PCED™ – Certified Entry-Level Data Analyst with Python: EXAM SYLLABUS

PCED-30-01 Exam Syllabus

Status: ACTIVE

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The exam is broken down into five blocks:

Block 1 → 15 items, Weight: 33%
Block 2 → 13 items, Weight: 29%
Block 3 → 4 items, Weight: 9%
Block 4 → 7 items, Weight: 16%
Block 5 → 6 items, Weight: 13%

Objectives covered by the block (15 exam items)

Data Collection, Integration, and Storage

  • Objective 1.1.1 – Understand different data collection methods and their roles in decision-making and research.
    • Explore different techniques: Surveys, interviews, web scraping.
    • Discuss representative sampling, challenges in data collection, and differences between qualitative and quantitative research.
    • Examine legal and ethical considerations in data collection.
    • Explain the importance of data anonymization in maintaining privacy and confidentiality, particularly with personally identifiable information (PII).
    • Investigate the impact of data collection on business strategy formation, market research accuracy, risk assessment, policy-making, and business decisions.
  • Objective 1.1.2 – Explain the data gathering process and various data sources.
    • Explain the process and methodologies of data collection, including survey design, audience selection, and structured interviews.
  • Objective 1.1.3 – Aggregate data from multiple sources and integrate them into datasets.
    • Explain techniques for combining data from various sources, such as databases, APIs, and file-based storage.
    • Address challenges in data aggregation, including data format disparities and alignment issues.
    • Understand the importance of data consistency and accuracy in aggregated datasets.
  • Objective 1.1.4 – Explain various data storage solutions.
    • Understand various data storage methods and their appropriate applications.
    • Distinguish between the concepts of data warehouses, data lakes, and file-based storage options like CSV and Excel.
    • Explain the concepts of cloud storage solutions and their growing role in data management.

Data Cleaning and Standardization

  • Objective 1.2.1 – Understand structured and unstructured data and their implications in data analysis.
    • Recognize the characteristics of structured data, such as databases and spreadsheets, and their straightforward use in analysis.
    • Understand unstructured data, including text, images, and videos, and the additional processing required for analysis.
    • Explore how the data structure impacts data storage, retrieval, and analytical methods.
  • Objective 1.2.2 – Identify, rectify, or remove erroneous data.
    • Identify data errors and inconsistencies through various diagnostic methods.
    • Address missing, inaccurate, or misleading information.
    • Tackle specific data quality issues: numerical data problems, duplicate records, invalid data entries, and missing values.
    • Explain different types of missingness (MCAR, MAR, MNAR), and their implications for data analysis.
    • Explore various techniques for dealing with missing data, including data imputation methods.
    • Understand the implications of data correction or removal on overall data integrity and analysis outcomes.
    • Explain the importance of data collection in the context of outlier detection.
    • Explain why high-quality data is crucial for accurate outlier detection.
    • Explain how different data types (numerical, categorical) may influence outlier detection strategies.
  • Objective 1.2.3 – Understand data normalization and scaling.
    • Understand the necessity of data normalization to bring different variables onto a similar scale for comparative analysis.
    • Understand various scaling methods like Min-Max scaling and Z-score normalization.
    • Explain encoding categorical variables for quantitative analysis, including one-hot encoding and label encoding methods.
    • Explain the pros and cons of data reduction (reduce the number of variables under consideration or simplify the models vs loss of data explainability).
    • Explain methods for handling outliers, including detection and treatment techniques to ensure data quality.
    • Understand the importance of data format standardization across different datasets for consistency, especially when dealing with date-time formats and numerical values.
  • Objective 1.2.4 – Apply data cleaning and standardization techniques.
    • Perform data imputation techniques, string manipulation, data format standardization, boolean normalization, string case normalization, and string-to-number conversions.
    • Discuss the pros and cons of imputation vs. exclusion and their impact on the reliability and validity of the analysis.
    • Explain the concept of One-Hot Encoding and its application in transforming categorical variables into a binary format, and preparing data for machine learning algorithms.
    • Explain the concept of bucketization and its application in transforming continuous variables into categorical variables.

