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

Inferential Statistical Analysis with Python

Course Link: https://www.coursera.org/learn/inferential-statistical-analysis-python

Inferential Statistical Analysis with Python
This course teaches you to use statistical inference to make sure your conclusions are based on an appropriate data and/or interpretative analysis. You learn how to construct confidence intervals and significance tests using Python so you can use them in Python programming. We also go over how to use randomization to select the sample from a population. These concepts are introduced using a practical example using data from a decade of clinical practice. You are required to write a program that approximates the distributions of variables in your data. You also learn how to visualize your results and interpret a chi-square test for significance. You then learn how to summarize and view your data in a chart. This course is designed to cover the basic prerequisites for working with statistical inference, interpreting and tabulating data, as well as the modeling and optimization of statistical inference.

Learning Outcomes
After completing this course, you will be able to use Python to construct confidence intervals and draw conclusions about the population that you have. You will also be able to interpret a chi-square test for significance. You’ll also be able to recognize and interpret a randomization.Basics of Inferential Statistical Analysis
Randomization
Hypothesis Testing
Summarizing Data
Inequality and Inequality Without Equality: The Income Scenarios
In this course we focus on the question of inequality and inequality without inequality. We start by introducing the key measures of inequality and discuss the logic of measuring differences in income. We also discuss the concept of equality and the idea of equality without inequality. We conclude by discussing the issue of equality for all, including the concept of inequality based on income. We’ll also cover the issue of inequality across countries and income groups. We’ll also look at the distribution of income across individuals and households, including the concept of inequality between groups as well as the concept of equality without inequality. We’ll also explain the concept of income inequality and look at how income is earned.

Learning Outcomes
After completing this course, you will be able to explain the differences between different income measures and explain why different income measures are used. You will also be able to recognize and interpret a number of key economic indicators that are designed to measure inequality. You’ll also be able to explain the concept of income inequality and look at how income is earned.

Taking the Capstone
After completing this course, you will be able to explain the differences between different income measures and the logic of measuring them. You will also be able to recognize and interpret a number of key economic indicators that are designed to measure inequality. You’ll also be able to explain the concept of income inequality and look at how income is earned. The course will also cover the issue of equality across countries and income groups. You’ll also be able to recognize and interpret a number of key economic indicators

Course Link: https://www.coursera.org/learn/inferential-statistical-analysis-python

Inferential Statistics

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

Inferential Statistics and Data Analysis
Inferential statistics are the science and art of drawing inferences from small numbers. In this course, we cover the basic elements of data analysis. We start by introducing the different types of inferences, including generalizations, inference from correlation, inference from a model, and inference from a simple model. We discuss how to use inferential statistics and how to interpret the results. We also look at alternative designs for inference such as random sampling, mixed effects, and sub-clustering. We also look at the different statistical tests that can be used to assess the reliability of inferences. You will learn how to obtain confidence intervals and how to interpret these results. You will also learn techniques for performing hypothesis testing and evaluating the confidence interval.

At the end of this course, you will be able to:
• Explain the different types of inferences and draw inferences from small numbers
• Explain the different inferential techniques and test values for confidence intervals
• Understand the main techniques used to obtain confidence intervals and how to interpret the results
• Use different statistical tests to assess the reliability of inferencesIntroduction to Inferential Statistics
Data Analysis and Hypothesis Testing
Inference Methods
Inference Techniques
The Science of Well-Being
“The Science of Well-Being” taught by Professor Laurie Santos overviews science behind the best-selling authorship-based health promotion and wellbeing theory. In this course, you will learn how to measure wellbeing using a variety of methods and concepts, including questionnaires, self-report measures, and interviews. More specifically, you’ll learn how to measure wellbeing using a validated, standardised interview format, and how to incorporate social and voluntary aspects of wellbeing.

This course can also be used as a standalone course or as a precursor to the ‘Health in Quality and Quantity’ Specialisation.

In this introductory course, Professor Santos will walk you through a range of existing and future trends that are related to wellbeing; and examine the factors that affect wellbeing in different settings, such as work, education, relationships and family life. You’ll learn how to incorporate factors of personal and community wellbeing into wellbeing measurement, and how to use wellbeing as a proxy for other important wellbeing measures. You’ll also learn how to interpret and report on wellbeing, and how to interpret and report on social factors as well as other factors affecting wellbeing.

This course is one of three in the ‘Well-Being in Development’ series, for a fee of $49.95.

If you are interested in earning 3 university credits from the University of Illinois, please fill out the application form and take the 3 exams. If you have already taken the 3 exams, you can skip the first exam and complete the course with a grade of C+ or better.

