Data Science: Inference and Modeling faq

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learnersLearners: 1,009
instructor Instructor: Rafael Irizarry instructor-icon
duration Duration: 2.00 duration-icon

This course will teach you the fundamentals of data science, including statistical inference and modeling, through a motivating case study on election forecasting. You will learn how to use R to define estimates and margins of errors, understand confidence intervals and p-values, and apply Bayesian modeling. At the end of the course, you will be able to recreate a simplified version of an election forecast model and apply it to the 2016 election.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Edx

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

Self paced

Course Overview

❗The content presented here is sourced directly from Edx platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [May 25th, 2023]

Data Science: Inference and Modeling is a course designed to help learners understand the fundamentals of statistical inference and modeling. Learners will gain an understanding of the concepts necessary to define estimates and margins of errors, and how to use these to make predictions. They will also learn about confidence intervals, p-values, and Bayesian modeling. Finally, learners will be able to apply their knowledge to recreate a simplified version of an election forecast model and apply it to the 2016 election. This course is ideal for those who want to gain a better understanding of the fundamentals of data science and how to use them to make predictions.

[Applications]
After completing this course, students will be able to apply the concepts of statistical inference and modeling to develop their own statistical approaches for data analysis. They will be able to use R to define estimates and margins of errors, make predictions, and understand confidence intervals and p-values. Additionally, they will be able to apply Bayesian modeling to understand statements about the probability of a candidate winning. Finally, they will be able to recreate a simplified version of an election forecast model and apply it to real-world data.

[Career Paths]
Job Position Paths:
1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights, and then using those insights to develop predictive models and algorithms. They must be able to interpret and communicate their findings to stakeholders, and use their knowledge of statistics and machine learning to develop data-driven solutions. Data Scientists are in high demand, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

2. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data to identify patterns and trends. They must be able to interpret and communicate their findings to stakeholders, and use their knowledge of statistics and data visualization to develop data-driven solutions. Data Analysts are in high demand, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

3. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models and algorithms. They must be able to interpret and communicate their findings to stakeholders, and use their knowledge of statistics and machine learning to develop data-driven solutions. Machine Learning Engineers are in high demand, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

4. Business Intelligence Analyst: Business Intelligence Analysts are responsible for collecting, organizing, and analyzing data to identify patterns and trends. They must be able to interpret and communicate their findings to stakeholders, and use their knowledge of statistics and data visualization to develop data-driven solutions. Business Intelligence Analysts are in high demand, and the demand is only increasing as more organizations recognize the value of data-driven decision making.

[Education Paths]
1. Bachelor's Degree in Data Science: A Bachelor's Degree in Data Science is a great way to gain the skills and knowledge necessary to become a successful data scientist. This degree typically covers topics such as data analysis, machine learning, data visualization, and programming. It also provides an understanding of the fundamentals of statistics and probability. As the demand for data scientists continues to grow, more universities are offering this degree, making it easier for students to pursue a career in data science.

2. Master's Degree in Data Science: A Master's Degree in Data Science is a great way to further develop your skills and knowledge in the field. This degree typically covers topics such as data mining, artificial intelligence, natural language processing, and deep learning. It also provides an understanding of the fundamentals of data engineering and data architecture. With the increasing demand for data scientists, more universities are offering this degree, making it easier for students to pursue a career in data science.

3. Doctoral Degree in Data Science: A Doctoral Degree in Data Science is the highest level of education available in the field. This degree typically covers topics such as advanced machine learning, data analytics, and data visualization. It also provides an understanding of the fundamentals of data science research and development. With the increasing demand for data scientists, more universities are offering this degree, making it easier for students to pursue a career in data science.

4. Certificate in Data Science: A Certificate in Data Science is a great way to gain the skills and knowledge necessary to become a successful data scientist. This certificate typically covers topics such as data analysis, machine learning, data visualization, and programming. It also provides an understanding of the fundamentals of statistics and probability. With the increasing demand for data scientists, more universities are offering this certificate, making it easier for students to pursue a career in data science.

Course Provider

Provider Edx's Stats at AZClass

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faq FAQ for Statistics & Probability Courses

Q1: What topics will be covered in this course?

This course will cover topics such as statistical inference, modeling, election forecasting, R programming, confidence intervals, p-values, and Bayesian modeling.

Q2: Does the course offer certificates upon completion?

Yes, this course offers a free certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q3: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q4: Can I take this course for free?

Yes, this is a free course offered by Edx, please click the "go to class" button to access more details.

Q5: How many people have enrolled in this course?

So far, a total of 1009 people have participated in this course. The duration of this course is 2.00 hour(s). Please arrange it according to your own time.

Q6: How Do I Enroll in This Course?

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