Data Validation and Integrity

  • Objective 1.3.1 – Execute and understand basic data validation methods.
    • Perform type, range, and cross-reference checks.
  • Objective 1.3.2 – Establish and maintain data integrity through clear validation rules.
    • Understand the concept of data integrity and its importance in maintaining reliable and accurate databases.
    • Apply clear validation rules that enforce the correctness and consistency of data.

Data Preparation Techniques

  • Objective 1.4.1 – Understand File Formats in Data Acquisition.
    • Explain the roles and characteristics of common data file formats: CSV for tabular data, JSON for structured data, XML for hierarchically organized data, and TXT for unstructured text.
    • Understand basic methods for importing and exporting these file types in data analysis tools, focusing on practical applications.
  • Objective 1.4.2 – Access, manage, and effectively utilize datasets.
    • Understand the basics of accessing datasets from various sources like local files, databases, and online repositories.
    • Understand the principles of data management, including organizing, sorting, and filtering data in preparation for analysis.
  • Objective 1.4.3 – Extract data from various sources.
    • Explain fundamental techniques for extracting data from various sources, emphasizing methods to retrieve and collate data from databases, APIs, and online services.
    • Understand basic challenges and considerations in data extraction, such as data compatibility and integrity.
  • Objective 1.4.4 – Enhance data readability and format in spreadsheets.
    • Improve the readability and usability of data in spreadsheets, focusing on layout adjustments, formatting best practices, and basic formula applications.
  • Objective 1.4.5 – Prepare, adapt, and pre-process data for analysis.
    • Understand the importance of the surrounding context, objectives and stakeholder expectations to guide the preparation steps.
    • Understand basic concepts of data pre-processing, including sorting, filtering, and preparing data sets for analytical work.
    • Discuss the importance of proper data formatting for analysis, such as ensuring consistency in date-time formats and aligning data structures.
    • Introduce concepts of dataset structuring, including the basics of transforming data into a format suitable for analysis (e.g., wide vs. long formats).
    • Explain the concept of splitting data into training and testing sets, particularly for machine learning projects, emphasizing the importance of this step for model validation.
    • Understand the impact of outlier management on data quality in preprocessing.

Objectives covered by the block (13 exam items)

Python Proficiency

  • Objective 2.1.1 – Apply Python syntax and control structures to solve data-related problems.
    • Accurately use basic Python syntax for variables, scopes, and data types.
    • Implement control structures like loops and conditionals to manage data flow.
  • Objective 2.1.2 – Analyze and create Python functions.
    • Design functions with clear purpose, using both indexed and keyword arguments.
    • Differentiate between optional and required arguments and apply them effectively.
  • Objective 2.1.3 – Evaluate and navigate the Python Data Science ecosystem.
    • Identify key Python libraries and tools essential for data science tasks.
    • Critically assess the suitability of various Python resources for different data analysis scenarios.
  • Objective 2.1.4 – Organize and manipulate data using Python's core data structures.
    • Effectively use tuples, sets, lists, dictionaries, and strings for data organization and manipulation.
    • Solve complex data handling tasks by choosing appropriate data structures.
  • Objective 2.1.5 – Explain and implement Python scripting best practices.
    • Understand and apply PEP 8 guidelines for Python coding style.
    • Comprehend and utilize PEP 257 for effective docstring conventions to enhance code documentation.

Module Management and Exception Handling

  • Objective 2.2.1 – Import modules and manage Python packages using PIP.
    • Apply different types of module imports (standard imports, selective imports, aliasing).
    • Understand importing modules from different sources (Python Standard Library, via package managers like PIP, and from locally developed modules/packages).
    • Identify and import necessary Python modules for specific tasks, understanding the functionality and purpose of each.
    • Demonstrate proficiency in managing Python packages using PIP, including installing, updating, and removing packages.
  • Objective 2.2.2 – Apply basic exception handling and maintain script robustness.
    • Implement basic exception handling techniques to manage and respond to errors in Python scripts.
    • Predict common errors in Python code and develop strategies to handle them effectively.
    • Interpret error messages to diagnose and resolve issues, enhancing the robustness and reliability of Python scripts.

