Data Wrangling with Python Assessment Test

A Data Wrangling with Python Assessment Test evaluates candidates on their proficiency in using Python for preparing, cleaning, and transforming raw data into a usable format for analysis and visualization. Data wrangling is essential in data science and analytics to ensure data quality and usability for various applications.

A Data Wrangling with Python Assessment Test typically evaluates candidates for:

1. Data Cleaning: Ability to identify and handle missing values, outliers, and inconsistencies in datasets using Python libraries such as pandas. Proficiency in techniques like imputation, filtering, and normalization to ensure data integrity.
2. Data Transformation: Skills in transforming data formats (e.g., CSV, JSON, Excel) and structures (e.g., wide to long format) using pandas or other relevant Python libraries. Experience with functions like apply(), map(), and vectorized operations for efficient data manipulation.
3. Data Integration: Capability to merge, join, and concatenate multiple datasets from different sources using pandas. Knowledge of inner joins, outer joins, and concatenation methods to ensure data consistency and alignment.
4. Data Extraction: Proficiency in extracting relevant data subsets from large datasets using SQL queries or NoSQL queries with libraries like SQLAlchemy. Experience in retrieving data from databases and APIs using Python.
5. Feature Engineering: Ability to create new features or variables derived from existing data to enhance predictive models. Knowledge of feature scaling, transformation, and selection techniques using libraries like scikit-learn or pandas.
6. Handling Time-Series Data: Skills in managing and analyzing time-series data using pandas for timestamp manipulation, rolling calculations, and seasonality adjustments. Familiarity with time-series libraries like datetime and time series analysis techniques.
7. Text Data Wrangling: Experience in preprocessing and cleaning text data for natural language processing (NLP) tasks using Python libraries such as NLTK or spaCy. Proficiency in techniques like tokenization, stemming, and sentiment analysis.
8. Handling Big Data: Understanding of techniques and tools for data wrangling at scale using distributed computing frameworks like Apache Spark with Python (PySpark). Proficiency in handling large volumes of data efficiently and performing data transformations in distributed environments.
9. Data Quality Assessment: Knowledge of methods to assess data quality, including data profiling, outlier detection, and validation against predefined rules or constraints using Python libraries. Experience with data profiling tools and metrics.
10. Data Wrangling Automation: Ability to automate data wrangling tasks using Python scripts, shell scripting, or workflow automation tools (e.g., Apache Airflow). Experience in scripting repetitive data cleaning and transformation processes.
11. Version Control and Documentation: Capability to maintain version control of data transformation processes using Git and document data wrangling steps, assumptions, and decisions in Jupyter Notebooks or Markdown files for reproducibility and auditability.
12. Data Governance and Compliance: Awareness of data governance principles, regulatory requirements (e.g., GDPR, HIPAA), and ethical considerations in data handling and transformation using Python.

Overall, a Data Wrangling with Python Assessment Test measures candidates' ability to effectively clean, transform, and prepare data for analysis using Python, ensuring data quality and usability in decision-making processes. It provides valuable insights for hiring managers seeking data professionals proficient in Python for data wrangling tasks across industries such as data science, business intelligence, and analytics.