to use R to analyze and visualize data.

To get the most out of this course, learners must:
• Have prior working knowledge of probability and data analysis
• Be able to use R package (R core Files, RStudio)
• To have basic knowledge of EnglishBias
• Have prior experience using statistical software
• Have prior knowledge of R programming

The course is designed for non-native English speakers who are interested in learning how to use R to analyze and visualize data.

To get the most out of this course, learners must:
• Have prior working knowledge of probability and data analysis
• Be able to use R package (R core Files, RStudio)
• To have basic knowledge of EnglishBias
• Have prior experience using statistical software
• Have prior knowledge of R programming

The course is designed for non-native English speakers who are interested in learning how to use R to analyze and visualize data.

To get the most out of this course, learners must:
• Have prior working knowledge of probability and data analysis
• Be able to use R package (R core Files, RStudio)
• To have basic knowledge of EnglishBias
• Have prior experience using statistical software
• Have prior knowledge of R programming

The course is designed for non-native English speakers who are interested in learning how to use R to analyze and visualize data.

To get the most out of this course, learners must:
• Have prior working knowledge of probability and data analysis
• Be able to use R package (R core Files, RStudio)
• To have basic knowledge of EnglishBias
• Have prior experience using statistical software
• Have prior knowledge of R programming

The course is designed to provide learners with the basic

Course Link: https://www.coursera.org/learn/probability-intro

Linear Regression and Modeling

Course Link: https://www.coursera.org/learn/linear-regression-model

Linear Regression and Modeling
We will use linear regression models to explore the relationships between variables in a data set and a continuous outcome, linear model specification, and explanatory variables. We will also use regression diagnostics to determine underlying causes of regression.

You will need to have a basic knowledge of statistics and machine learning to be able to use the models and specifications in this course.Linear Regression Methods and Modeling
Linear Model Specification
Model Diagnostics and Model Run Quality
Exploring Univariate Regression
Machine Learning in python
This course is all about Machine Learning and its applications in the data science world. We will learn about the most important algorithms and techniques for constructing models for data science, and how to apply those techniques to solve problems in various specialties. We will also learn about deep learning techniques that can help with tasks like recognizing faces from videos. These techniques are applied in the context of a wide variety of scientific and engineering problems, and are all based on the linear model principle. We will also learn about different types of deep learning models and how to evaluate their performance.

This is a deep dive course, and will require some prerequisite knowledge of python. We will start with a short description of what machine learning is, the basic concepts, how it differs from regular neural networks, and how they perform. We will then explain the common techniques used for classification problems, including linear models, random forests, and multilayer perceptrons. We will also learn about the most popular deep learning techniques – SGD, RBFR, and convolutional neural networks.

If you are new to python programming, please review the tutorial “Getting Started” and “Working with numpy and numpy modules”.

We hope you enjoy this course, and look forward to seeing you in class!The Linear Regression in Python
Linear Models and ML
Deep Learning for Science
Deep Learning for Engineering
Monte Carlo simulation
This is a general purpose simulation tool based on the popular Arnold-Hinton game theory. It is suitable for general purpose simulation, computer vision, and many other fields. It can run on GPU-hardened hardware (Intel x86_64 or AMD64), as well as other modern operating systems. It is compatible with most common programming tools, and uses the popular OpenCL framework. The simulator uses a very simple language: Python, so you can learn python without learning any special knowledge. The program has a number of libraries, including the optimization toolchain, a number of simulation libraries (including ones for numpy, matplotlib, and flex), as well as a number of other small tools and a large number of lines of code. The minimal required software to run the program and build the simulator is a python-based distro like Ubuntu 14.04 LTS, or equivalent

Course Link: https://www.coursera.org/learn/linear-regression-model

Math behind Moneyball

Course Link: https://www.coursera.org/learn/mathematics-sport

Math behind Moneyball
This course is all about using math and statistics to help you win games of baseball. We’ll learn how to use basic principles of math to answer baseball questions and how to use these principles to answer more advanced questions. We’ll cover topics like proportions, proportions, angles, quantities, and stuff you’ll need in order to use baseball statistics effectively. We’ll also cover topics like counting, probability, and random variables, and stuff like that. By the end of this course, you’ll have a pretty good grasp on the concepts involved in winning games of baseball.Course Overview and Module 1
Mathematical Principles Behind Winning Ballgames
Mathematical Modeling for Winning Games
More Mathematics in Winning Games
Math with the ELL in Mind
This course is aimed at introducing the basics of Math to third and fourth-graders through an anthropomorphic, educational and fun-filled, multi-sensory approach. The course aims to teach the fundamentals of Math through an experiential, approach, rather than through theory-based lectures.

The ELL in the class is an important part of the course. We hope that with the ELL in Mind the level of the class will increase as well as the fun-factor.

The course is 70% science (biological, psychosocial and learning), 20% technology (brief description of concepts and an exploration of language), 5% music (music of any kind, including songs, raps, poetry, and anything in between)). The remaining % is spent on teaching, making connections, and meetinging out projects.

We hope that with the ELL in Mind the level of the class will increase as well as the fun-factor.

The course is 70% science (biological, psychosocial and learning), 20% technology (brief description of concepts and an exploration of language), 5% music (music of any kind, including songs, raps, poems, and anything in between)). The remaining % is spent on teaching, making connections, and meetinging out projects.

We hope that with the ELL in Mind the level of the class will increase as well as the fun-factor.

The course is aimed at introducing the basics of Math to third and fourth-graders through an anthropomorphic, educational and fun-filled, multi-sensory approach. The course aims to teach the fundamentals of Math through an experiential, approach, rather than through theory-based lectures.

The ELL in the class is an important part of the course. We hope that with the ELL in Mind the level of the class will increase as well as the fun-factor.

The course is 70% science (biological, psychosocial and learning), 20% technology (brief description of concepts and an

Course Link: https://www.coursera.org/learn/mathematics-sport