Description

Introduction

Data preparation is a crucial phase in the machine learning process. As the foundation of any successful model, well-prepared data leads to better insights and more accurate predictions. The BigML Certified Analyst course is specifically designed to teach the fundamental concepts and best practices for data preparation using BigML’s platform.

This course covers a variety of essential topics in data wrangling, including data cleaning, feature engineering, and transformation techniques. By the end of this training, participants will be able to prepare high-quality datasets that will power effective machine learning models and contribute to successful business outcomes.

Prerequisites

• Basic understanding of machine learning concepts.
• Familiarity with working with datasets and performing simple data analysis.
• No prior experience with BigML is required.

Table of Contents

1. Introduction to BigML and Data Preparation
   • 1.1 Overview of BigML for Analysts
     • 1.1.1 The BigML Platform and Its Tools
     • 1.1.2 The Role of Data Analysts in Machine Learning
   • 1.2 Understanding the Importance of Data Quality
     • 1.2.1 How Data Quality Impacts Model Performance
     • 1.2.2 Common Data Issues and Their Solutions
2. Loading and Exploring Data in BigML
   • 2.1 Uploading and Managing Datasets
     • 2.1.1 Supported Data Formats
     • 2.1.2 Organizing and Labeling Datasets
   • 2.2 Exploring and Visualizing Your Data
     • 2.2.1 Data Profiling in BigML
     • 2.2.2 Identifying Patterns and Outliers
3. Data Cleaning and Transformation
   • 3.1 Handling Missing Values
     • 3.1.1 Imputation Methods
     • 3.1.2 Removing or Replacing Missing Data
   • 3.2 Data Normalization and Standardization
     • 3.2.1 Scaling Features for Consistency
     • 3.2.2 Dealing with Skewed Distributions
   • 3.3 Removing Duplicates and Redundancies
     • 3.3.1 Techniques for Detecting Duplicates
     • 3.3.2 Ensuring Data Integrity
4. Feature Engineering for Improved Model Performance
   • 4.1 Selecting Relevant Features
     • 4.1.1 Correlation Analysis
     • 4.1.2 Feature Selection Techniques
   • 4.2 Creating New Features from Existing Data
     • 4.2.1 Encoding Categorical Data
     • 4.2.2 Feature Extraction Methods(Ref: Becoming a BigML Certified Architect: Practical Insights into ML Engineering)
5. Handling Imbalanced Datasets
   • 5.1 Identifying and Understanding Class Imbalance
     • 5.1.1 Why Imbalance Affects Model Performance
     • 5.1.2 Metrics for Imbalanced Data
   • 5.2 Techniques for Balancing Data
     • 5.2.1 Sampling Methods (Over-sampling, Under-sampling)
     • 5.2.2 Synthetic Data Generation
6. Data Preprocessing and Pipelines
   • 6.1 Automating the Data Preprocessing Workflow
     • 6.1.1 Building Efficient Pipelines in BigML
     • 6.1.2 Managing Preprocessing in Large Datasets
   • 6.2 Data Transformation for Model Training
     • 6.2.1 Data Conversion and Splitting
     • 6.2.2 Preparing Data for Real-Time Predictions
7. Case Studies: Data Preparation in Real-World Applications
   • 7.1 Preparing Data for Predictive Analytics
     • 7.1.1 Forecasting with Time Series Data
     • 7.1.2 Anomaly Detection in IoT Data
   • 7.2 Use Case: Preparing Data for Customer Segmentation
8. Preparing for the BigML Certified Analyst Exam
   • 8.1 Overview of the Certification Process
   • 8.2 Best Practices and Resources for Exam Success

Conclusion

Data preparation is the key to unlocking the true potential of machine learning. The BigML Certified Analyst certification provides essential skills and knowledge that will enable you to clean, transform, and engineer data effectively. With BigML’s user-friendly tools and this structured training, you can be confident in your ability to prepare datasets that lead to more accurate and reliable machine learning models.

Becoming certified will set you apart as an expert in data preparation, a critical skill for any data-driven organization. Start your journey to becoming a BigML Certified Analyst and enhance your data preparation capabilities today!

Reference