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

Inferential and Predictive Statistics for Business

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

Inferential and Predictive Statistics for Business Statistics
Inferential and predictive statistics are the applied statistical methods within business statistics that use quantitative techniques to make inferences based on statistical measures. These methods are most commonly applied to predict the output from economic models. Inferential statistics are methods that attempt to use probability to guide decision making, while predictive methods attempt to use statistical measures to guide action. The course covers basic concepts of inferential statistics, models, and models, with an emphasis on applying these methods to make inferences based on models and specifications. The course is designed to take you from an introductory level grasp of the key concepts to a more advanced level of skill at interpreting and interpreting business statistics. You will be introduced to methods to determine the relationship between variables in a data set and a desired outcome. You will also be introduced to methods to determine the significance of variables in a data set. We will use statistical measures to guide decision making, and we will use inferential statistical methods to make inferences. You will also be introduced to different statistical approaches that apply these methods to make inferences. The course will benefit from the perspectives of different statisticians so you can make informed decisions as you go.Chapter 11: Introduction and Models of Business Statistics
Chapter 11:1: Linear Models
Chapter 11:2: Logistic Regression
Chapter 11:3: Multilevel Models
Inferential Statistical Analysis
In this course, you will learn the basic concepts and interpretive strategies of the statistical technique of drawing inferences from a large number of independent studies. You will learn how to interpret the statistical measures that are part of a data set and interpret the conclusions that can be drawn from a small number of studies. In particular, you will learn the different types of studies and the different types of statistical measures used in a statistical analysis. You will also learn how to interpret the conclusions that can be drawn from a small number of studies. In this way, you will have a high-level understanding of the statistical methods covered in the specialisation. The course will also guide you through the interpretation of conclusions that are based on studies. You will also learn how to explore the statistical methods covered in the specialisation using the “Tools” menu item on the right. This is the third course in the Data Analysis Specialisation. The course starts with an overview of the statistical techniques that are used to interpret a data set and the different types of studies that support those techniques. We then explain the different statistical measures and explain how they are used. We then explain how the statistical analysis is done. We will also explain the different statistical models used in the statistical models. We will also explain the different types of statistical tests used in the statistical tests and how they are done. Finally, we will explain how the tools and statistical models are applied in practice. We will describe the different types of statistical analysis and models that are commonly used in practice. This course also guides you

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

Statistical Inference

Course Link: https://www.coursera.org/learn/statistical-inference

Statistical Inference
Inference is the use of statistical methods to support scientific claims in medicine and medical research. In this course, we will consider the basic principles of statistical inference and its implementation in practice. We will analyze multiple variables in a regression, and we will consider the null hypothesis that the data do not vary significantly. We will also consider the possibility that the observed values are not normally distributed and that the observed values are not normally distributed at all. We will consider the hypothesis that the null hypothesis is true, and the null hypothesis can be ignored. We will consider different types of statistical tests, including multiple regression, Wilcoxon, Mantel-Haens’ tests, and logistic regression. We will also consider the possibility that the observed values are not normally distributed, and we will consider the null hypothesis that the null hypothesis is not true. We will consider different types of tests, including multiple regression, Wilcoxon, Mantel-Haens’ tests, and logistic regression. We will also consider the null hypothesis that the null hypothesis is not true, and we will consider the negative binomial distribution. We will consider the null hypothesis that the null hypothesis is not true, and we will consider the negative binomial distribution. We will consider different types of tests, including multiple regression, Wilcoxon, Mantel-Haens’ tests, and logistic regression. We will also consider the null hypothesis that the null hypothesis is not true, and we will consider the negative binomial distribution.Statistical Inference
Multiple Regression
Wilcoxon
Mantel-Haens’ Test
Statistical Methods in Practice
Through this course, you will gain a practical understanding of the most popular statistical methods in the field of statistics: Chi-Square, Aldens, multiple imputation, regression, and multiple observations. You will be able to recognize the most important types of statistical tests, and you will know how to apply the most popular statistical inference methods to detect and interpret data patterns. You will also be familiar with the data visualization tools and their uses in practice.

This course is designed to provide you with a solid foundation for the statistical experiment. We will introduce you to the basic of statistics, a common area of study for most statistics students, and give you a few pointers in how to approach the field from a beginner’s point of view. We will learn how to select appropriate statistical tests, and we will get you up and running with basic statistical inference methods in Python so that you can spot errors and issues in your data. We will also walk you through the most common statistical methods in the field of statistics, such as chi-square, random variables, random effects, and multiple comparisons. You will be able to make educated guesses in your data from the statistical results, and you will learn how to interpret the results correctly from a novice’s point of view.

