1. Data Science 101 - Your First Analysis in R and Python

Summary:

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This course is meant to get you up to speed on performing a data analysis with R and Python. This course contains programmatic instruction in these programs, but also provides solid theoretical foundation to carry out the analysis process. This course is unlike any out there in that it is not a programming only course. The goal here is knowledge retention. The instructional style is approachable and seeks to help the student retain the knowledge being trained through assessments and applications. The course capstone gives the student a real life data problem to solve.

What You Will Learn:

  1. Why analysis is important

  2. The data science process

  3. Getting started with R and Python

  4. Math fundamentals for data science

  5. Statistical theory for data science

  6. Exploratory data analysis

  7. Data cleaning and manipulation

  8. Inference based testing

  9. Model based methods such as Linear regression

  10. Categorical data analysis

  11. Next steps in your learning path

Who Should Take This Course?

Data analysts, aspiring data scientists, managers, aspiring programmers.

Pre-Requisites:

None. Just general curiosity in data analysis.

Course Mediums:

1. Web Based (self-paced)

2. Corporate Training

Syllabus:

Coming Soon

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2. Machine Learning White Belt

Summary:

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This course provides a soft introduction to machine learning. The course is completed with the EZ Learner App and no coding is required. In this course, you will learn how to format your data for upload, upload data into the app, inspect and clean the data, perform exploratory data analysis, determine problem type, overview of popular models, training your data, training analytics, uploading testing data and predicting with your model. The student will gain hands on experience with machine learning that is more application based. Upon completion the student should be able to operate the EZ Learner app and complete the machine learning process from beginning to end. The instructional style is approachable and seeks to help the student retain the knowledge being trained through assessments and applications. The course capstone gives the student a real life data problem to solve. Upon completion, the student will be certified with a Machine Learning White Belt. 

Who Should Take This Course?

Data analysts, aspiring data scientists, managers, business executives, students, those in non-statistical fields wishing to utilize machine learning, aspiring programmers.

Pre-Requisites:

None. Just general curiosity in machine learning

Course Mediums:

1. Web Based (self-paced)

2. Corporate Training

Syllabus:

Coming Soon

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Name

3. Machine Learning Green Belt

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Summary:

This course provides a more thorough introduction to machine learning than the white belt course. The courses utilizes both R and Python to perform machine learning and teaches the student hands on how to code a machine learning pipeline. In this course you will learn to frame your machine learning problem, load and inspect data, prepare your data for learning, perform exploratory data analysis, variance and bias trade off, overfitting and underfitting, determining your problem type, split the data into test and train data, re-sampling methods, theoretical and practical application of top machine learning models, interpreting your model statistics, predicting on test data and serializing your models. After the completion of this course, the student should be able to code a machine learning pipeline from beginning to end in R and Python. The instructional style is approachable and seeks to help the student retain the knowledge being trained through assessments and applications. The course capstone gives the student a real life data problem to solve. Upon completion, the student will be certified with a Machine Learning Green Belt. 

Who Should Take This Course?

Data analysts, aspiring data scientists, managers, business executives, students, those in non-statistical fields wishing to utilize machine learning, aspiring programmers.

Pre-Requisites:

Preferable: Completion of Your First Analysis in R and Python or general knowledge of R and Python programming. This course will not cover installing and navigating these software packages.

Course Mediums:

1. Web Based (self-paced)

2. Corporate Training

Syllabus:

Coming Soon

Name *
Name