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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.