# AWS Certified Machine Learning: Data Analysis Fundamentals

Amazon Web Services    |    Intermediate
• 12 videos | 34m 2s
• Includes Assessment
• Earns a Badge
Rating 4.1 of 24 users (24)
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• Learn how to differentiate between categorical and numerical data types.
• 3.  Bernoulli, Uniform, and Binomial Data Distributions
In this video, you will learn how to define Bernoulli, uniform, and binomial data distributions.
• 4.  Normal, Poisson, and Exponential Data Distributions
During this video, you will learn how to define normal, Poisson, and exponential data distributions.
• 5.  The Primary Role of Data Visualization
In this video, discover how visualization can help communicate information from analyzed data.
• 6.  Data Visualization - Traditional Graphic Types
Discover how to name and describe traditional types of graphics used in data analysis.
• 7.  Data Visualization - Modern Graphic Types
In this video, you will name and describe modern graphic types used in data analysis.
• 8.  Implementing Data Visualization with Python
After completing this video, you will be able to work with Python toolkits to implement various types of data visualization.
• 9.  Time Series Analysis in Data Science
In this video, you will learn how to describe what is meant by time series analysis and its role in data science.
• 10.  Advanced Time Series Analysis Concepts
Upon completion of this video, you will be able to recognize what is meant by advanced time series analysis concepts, such as trends, seasonality, and autocorrelation.
• 11.  Implementing Time Series Analysis in Python
During this video, you will learn how to work with time series data in Python by implementing data analysis pipelines.
• 12.  Course Summary
In this video, we will summarize the key concepts covered in this course.

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

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