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

Advanced R Programming

Course Link: https://www.coursera.org/learn/advanced-r

Advanced R Programming
The objective of this course is to teach advanced R programming using practical examples and examples of RStudio. We will cover topics such as data structures, data analysis, functions, functions as parameters, functions as return values, and generics. We will also cover basic R coding techniques such as comments and template metaprogramming. By the end of this course, learners will be able to code their programs in R using the full range of R features available, including R packages and RStudio.

This is the second course in the specialization: R Package and R Studio.Module 1: R Basics
Module 2: Functions
Module 3: Variables
Module 4: Go Deeper
Applying for Admission to Harvard Medical School
In this course, you will be introduced to the application of basic concepts of medical and engineering science in the application of medical school admission. You will learn the foundational principles of engineering as applied to medical school admission, including how to read applicants fingerprints, how to control admission officers, and what factors are considered in the admission control of medical schools. You will also learn the basics of how medical schools select students based on their performance, and compare and contrast the performance of different schools.

Upon successful completion of this course, you will be able to:
1. Choose a school to apply to
2. Make an application for admission to medical school
3. Apply for admission to medical school using the standardized application
4. Read and answer multiple choice questions
5. Review and grade essays
6. Apply and prepare for the standardized English Proficiency (ESP) test
7. Write and submit an official application for admission to medical school

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: Starting to Work with Data
Module 2: R Packages
Module 3: Running Data Analysis
Module 4: Putting it all Together
Applying for a Job in Healthcare
In this course, you will be introduced to the steps required to apply for a job in the health related field. You will learn which steps apply to applications from the general public, as well as job applications. You will also learn the steps required to submit your resume and cover letter, and the steps required to apply for a job as well as the job. You will also learn the steps needed to submit a cover letter and resume. Finally, you will be

Course Link: https://www.coursera.org/learn/advanced-r

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

Bayesian Statistics

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

Bayesian Statistics
Bayesian Statistics is an introductory course in statistics and machine learning that provides an introduction to Bayesian methods and statistics that can be applied to machine learning problems. The course introduces the concept of batch normalization and the various normalization methods that can be applied. The course also covers the concept of missing data and overfitting in a batch normalization task. The course then shows how statistical techniques can be applied to the missing data problem to correct for missing data. The course then shows how statistical methods can be applied to the overfitting problem. These techniques are then applied in a simple case study of a rain-dependent optimization problem. The final project is a complete Bayesian analysis of a real-world data set.Bayesian Statistics
Statistical Modeling
Overfitting
Business Strategy
You are a business leader, and you must develop a strategy to reach new markets. In this course you will learn about the most important considerations in developing a successful business strategy. You will learn how to align resources to achieve your strategic objectives. You will also learn how to evaluate competitors’ strategies and launch attacks. This course will give you an opportunity to practice the process of developing a business strategy that will enable you to acquire new markets, expand your reach, and become a more influential player in the evolving global business landscape.

Through this course you:
• Will learn how to use a set of concepts and techniques to analyze and develop a strategy
• Will learn how to align resources to achieve your strategic objectives
• Will learn how to evaluate competitors’ strategies and launch attacks

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.Milestone 1: Becoming an Enforcer
Milestone 2: Strategy
Milestone 3: Market Expansion
Milestone 4: Investing
Business Strategies for Social Impact
What is business strategy? What is the role of business strategy in achieving social objectives? This course aims to provide you with an overview of the topic. We’ll cover topics such as how to form a strong corporate citizen; how business strategy shapes organizational strategy; the importance of a corporate citizen; and the role of social enterprise.

