Master techniques for acquiring, exploring, and transforming data to prepare it for analysis.
Covers using data acquisition methods including data integration techniques and queries to gather and combine data from multiple sources. Includes performing data exploration to find missing values, duplication, redundancy, or outliers in datasets. Also covers applying data transformation techniques including data cleansing, merging, parsing, and formatting to ensure data quality and consistency before analysis.
5 minutes
5 Questions
Data Acquisition and Preparation forms the foundation of the analytics lifecycle, representing a critical domain within the CompTIA Data+ V2 certification objectives. This phase involves gathering raw data from various sources and transforming it into a clean, usable format for analysis.
**Data Acquisition** focuses on identifying and connecting to relevant data sources. A data analyst must understand diverse data structures, including relational databases (SQL), non-relational databases (NoSQL), flat files (CSV, Excel), and web APIs. Key competencies involve writing efficient SQL queries to extract specific datasets, understanding database schemas, and strictly adhering to data governance and privacy standards (such as handling PII/PHI) during the extraction process.
**Data Preparation**, often referred to as data wrangling, is where analysts typically invest the majority of their time. Raw data rarely arrives in a pristine state. This stage involves several critical workflows:
1. **Data Profiling:** Assessing the quality and structure of the data to identify anomalies, outliers, and schema mismatches.
2. **Data Cleaning:** Addressing issues such as handling missing values (through imputation or removal), correcting inconsistent formatting (e.g., standardizing date formats), and removing duplicate records.
3. **Data Transformation:** Modifying data to fit analytical needs. This includes recoding values, creating calculated fields (derived variables), merging or joining multiple datasets to create a unified view, and pivoting/unpivoting tables.
In the Data+ V2 context, proficiency in ETL (Extract, Transform, Load) and ELT processes is essential. Candidates must demonstrate the ability to automate these workflows and ensure data quality dimensions—accuracy, completeness, consistency, timeliness, and validity—are met. Without rigorous acquisition and preparation, subsequent visualizations and reports will rely on compromised data, leading to inaccurate insights.Data Acquisition and Preparation forms the foundation of the analytics lifecycle, representing a critical domain within the CompTIA Data+ V2 certification objectives. This phase involves gathering raw data from various sources and transforming it into a clean, usable format for analysis.
**Data Ac…