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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A) == operator for checking value equivalence
B) id() function to retrieve unique object identifiers
C) is operator for memory-level comparison
D) str() function to convert data formats


2. What is the primary reason to use .head() and .tail() methods in pandas when exploring datasets?

A) To create visualizations
B) To rename columns
C) To preview the first and last few rows of data
D) To delete missing values


3. What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

A) Inheritance
B) Encapsulation
C) Abstraction
D) Composition


4. Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

A) Include concise textual summaries that highlight the chart's message
B) Avoid summarizing the data verbally
C) Use visualizations alone without titles or captions
D) Repeat all numerical data in both the chart and text


5. When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

A) Generating multiple datasets of the same size by randomly sampling with replacement
B) Scaling all values between 0 and 1 before resampling
C) Creating samples by removing all duplicates
D) Drawing one sample and calculating the mean only once


Solutions:

Question # 1
Answer: B,C
Question # 2
Answer: C
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: A

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