We’ll also examine the role of entrepreneurship and innovation in the implementation of business strategy. By interleaving different topics, this course allows you to deepen your understanding of the topic. A learner with no prior formal business strategy background will be able to recognize trends and opportunities, and make more informed decisions as they pertain to their firm or organization.What Is Strategy?
Corporate Citizen
Organizational Strategy
Self-Assessment <| Course Link: https://www.coursera.org/learn/bayesian

Bayesian Statistics: Techniques and Models

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

Bayesian Statistics: Techniques and Models
Bayesian statistics are used to solve scientific and engineering problems in computer science and statistics, and are the basis of many algorithms used in industry. In this course, we will learn how to implement Bayesian methods, and then use the tools we learn to solve typical science problems involving Bayesian statistics. We will use Python to implement most of the important Bayesian techniques and make our own Bayesian software. We will use the Statistical Inference Framework (SIF) to make our own Bayesian software, and use the Boost SIF framework to do our own Bayesian analysis. We will also use Boost for Machine Learning. This course is the second in a sequence of three. In this course, we will cover the topics of Frequentist statistics, Bayesian programming, and statistical inference.Bayesian Statistics
Regularization
Bayesian Models
Regularization and Randomization
Basecamp for iOS: Get Started
In this introductory course to Basecamp for iOS, we’ll cover basic concepts such as getting started, looking at the project in question, and milestone tasks. We’ll also cover how to get started using the command line, including how to install and configure Xcode, and how to use Xcode Viewer to navigate between different sections of a project. We’ll also cover things like closing and launching a project, using the command line to debug and optimize code, and how to add features and fix bugs. We’ll use the Xcode framework to get started.

At the end of this course, you will be able to:
• Program your own project from scratch
• Create a new project from scratch using the command line
• Browse and install Xcode
• Access the project’s resources using the command line
• Use Xcode Viewer to navigate between different sections of a project

This is the first course in the Xcode/Apple family of courses. You should know how to build a project, and know how to use the command line. You should be familiar with Cocoa and the Objective-C standard. You should have some experience with building other types of apps, and know how to use the command line to debug and optimize code. If you are new to Xcode, proceed through the other courses in this specialization to make sure you understand the material required. If you are a seasoned Xcode user, follow the steps to set up your environment and get familiar with the language. If you are new to Xcode, you may want to check out the Quick Start Guide (https://developer.apple.com/library/ios/quickstart) for an in-depth look at the code and tools required to get started.

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

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

Big Data, Genes, and Medicine

Course Link: https://www.coursera.org/learn/data-genes-medicine

Big Data, Genes, and Medicine
Are you curious about the data behind today’s cures? Do you want to know how your own biologic and molecular clock is ticking? This course will take you on a journey to the answer those questions and more! Each week we will introduce new challenges while we delve deeper into the fascinating world of Big Data and how it is used. We will discuss topics such as statistical methods, their limitations, their applicability, and the ethics/values of using data for personal gain.

Already have a basic knowledge of data analysis? If so, welcome to the course!

Disclaimer: As you will notice, the class each week is significantly shorter than the course on average. This is to ensure a high level of quality and continuity. Each week you will complete the challenges one by one while learning a skill set that you can apply to other parts of your life. Each week you will complete the entire course with a minimum of 4 quizzes and a maximum of 12 mastery quizzes. Each week you will also get an opportunity to apply what you’ve learned by doing some additional activities and/or hands-on exercises to enhance your learning. We’ll cover everything needed to get started with data analysis including basic clustering, molecular analysis, and gene expression analysis.

If you enjoy this course and are interested in science, join millions of learners who are also passionate about health and medicine. The Big Data revolution is upon us and the opportunities are endless!Welcome & Introduction
Data Analysis & Bioconductor
Regional Variables
Sequence Analysis
Big Data Applications: Machine Learning
Machine learning is the process of getting computers to act on their own, by using machine learning algorithms. Machine learning requires deep learning, but it is possible to get computers to act on their own through simple tweaks in hardware and software. In this course, we will focus on the practical application of machine learning in various types of data, including text, music, pictures, and other images. We will learn both how to implement machine learning algorithms in C and how to use various open source and free software tools for machine learning research. You will also learn about the use of Python as a general purpose language for building machine learning models. Along the way, you will also learn how to use Python 3 as a general purpose operating system, as well as some practical techniques for programming it. This course is the third and last in the series on Data Science and Machine Learning.Welcome & Introduction to Machine Learning
Image Analysis & Sequencing
Text Analysis
Photo & Video Analysis
Big Data Applications: Regular Expression
This course will introduce you to regular expression and the basic rules that underlie its use in computer programs. You will start your journey in the world of regular expression by exploring the different parts of the regular expression tree and common

Course Link: https://www.coursera.org/learn/data-genes-medicine

Bioconductor for Genomic Data Science

Course Link: https://www.coursera.org/learn/bioconductor

Bioconductor for Genomic Data Science
Bioconductor is a data science software package that allows users to directly interface the NCBI’s Big Data Systems and Big Evolution libraries directly into their applications. The packages is freely distributable under the GNU General Public License.