SQL for Data Analysts

  • Objective 2.3.1 – Perform SQL queries to retrieve and manipulate data.
    • Compose and execute SQL queries to extract data from database tables.
    • Apply SQL functions and clauses to manipulate and filter data effectively.
    • Construct and execute SQL queries using SELECT, FROM, JOINS (INNER, LEFT, RIGHT, FULL), WHERE, GROUP BY, HAVING, ORDER BY, and LIMIT.
    • Analyze data retrieval needs and apply appropriate clauses from the SFJWGHOL set to meet those requirements effectively.
  • Objective 2.3.2 – Execute fundamental SQL commands to create, read, update, and delete data in database tables.
    • Demonstrate the ability to use CRUD operations (Create, Read, Update, Delete) in SQL.
    • Construct SQL statements for data insertion, retrieval, updating, and deletion.
  • Objective 2.3.3 – Establish connections to databases using Python.
    • Understand and implement methods to establish database connections using Python libraries (e.g., sqlite3, pymysql).
    • Analyze and resolve common issues encountered while connecting Python scripts to databases.
  • Objective 2.3.4 – Execute parameterized SQL queries through Python to safely interact with databases.
    • Develop and execute parameterized SQL queries in Python to interact with databases securely.
    • Evaluate the advantages of parameterized queries in preventing SQL injection and maintaining data integrity.
  • Objective 2.3.5 – Understand, manage and convert SQL data types appropriately within Python scripts.
    • Identify and understand various SQL data types and their counterparts in Python.
    • Practice converting data types appropriately when transferring data between SQL databases and Python scripts.
  • Objective 2.3.6 – Understand essential database security concepts, including strategies to prevent SQL query injection.
    • Comprehend fundamental database security principles, including measures to prevent SQL injection attacks.
    • Assess and apply strategies for writing secure SQL queries within Python environments.

Objectives covered by the block (4 exam items)

Descriptive Statistics

  • Objective 3.1.1 – Understand and apply statistical measures in data analysis.
    • Understand and describe measures of central tendency and spread.
    • Identify fundamental statistical distributions (Gaussian, Uniform) and interpret their trends in various contexts (over time, univariate, bivariate, multivariate).
    • Apply confidence measures in statistical calculations to assess data reliability.
  • Objective 3.1.2 – Analyze and evaluate data relationships.
    • Analyze datasets to identify outliers and evaluate negative and positive correlations using Pearson’s R coefficient.
    • Interpret and critically assess information presented in various types of plots and graphs, including Boxplots, Histograms, Scatterplots, Lineplots, and Correlation heatmaps.

Inferential Statistics

  • Objective 3.2.1 – Understand and apply bootstrapping for sampling distributions.
    • Understand the theoretical basis and statistical principles underlying bootstrapping.
    • Differentiate between discrete and continuous data types in the context of bootstrapping.
    • Recognize situations and data types where bootstrapping is an effective method for estimating sampling distributions.
    • Demonstrate proficiency in applying bootstrapping methods using Python to generate and analyze sampling distributions.
    • Analyze the reliability and validity of results obtained from bootstrapping in various statistical scenarios.
  • Objective 3.2.2 – Explain when and how to use linear and logistic regression.
    • Comprehend the theory, assumptions, and mathematical foundation of linear regression.
    • Explain the concepts, use cases, and statistical underpinnings of logistic regression.
    • Develop the ability to choose between linear and logistic regression based on the nature of the data and the research question.
    • Apply the concepts of discrete and continuous data in choosing and implementing linear and logistic regression models.
    • Demonstrate the application of linear and logistic regression models on datasets using Python, including parameter estimation and model fitting.
    • Accurately interpret the outcomes of regression analyses, including coefficients and model fit statistics.
    • Identify limitations, assumptions, and potential biases in linear and logistic regression models and their impact on results.

