Data Cleaning and Preparation
( 24 Modules )

Module #1
Introduction to Data Cleaning
Understanding the importance of data cleaning and preparation in the data science workflow

Module #2
Data Quality Issues
Common data quality issues and their impact on analysis and modeling

Module #3
Data Profiling
Techniques for understanding and summarizing data distributions and relationships

Module #4
Handling Missing Values
Methods for detecting, imputing, and handling missing data

Module #5
Outlier Detection
Techniques for identifying and handling outliers and anomalies in data

Module #6
Data Normalization
Methods for scaling and normalizing data for analysis and modeling

Module #7
Data Transformation
Techniques for transforming data types and formats for improved analysis

Module #8
Data Aggregation
Methods for aggregating data for summarization and analysis

Module #9
Data Merging and Joining
Techniques for combining data from multiple sources

Module #10
Data Splitting and Sampling
Methods for dividing and sampling data for modeling and testing

Module #11
Data Validation
Techniques for verifying data accuracy and consistency

Module #12
Data Standardization
Methods for standardizing data formats and conventions

Module #13
Handling Duplicate and Erroneous Data
Techniques for detecting and removing duplicates and errors

Module #14
Working with Dates and Timestamps
Methods for handling and manipulating date and timestamp data

Module #15
Working with Text Data
Techniques for cleaning, tokenizing, and preprocessing text data

Module #16
Working with Categorical Data
Methods for encoding, grouping, and summarizing categorical data

Module #17
Data Visualization for Data Cleaning
Using visualization to identify and understand data quality issues

Module #18
Automating Data Cleaning
Using scripts and workflows to automate repetitive data cleaning tasks

Module #19
Data Cleaning Best Practices
Guidelines and principles for effective data cleaning and preparation

Module #20
Common Data Cleaning Tools and Software
Overview of popular data cleaning tools and software, including Excel, Python, and R

Module #21
Data Cleaning in Big Data Environments
Challenges and strategies for data cleaning in big data environments

Module #22
Data Cleaning for Machine Learning
Special considerations for data cleaning in machine learning and deep learning applications

Module #23
Data Cleaning for Data Storytelling
Using data cleaning to prepare data for effective storytelling and communication

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Data Cleaning and Preparation career

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Data Cleaning and Preparation ( 24 Modules )

Data Cleaning and Preparation
( 24 Modules )