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Data Preparation for Predictive Analytics
This one-day session will expose analytics practitioners, data scientists, and those looking to get started in predictive analytics to the critical importance of selecting, transforming, and properly preparing data ahead of model building.
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Hands-on Data Manipulation and Cleaning in Python
Data manipulation and cleaning in machine learning is estimated to take more than 50% of the time allotted for any given machine learning project. This course will cover topics important in handling structured and unstructured data, scraping data in Python, including using key packages such as Pandas, NumPy, and Matplotlib.
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Introduction to Data Wrangling
This course addresses how to translate the problem statement, identifying data sources, exploring the data for relationships and recognize patterns, identifying the starting inputs for the model, preparing data, and validating it for the model fitting process.
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TDWI Data Quality Management
This course is designed to help your organization better understand and successfully tackle your data quality challenges.
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Data Science Bootcamp // Data Sourcing and Preparation for Data Science
In this session, we will provide an overview of sourcing and preparing data for data science and predictive analytics projects. We will use a motivating example from the speaker’s work and also touch on how Python, SQL, and Hadoop can be used in the data preparation workflow.
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TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement
The only proven path to sustainable data quality is through a comprehensive quality management program that includes data profiling, data quality assessment, root cause analysis, data cleansing, and process improvement.
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Data Science Bootcamp Day 2:
Supervised and Unsupervised Modeling
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TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement
The only proven path to sustainable data quality is through a comprehensive quality management program that includes data profiling, data quality assessment, root cause analysis, data cleansing, and process improvement.
more
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TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement
The only proven path to sustainable data quality is through a comprehensive quality management program that includes data profiling, data quality assessment, root cause analysis, data cleansing, and process improvement.
more
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Data Science Bootcamp Day 2:
Supervised and Unsupervised Modeling
You will learn how to explain models and model accuracy to business stakeholders. Model interpretation strategies and metrics for complex algorithms will be also be described, equipping you with the communication techniques needed to generate business value.
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