Objectives covered by the block (7 exam items)

Data Analysis with Pandas and NumPy

  • Objective 4.1.1 – Manage data effectively with Pandas.
    • Organize and clean data using Pandas' data manipulation tools (like filtering, sorting, and handling missing values).
    • Apply advanced data manipulation techniques such as merging, joining, and reshaping data frames.
  • Objective 4.1.2 – Understand and Utilize the Relationship Between DataFrame and Series in Pandas.
    • Explain the conceptual differences and connections between DataFrames and Series in Pandas.
    • Implement indexing methods and use vectorized functions for efficient data manipulation.
    • Practice locating data using .iloc and .loc methods, and analyze the outcomes to ensure accurate data retrieval and manipulation.
  • Objective 4.1.3 – Perform Array Operations and Differentiate Data Structures with NumPy.
    • Execute array operations using NumPy, including basic arithmetic, broadcasting, and aggregation functions.
    • Distinguish between arrays, lists, NDArrays, Series, and DataFrames, understanding their respective use cases and performance characteristics.
    • Analyze and compare the efficiency and suitability of these data structures for different types of data analysis tasks.
  • Objective 4.1.4 – Apply and Analyze Data Organization Techniques in Pandas and NumPy.
    • Apply methods for reshaping data, including subsetting and sorting, in Pandas.
    • Analyze datasets by grouping data using groupby and creating pivot/cross tables for enhanced data comprehension.
    • Compute and interpret descriptive statistics using Pandas and NumPy to extract meaningful insights from data.

Statistical Methods and Machine Learning

  • Objective 4.2.1 – Apply Python's descriptive statistics for dataset analysis.
    • Calculate and interpret key statistical measures such as mean, median, mode, variance, and standard deviation using Python.
    • Utilize Python libraries (like Pandas and NumPy) to generate and analyze descriptive statistics for real-world datasets.
  • Objective 4.2.2 – Recognize the importance of test datasets in model evaluation.
    • Understand the role of test datasets in validating the performance of machine learning models.
    • Demonstrate knowledge of proper test dataset selection and usage to ensure unbiased and accurate model evaluation.
  • Objective 4.2.3 – Analyze and Evaluate Supervised Learning Algorithms and Model Accuracy.
    • Analyze various supervised learning algorithms to understand their specific characteristics and applications.
    • Evaluate the concepts of overfitting and underfitting within these models, including a detailed explanation of the bias-variance tradeoff.
    • Assess the intrinsic tendencies of linear and logistic regression in relation to this tradeoff, and apply this understanding to prevent model accuracy issues.

Objectives covered by the block (6 exam items)

Data Visualization Techniques

  • Objective 5.1.1 – Demonstrate essential proficiency in data visualization with Matplotlib and Seaborn.
    • Utilize Matplotlib and Seaborn to create various types of plots, including Boxplots, Histograms, Scatterplots, Lineplots, and Correlation heatmaps.
    • Interpret the data and findings represented in these visualizations to gain deeper insights and communicate results effectively.
  • Objective 5.1.2 – Assess the pros and cons of different data representations.
    • Evaluate the suitability of various chart types for different types of data and analysis objectives.
    • Critically analyze the effectiveness of chosen visualizations in conveying the intended message or insight.
  • Objective 5.1.3 – Label, annotate, and test insights from data visualizations.
    • Incorporate labels, titles, and annotations in visualizations to clarify and emphasize key insights.
    • Utilize visual exploration to generate hypotheses and test insights from datasets.
    • Practice making data-driven decisions based on the interpretation of visualized data.
  • Objective 5.1.4 – Improve the clarity and accuracy of data interpretation by managing display features such as colors, labels and legends.
    • Customize colors in plots to improve readability of a scatterplot.
    • Label axes and add titles to improve data readability.
    • Manipulate legend properties such as position, font size, and background color, to improve the esthetics and readability of data.

