Dataprep Fundamentals

• Basic understanding of data concepts

Audience

• Data analysts
• Database administrators
• IT professionals

Dataprep is a smart data service that facilitates the visual exploration, cleansing, and organization of both structured and unstructured data, getting it ready for analysis, reporting, and utilization in machine learning applications.

This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level IT professionals who wish to gain the knowledge and practical skills required to effectively prepare data for analysis, ensuring accuracy, consistency, and reliability in diverse datasets.

By the end of this training, participants will be able to:

• Gain a thorough understanding of data preparation's significance in ensuring high-quality, reliable data for analysis and modeling purposes.
• Acquire hands-on proficiency in data collection, cleaning, transformation, and integration techniques using real-world datasets.
• Develop the ability to identify and address data-related challenges, discrepancies, and inconsistencies effectively.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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