Quiz PCAD-31-02 - Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Authoritative Valid Test Dumps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Handling missing, duplicate and invalid values
  • 3. Quality assurance and validation techniques
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data collection methods and sources
  • 3. Data formats and storage systems
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation
SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q13-Q18):

NEW QUESTION # 13
What is the primary reason for using parameterized queries instead of directly formatting SQL strings in Python?

Answer: D


NEW QUESTION # 14
When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

Answer: B


NEW QUESTION # 15
Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

Answer: A,C


NEW QUESTION # 16
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: A


NEW QUESTION # 17
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

Answer: D


NEW QUESTION # 18
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