Dataprep Fundamentals Training Course
Dataprep is an intelligent data service designed to simplify the visual exploration, cleansing, and organization of both structured and unstructured data, preparing it for analysis, reporting, and use in machine learning applications.
This instructor-led live training (available online or onsite) targets beginner to intermediate IT professionals seeking to acquire the knowledge and practical skills necessary to effectively prepare data for analysis, ensuring accuracy, consistency, and reliability across diverse datasets.
Upon completion of this training, participants will be able to:
- Comprehend the critical role of data preparation in delivering high-quality, reliable data for analysis and modeling.
- Gain practical proficiency in data collection, cleaning, transformation, and integration techniques using real-world datasets.
- Identify and resolve data-related challenges, discrepancies, and inconsistencies effectively.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live lab environment.
Customization Options
- To request a customized training session for this course, please contact us to arrange details.
Course Outline
Introduction
- Understanding the importance of data preparation in analytics and machine learning
- Data preparation pipeline and its role in the data lifecycle
- Exploring common challenges in raw data and the impact on analysis
Data Collection and Acquisition
- Sources of data: databases, APIs, spreadsheets, text files, and more
- Techniques for collecting data and ensuring data quality during collection
- Collecting data from various sources
Data Cleaning Techniques
- Identifying and handling missing values, outliers, and inconsistencies
- Dealing with duplicates and errors in the dataset
- Cleaning real-world datasets
Data Transformation and Standardization
- Data normalization and standardization techniques
- Categorical data handling: encoding, binning, and feature engineering
- Transforming raw data into usable formats
Data Integration and Aggregation
- Merging and combining datasets from different sources
- Resolving data conflicts and aligning data types
- Techniques for data aggregation and consolidation
Data Quality Assurance
- Methods for ensuring data quality and integrity throughout the process
- Implementing quality checks and validation procedures
- Case studies and practical applications of data quality assurance
Dimensionality Reduction and Feature Selection
- Understanding the need for dimensionality reduction
- Techniques like PCA, feature selection, and reduction strategies
- Implementing dimensionality reduction techniques
Summary and Next Steps
Requirements
- Fundamental understanding of data concepts
Audience
- Data analysts
- Database administrators
- IT professionals
Open Training Courses require 5+ participants.
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Testimonials (2)
The variety of the information shared and the clarity to explain terms in plain English.
Arisbe Mendoza - Fairtrade International
Course - GDPR Workshop
It's a hands-on session.
Vorraluck Sarechuer - Total Access Communication Public Company Limited (dtac)
Course - Talend Open Studio for ESB
Provisional Upcoming Courses (Require 5+ participants)
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