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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

Answer: B,D


NEW QUESTION # 112
What distinguishes structured data from unstructured data in the context of data modeling and storage?

Answer: C


NEW QUESTION # 113
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

Answer: B


NEW QUESTION # 114
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

Answer: C


NEW QUESTION # 115
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

Answer: D


NEW QUESTION # 116
......

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