Training on Data Cleaning and Preprocessing Fundamentals

Course Objectives

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

• Understand the importance of data cleaning and preprocessing in the data analysis pipeline.
• Identify common data quality issues and learn techniques to address them.
• Gain hands-on experience with tools and methods for data cleaning.
• Learn how to preprocess data for various types of analyses.
• Develop skills in feature engineering to improve model performance.
• Understand the role of data transformation and normalization in data preprocessing.
• Explore best practices in handling missing data and outliers.
• Master techniques for data aggregation and merging from different sources.
• Apply data cleaning and preprocessing techniques to real-world datasets.
• Enhance data quality to ensure more accurate and reliable analysis outcomes.

Course Outline:

Module 1: Introduction to Data Cleaning and Preprocessing

• Importance of data quality in data analysis
• Data cleaning vs. preprocessing
• Data exploration and visualization techniques

Module 2: Handling Missing Data

• Types of missing data (missing completely at random, missing at random, missing not at random)
• Handling missing data techniques (deletion, imputation, modeling)

Module 3: Outlier Detection and Treatment

• Outlier identification methods (z-score, IQR, box plots)
• Outlier treatment techniques (trimming, capping, transformation)

Module 4: Data Imputation

• Imputation methods (mean/median imputation, mode imputation, hot deck imputation)
• Handling categorical and numerical missing values
• Imputation evaluation

Module 5: Data Standardization and Normalization

• Scaling techniques (min-max scaling, z-score standardization)
• Normalization techniques (log transformation, power transformation)
• Impact of scaling on data analysis

Module 6: Categorical Data Handling

• Encoding categorical variables (one-hot encoding, label encoding)
• Handling ordinal data
• Feature creation from categorical data

Module 7: Data Integration and Profiling

• Data integration challenges and solutions
• Data profiling techniques (data quality assessment, consistency checks)
• Data merging and concatenation

Module 8: Data Discretization and Binning

• Discretization methods (equal-width, equal-frequency, clustering)
• Binning techniques (binning by attribute, binning by data value)

Module 9: Feature Selection and Extraction

• Feature selection techniques (filter, wrapper, embedded methods)
• Feature engineering and creation
• Dimensionality reduction

Module 10: Data Validation and Quality Assessment

• Data validation techniques (consistency checks, range checks)
• Data profiling reports
• Continuous data monitoring and improvement

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

A certificate from Ideal Sense & Workplace Solutions is awarded upon successful completion.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to outreach@idealsense.org.
For further inquiries, please contact us on details below:

Email: outreach@idealsense.org

Mobile: +254759708394