AWS Certified Machine Learning: Data Analysis Fundamentals
Amazon Web Services
| Intermediate
- 12 videos | 34m 2s
- Includes Assessment
- Earns a Badge
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
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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
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1m 3s
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2m 51sLearn how to differentiate between categorical and numerical data types. FREE ACCESS
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3. Bernoulli, Uniform, and Binomial Data Distributions2m 38sIn this video, you will learn how to define Bernoulli, uniform, and binomial data distributions. FREE ACCESS
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4. Normal, Poisson, and Exponential Data Distributions3m 40sDuring this video, you will learn how to define normal, Poisson, and exponential data distributions. FREE ACCESS
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5. The Primary Role of Data Visualization2m 17sIn this video, discover how visualization can help communicate information from analyzed data. FREE ACCESS
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6. Data Visualization - Traditional Graphic Types2m 12sDiscover how to name and describe traditional types of graphics used in data analysis. FREE ACCESS
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7. Data Visualization - Modern Graphic Types2m 14sIn this video, you will name and describe modern graphic types used in data analysis. FREE ACCESS
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8. Implementing Data Visualization with Python7m 33sAfter completing this video, you will be able to work with Python toolkits to implement various types of data visualization. FREE ACCESS
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9. Time Series Analysis in Data Science2m 25sIn this video, you will learn how to describe what is meant by time series analysis and its role in data science. FREE ACCESS
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10. Advanced Time Series Analysis Concepts1m 52sUpon 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. FREE ACCESS
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11. Implementing Time Series Analysis in Python4m 25sDuring this video, you will learn how to work with time series data in Python by implementing data analysis pipelines. FREE ACCESS
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12. Course Summary53sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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