This course assumes only

Course Link: https://www.coursera.org/learn/statistical-inference

Mindware: Critical Thinking for the Information Age

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

Mindware: Critical Thinking for the Information Age
Information Technology has changed the way we do our everyday things. The amount of data that is out there in the world is overwhelming. We need to be able to process and store this information in a way that is efficient and convenient for our everyday lives. Information technology is the key to unlocking the full potential of information systems. Mindware teaches you how to think critically and creatively in an age when cognitive shortcuts are becoming all the rage. Through this course, you will learn how to think critically and creatively in an era when cognitive shortcuts are becoming all the rage. We will cover topics such as how to think critically of what you see and hear, the difference between objective and subjective truth, the importance of skepticism and the power of reasonableness in evaluating information, and the need for objectivity in evaluating information. Mindware will be taught by top experts with over 30 years of combined experience in information technology, computer science, mathematics, statistics, and data science, and will equip you with the skills to identify and assess the truth of what you see and hear.Introduction to Mindware
The Science of Information Technology
Devolving Information
Objective-Selected Information
MindShift: Mindless Thinking for University Success
Welcome to MindShift for University Success. In this course, you will learn how to think less and actively pursue goals beyond what is reasonable in your circumstances. You will learn how to improve your decision-making process and your ability to analyze situations creatively. You will also learn how to improve your decision-making skills when you are faced with situations that are beyond your control. You will examine strategies to accomplish goals that are aligned with your priorities and to make smarter decisions.

Upon completing this course, you will be able to:
1. Select appropriate activities within your chosen area of expertise
2. Understand situations that call for different decision-making skills than you have experienced
3. Analyze situations that call for different decision-making skills than you have experienced
4. Choose appropriate decision-making process and analysis
5. Choose appropriate decision-making style and approach
6. Choose appropriate decision-making mode and approach
7. Choose appropriate decision-making mode and approach
8. Choose appropriate decision-making mode and approach
9. Choose appropriate decision-making mode and approach
10. Analyze situations that call for different decision-making skills than you have experienced
11. Analyze situations that call for different decision-making

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

Improving your statistical inferences

Course Link: https://www.coursera.org/learn/statistical-inferences

Improving your statistical inferences
In this course you will improve your statistical reasoning skills by learning how to use different statistical techniques to draw more informed inferences. This includes a structure of the data, adjustments for measurement error, as well as other techniques such as design of experiments and the analysis of outcomes. You will also learn to interpret different interpretation techniques and examine their advisability. We will start with a look at the various adjustments that statisticians make to their methods to interpret the results of their experiments. We will then cover various designs and explore the different issues associated with them. We will also look at how statistical inference is different from other methods employed in medicine, engineering, and business. We will conclude the course with a review of the different areas of expertise of statisticians and of course a survey of the specialties to be pursued in the future.Module 1: Introduction to Improving your Inferences
Module 2: Structure of the Data
Module 3: Adjustments for Undesirable Variables
Module 4: Interpretation Techniques
Italian football: Roma v Napoli
This is a friendly game between two of Europe’s top clubs. The two clubs are well-established in the footballing world, having played each other a number of times over the last decade. However, the game is played in many different parts of the world and in many different leagues, so it is difficult to know which region of Italy to watch out for. The game is also a somewhat obscure affair, especially as it is played in the warm summer months. Nevertheless, this course will enable you to become familiar with the language and culture of the region and get a feel for the game of today.

Roma are the current holders of the Serie A title and are currently in the process of winning the Supercup for the region. They are also in the running to win the Champions League, so you will have a good idea of what to expect from them. The game is played on grass, on the fields of Turin and on the fields of Bologna. The pitch used for the match between Roma and Napoli is made up of soft, medium and hard rubber and it is served in a traditional, locally-made way.

The setting of the match in the early stages of the game was obviously influenced by the weather in the region. The pitch was partly covered with mud, which affected the game considerably. Nevertheless, the game went on as planned and the atmosphere was excellent, particularly in the first half, when the game was won 3-2 on the bonus-time goals scored by Leonardo Dini and Andrea Pozzo.

