IT Skills     Cloud Computing and Virtualization     Amazon     AWS Certified Machine Learning

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Data Analysis is a primary method for deriving valuable insight from raw and unstructured data. The appropriate application of data analysis techniques is vital in deriving only the relevant insight and factual knowledge from available data. Picking the correct data distribution or visualization technique can become critical to the overall data analysis results.
Using this course, become familiar with the core foundations of data – the essential ground for any data analysis and machine learning operation. Examine the various types of data that exist, inherent data distributions, both traditional and modern methods of visualizing data, and how time series analysis works.
When you've completed this course, you'll be able to describe the core concepts of data analysis and implement some valuable visualization and analysis techniques using Python. This course will prepare you for the AWS Certified Machine Learning - Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Data Analysis Fundamentals discover the key concepts covered in this course differentiate between categorical and numerical data types define Bernoulli, uniform, and binomial data distributions define normal, Poisson, and exponential data distributions describe the key role of visualization in communicating information from analyzed data name and describe traditional graphic types used in data analysis name and describe modern graphic types used in data analysis work with Python toolkits to implement various types of data visualization describe what's meant by time series analysis and define its role in data science recognize what's meant by advanced time series analysis concepts, such as trends, seasonality, and autocorrelation work with time series data in Python, implementing data analysis pipelines summarize the key concepts covered in this course