Effective Communication of Data Insights

  • Objective 5.2.1 – Tailor communication to different audience needs, and combine visualizations and text for clear data presentation.
    • Analyze the audience to understand their background, interests, and knowledge level.
    • Adapt communication style and content to meet the specific needs and expectations of diverse audiences.
    • Create presentations and reports that effectively convey data insights to both technical and non-technical stakeholders.
    • Integrate visualizations seamlessly into presentations and reports, aligning them with the narrative.
    • Use concise and informative text to complement visualizations, providing context and key takeaways.
    • Ensure visual and textual elements work harmoniously to enhance data clarity and understanding.
    • Avoid slide clutter and optimize slide content to maintain focus on key messages.
    • Craft a compelling data narrative that tells a story with data, highlighting insights and actionable takeaways.
    • Select an appropriate and consistent color palette for visualizations, ensuring clarity and accessibility.
  • Objective 5.2.2 – Summarize key findings and support claims with evidence and reasoning.
    • Understand the process of identifying and extracting key findings from data analysis.
    • Apply techniques to condense complex information into concise and meaningful summaries.
    • Prioritize and emphasize the most relevant insights based on context.
    • Explain the importance of backing assertions and conclusions with data-driven evidence and reasoning.
    • Articulate the basis for claims and recommendations, demonstrating transparency in decision-making.
    • Demonstrate proficiency in clearly presenting evidence to support claims and recommendations.

Last updated: April 12, 2024
Aligned with Exam PCED-30-01

MQC Profile

A minimally qualified candidate (MQC) for the PCED exam is expected to have a foundational understanding of data acquisition and pre-processing, including effective data collection, integration, cleaning, and preparation techniques. They should possess basic programming and database skills, with proficiency in Python and the ability to perform SQL queries for data manipulation. The candidate should also have a grasp of statistical analysis, demonstrating the ability to apply descriptive and inferential statistics to derive insights from data. Furthermore, they must demonstrate knowledge of and skills in data analysis and modeling using tools like Pandas and NumPy, and have the capability to communicate their findings through clear and insightful data visualizations using Matplotlib and Seaborn. Effective communication skills are essential to present data insights coherently, catering to diverse audiences.

This profile represents a blend of technical proficiency, analytical thinking, and communication skills crucial for navigating the complexities of data-driven environments.

Block 1: Data Acquisition and Pre-Processing (33% of total exam)

Minimum Coverage: A candidate must demonstrate a basic understanding of data collection methods, including surveys and web scraping, and recognize the importance of ethical considerations. Proficiency in data cleaning techniques, such as handling missing values and data standardization, is required.

Block 2: Programming and Database Skills (29% of total exam)

Minimum Coverage: Proficiency in fundamental Python programming, including basic syntax, data structures, and simple control structures, is necessary. Basic SQL query formulation for data retrieval and a rudimentary understanding of database connections in Python are required.

Block 3: Statistical Analysis (9% of total exam)

Minimum Coverage: Understanding basic descriptive statistics, such as measures of central tendency and variability, and an introductory grasp of inferential statistics, including simple regression analyses.

Block 4: Data Analysis and Modeling (16% of total exam)

Minimum Coverage: Basic capability to utilize Pandas for data manipulation and NumPy for numerical computations. A minimal understanding of data modeling concepts and the ability to apply basic machine learning algorithms using Python libraries.

Block 5: Data Communication and Visualization (13% of total exam)

Minimum Coverage: Basic skills in data visualization with tools like Matplotlib and Seaborn, and the ability to communicate findings effectively, albeit in a simple manner.

To pass the PCED exam, a candidate's cumulative average score across all blocks must reach 75%.