This course should be taken after:
• Introduction to Data Science and Big Data in Genomic Medicine
• Advanced Big Data and Genomic Processing in NCBI’s NCBI Big Data Systems
• Introduction to Computational Biology and Python
• Introduction to Bioinformatics and Data Analysis
• Introduction to Machine Learning and Processing
• Introduction to Data Visualization and Visualization of Sequent Data
• Introduction to Big Data and Analysis on the PCSBI Platform
• Introduction to computer architecture and software concepts
• Introduction to machine language programming and data structures
• Introduction to Python)

Course Overview: http://bit.ly/2bPZKrz

Week 1: Coding Bioinformatics Projects with Python
In this week’s programming assignment, you will start to write a program that will read and write data from the surrounding very large genomic datasets (1000-fold, for example). We will use Python to implement several important algorithms, and you will learn how to use functions and variables to implement your own data-intensive methods.

First, you will need to install the required software:

sudo apt-get install python-plists (required as a prerequisite)
sudo apt-get install python-devtools (required as a prerequisite)

Next, you will need to install several packages:

cd ~ mkdir -p python-apt-get install python-devtools python-plists

Finally, you will need to run the program by running the command in the terminal:

python ~/Library/Application\ Support/Python/Python3/bin/plists

You can test that the program runs by typing in the console:

>>> python ~/Library/Application\ Support/Python/Python3/bin/plists

The course introduces Python 3.

Week 2: Reading & Writing Genomic Data
In this week’s programming assignment, you will implement a ReadPlacid algorithm that reads placid genomic data from a file. We will use Python’s BeautifulStrings library and placid library as examples.

First, you will need to install the required software:

sudo apt-get install python-plists (required as a prerequisite)

Next, you will need to install the required software:

cd ~ mkdir -p python-apt-get install python-devtools (required as a prerequisite)

Finally, you will need to run the program by running the command in the terminal:

python ~/Library/Application\ Support/Python/Python3/bin/plists

You can test that the program

Course Link: https://www.coursera.org/learn/bioconductor

Data Science Capstone

Course Link: https://www.coursera.org/learn/data-science-project

Data Science Capstone
The Capstone Project is the culmination of your data science journey through the Data Science Specialization. You will build on your understanding of the material covered in the Specialization modules A, B, and C by building an algorithm that can solve a high-dimensional optimization problem. The emphasis in this final project is squarely on practical skills that you can apply to solving real world optimization problems.

You will have access to the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your own neural network and evaluate its performance. You will have a chance to wrap up your data science journey with a project involving building a deep neural network that can read text.

This course is the culmination of your data science journey through the Data Science Specialization. You will build an algorithm that can solve a high-dimensional optimization problem and then use that knowledge to build an application. The emphasis in this final project is squarely on practical skills that you can apply to solving real world optimization problems.

You will have access to the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your own neural network and evaluate its performance. You will have a chance to wrap up your data science journey with a project involving building a deep neural network that can read text.

This course is the culmination of your data science journey through the Data Science Specialization. You will learn how to use the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your own neural network and evaluate its performance. You will have a chance to wrap up your data science journey with a project involving building a deep neural network that can read text.

This course is the culmination of your data science journey through the Data Science Specialization. You will learn how to use the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your own neural network and evaluate its performance. You will have a chance to wrap up your data science journey with a project involving building a deep neural network that can read text.

This course is the culmination of your data science journey through the Data Science Specialization. You will learn how to use the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your own neural network and evaluate its performance. You will have a chance to wrap up your data science journey with a project involving building a deep neural network that can read text.

This course is the culmination of your data science journey through the Data Science Specialization. You will learn how to use the full power of the TI-84 Data Science Tools and pre-trained neural networks. You will also have the capacity to run your

Course Link: https://www.coursera.org/learn/data-science-project

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