Python Institute Entry-Level Data Analyst with Python (PCED-30-01) Exam Syllabus

• 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.

- 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.

- 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.

- 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.

• 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.

- 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.

- 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.

- 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.

• Perform type, range, and cross-reference checks.

- 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.

• 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.

- 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.

- 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.

- 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.

- 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.

• Accurately use basic Python syntax for variables, scopes, and data types.
• Implement control structures like loops and conditionals to manage data flow.

- 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.

- 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.

- 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.

- 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.

• 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.

- 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.

• 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.

- 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.

- 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.

- 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.

- 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.

- 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.

• 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.

- 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.

• 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.

- 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.

• 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.

- 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.

- 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.

- 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.

• 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.

- 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.

- 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.

• 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.

- 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.

- 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.

- 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.

• 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.

- 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.