The following day, the weather was much better and the pitch was partly covered with grass. As a result, the game went on much longer and the atmosphere even better. The following evening, the weather was even better and the atmosphere even better. So the game went on for

Course Link: https://www.coursera.org/learn/statistical-inferences

Methods and Statistics in Social Sciences Specialization

Course Link: https://www.coursera.org/specializations/social-science

Methods and Statistics in Social Sciences Specialization
This course is the last before the specialization entitled Methods and Statistics in Social Sciences. In this course, you will continue to expand your knowledge of descriptive statistics and linear regression in the context of the population and population groups. You will then apply these statistical techniques to the question of life expectancy in several advanced countries around the world. We examine the issue in detail using data from the life tables of populations. By the end of this course, you will be able to do descriptive analysis of the data and perform linear regression with appropriate adjustment for multiple variables. You will also be able to do descriptive analysis of the life expectancy data and perform linear regression with appropriate adjustment for multiple variables. You will use R® to build out your skillset and to get started with building statistical pipelines for analysis and visualization.

This is the last course in the Data Science Specialization. We highly recommend that you take the skills acquired in this course, and the other courses, to the next course in the Specialization entitled Methods and Statistics in Social Sciences, and consider applying to the Master’s in Clinical Epidemiology and Preventive Medicine at the University of Copenhagen.

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<https://www.coursera.org/specializations/social-science

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

Best Coursera Courses for Data Science

Here is a list of best coursera courses for data science.

1. Introduction to Data Science Specialization

This data science specialization provided by IBM, which include 4 sub courses. In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach them about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.

1) What is Data Science?
2) Open Source tools for Data Science
3) Data Science Methodology
4) Databases and SQL for Data Science

2. Applied Data Science Specialization

This data science specialization also provided by IBM, which include 4 sub courses. This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, and already has foundational skills (or has completed the Introduction to Applied Data Science specialization). You will learn Python – no prior programming knowledge necessary. You will then learn data visualization and data analysis. Through our guided lectures, labs, and projects you’ll get hands-on experience tackling interesting data problems. Make sure to take this specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.

1) Python for Data Science
2) Data Visualization with Python
3) Data Analysis with Python
4) Applied Data Science Capstone

3. Applied Data Science with Python Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

1) Introduction to Data Science in Python
2) Applied Plotting, Charting & Data Representation in Python
3) Applied Machine Learning in Python
4) Applied Text Mining in Python
5) Applied Social Network Analysis in Python

4. Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. The specialization includes 10 sub courses:

1) The Data Scientist’s Toolbox
2) R Programming
3) Getting and Cleaning Data
4) Exploratory Data Analysis
5) Reproducible Research
6) Statistical Inference
7) Regression Models
8) Practical Machine Learning
9) Developing Data Products
10) Data Science Capstone

5. Data Science at Scale Specialization

This data science specialization provides by University of Washington, which covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project. The specialization includes 4 sub courses:

1) Data Manipulation at Scale: Systems and Algorithms
2) Practical Predictive Analytics: Models and Methods
3) Communicating Data Science Results
4) Data Science at Scale – Capstone Project

6. Advanced Data Science with IBM Specialization

This data science specialization also provided by IBM, which include 4 sub courses. As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

1) Fundamentals of Scalable Data Science
2) Advanced Machine Learning and Signal Processing
3) Applied AI with DeepLearning
4) Advanced Data Science Capstone

7. Genomic Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. The specialization includes 8 courses:

1) Introduction to Genomic Technologies
2) Genomic Data Science with Galaxy
3) Python for Genomic Data Science
4) Algorithms for DNA Sequencing
5) Command Line Tools for Genomic Data Science
6) Bioconductor for Genomic Data Science
7) Statistics for Genomic Data Science
8) Genomic Data Science Capstone

8. Data Mining Specialization

This data science specialization provides by Illinois State University, which teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 – 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization. The specialization includes 6 courses:

1) Data Visualization
2) Text Retrieval and Search Engines
3) Text Mining and Analytics
4) Pattern Discovery in Data Mining
5) Cluster Analysis in Data Mining
6) Data Mining Project

9. Data Analysis and Interpretation Specialization

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world’s biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.

1) Data Management and Visualization
2) Data Analysis Tools
3) Regression Modeling in Practice
4) Machine Learning for Data Analysis
5) Data Analysis and Interpretation Capstone

10. Executive Data Science Specialization

Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects. In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

1)A Crash Course in Data Science
2)Building a Data Science Team
3)Managing Data Analysis
4)Data Science in Real Life
5)Executive Data Science Capstone

11. Other Useful Data Science Common Courses:

1) Data Science Math Skills
2) Data Science Ethics
3) How to Win a Data Science Competition: Learn from Top Kagglers

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