Specialized Models: Time Series and Survival Analysis

Course Overview

What skills and knowledge will you acquire during this course?
Completing this course will equip learners with the skills and knowledge to analyze time series data, decompose time series data, select and implement various time series models, and understand hazard and survival modeling approaches. Learners will also gain a better understanding of Python development environments, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics. These skills and knowledge can be applied to further development in the field of Machine Learning, such as Natural Language Processing, Computer Vision, and Reinforcement Learning, or to pursue a career in Data Science, such as Data Analyst, Data Engineer, or Data Scientist.

How does this course contribute to professional growth?
This course contributes to professional growth by providing learners with the knowledge and skills necessary to pursue further development in the field of Machine Learning. Learners will gain hands-on experience with topics such as Time Series Analysis and Survival Analysis, and learn how to identify common modeling challenges with time series data, decompose time series data, select and implement various time series models, and understand hazard and survival modeling approaches. This course also provides learners with the foundational understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics, which are essential for pursuing a career in Data Science, such as Data Analyst, Data Engineer, or Data Scientist.

Is this course suitable for preparing further education?
Specialized Models: Time Series and Survival Analysis is a suitable course for preparing further education in the field of Machine Learning. Learners will gain hands-on experience with topics such as Time Series Analysis and Survival Analysis, and learn how to identify common modeling challenges with time series data, decompose time series data, select and implement various time series models, and understand hazard and survival modeling approaches. Learners should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics. After completing this course, learners may pursue further development in the field of Machine Learning, such as Natural Language Processing, Computer Vision, and Reinforcement Learning. They may also pursue a career in Data Science, such as Data Analyst, Data Engineer, or Data Scientist.