People Analytics Analyst — Data Wrangling & Cleaning
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What you'll learn:
Deep dive into advanced data profiling techniques using Python and Pandas to understand data quality challenges in HR datasets. This includes identifying missing values, outliers, data inconsistencies, and data type issues specific to people analytics contexts like compensation, performance ratings, and employee demographics. Focus on automating the profiling process. - **Description:** Learn advanced Pandas functions for data profiling, including `.describe()`, `.info()`, `.value_counts()`, `.isnull().sum()`, and custom functions for detecting anomalies. Explore techniques for handling mixed data types, and creating visualizations with libraries like Seaborn and Matplotlib to identify trends and potential data quality problems in HR data. Focus on real-world examples using datasets on employee attrition, salary analysis, or performance reviews. Learn to develop data profiling reports. - **Resources:** - **Activities:** - **Expected Outcomes:** Proficient use of data profiling tools and techniques, ability to generate comprehensive data profiling reports, ability to identify and quantify data quality problems in HR data and the capacity to develop automated data profiling scripts.
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What you'll learn:
Deduplication and Standardization** - Mastering advanced data cleaning techniques focusing on handling duplicate records, address standardization, name parsing, and other advanced data issues often found in HR data. - **Description:** Learn to identify and handle duplicate records using fuzzy matching techniques and advanced string manipulation with libraries like FuzzyWuzzy and RecordLinkage, which are essential for cleaning HR data. Explore methods for standardizing addresses, names (e.g., first/last name separation, handling middle names/initials), job titles, and company names using regular expressions and custom functions. Apply techniques to combine and reconcile data from different HR systems. - **Resources:** - **Activities:** - **Expected Outcomes:** Expertise in deduplication, standardization and data merging techniques for people analytics, skill in applying fuzzy matching, regular expressions, and string manipulation for advanced data cleaning. Proficiency in automating data cleaning workflows.
Personal Notes:
What you'll learn:
Focus on preparing data for analysis and building useful features from raw HR data. This will include creating features for performance, tenure, compensation, and other areas. - **Description:** Learn how to create insightful features from existing data, such as calculating employee tenure, experience, performance scores, salary growth rates, promotion frequency, and the time between key events (e.g., hire date to performance review, promotion date). Learn to handle time series data commonly found in HR datasets and understand how to transform data for downstream analysis like machine learning. This involves applying techniques like binning, one-hot encoding, and feature scaling. Focus on applying these feature engineering techniques to real-world HR problems. - **Resources:** - **Activities:** - **Expected Outcomes:** Mastery of feature engineering techniques, the ability to create new, informative features from raw data, a strong understanding of how to transform data for use in various analytic methods, and proficiency in constructing feature pipelines.
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What you'll learn:
Imputation, Outlier Treatment, and Missing Value Strategies** - Addressing and implementing strategies for dealing with data imperfections and missing data in the context of people analytics. - **Description:** Explore advanced imputation methods for missing data, considering different data types and the nature of the missingness (MCAR, MAR, MNAR). Learn to deal with outliers using robust statistical methods. The emphasis is on understanding when and how to apply various imputation techniques (e.g., mean/median imputation, k-NN imputation, regression imputation) and robust outlier handling methods, and how these choices affect the integrity of your analyses. Explore the use of statistical methods like Winsorizing and trimming in outlier treatment. Consider the business context and the impact of the data cleaning choices. - **Resources:** - **Activities:** - **Expected Outcomes:** Skill in applying advanced imputation techniques and understanding their implications, proficiency in outlier detection and handling, ability to assess the effectiveness of data cleaning strategies, knowledge of data ethics and responsible data handling.
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What you'll learn:
Building Robust Data Cleaning Pipelines** - Learn how to ensure data quality and build automated data cleaning workflows that can be integrated into regular operations. - **Description:** Learn how to design and build data validation rules and automated data quality checks. Develop robust data cleaning pipelines using Python libraries like `Pandas`, `Dask`, and `Prefect`. Learn to implement automated data validation checks, including range checks, format validation, consistency checks across tables, and referential integrity checks. Learn to set up data quality monitoring, creating alerts and reports to proactively identify and address data quality issues. - **Resources:** - **Activities:** - **Expected Outcomes:** Skill in building and deploying automated data cleaning pipelines, the ability to implement data validation rules and set up quality control measures, understanding of workflow orchestration, improved data quality assurance practices.
Personal Notes:
What you'll learn:
Mastering SQL techniques for cleaning and transforming data, with specific emphasis on SQL-based data cleaning for HR-related data. - **Description:** This day focuses on using SQL (e.g., PostgreSQL, MySQL, or similar) to perform data cleaning and transformation tasks, which can be essential when working with data stored in databases. Explore advanced SQL functions such as string functions (e.g., `SUBSTRING`, `REPLACE`, `TRIM`, regular expressions) to standardize data, aggregate functions (e.g., `SUM`, `AVG`, `COUNT`) for data profiling, and window functions (e.g., `ROW_NUMBER()`, `RANK()`, `LAG()`) for feature engineering (e.g., calculating time differences, trends). Includes writing complex SQL queries for joins, filtering, and data aggregation essential for cleaning HR data from multiple tables. - **Resources:** - **Activities:** - **Expected Outcomes:** Expertise in using advanced SQL techniques for data cleaning and transformation, proficiency in writing complex SQL queries for handling HR data, the ability to build and automate data cleaning processes within a database environment.
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What you'll learn:
Emphasis on ethical considerations and creating processes that protect employee privacy and data integrity. - **Description:** Understand the ethical and legal considerations surrounding data privacy, data security, and responsible data usage in people analytics. Focus on data anonymization techniques, including masking, generalization, and differential privacy. Explore best practices for data storage, access control, and data governance within the context of HR data. Explore the potential biases that may be present in data and how this might impact the results of HR data analysis. - **Resources:** - **Activities:** - **Expected Outcomes:** Thorough understanding of data ethics and responsible data practices, proficiency in implementing data anonymization techniques and data governance principles, and an increased awareness of the importance of ethical considerations in people analytics.
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