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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data formats and storage systems
  • 3. Data integration and merging
- 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 reshaping and aggregation
  • 3. Data structures: Series, DataFrame, ndarray
Topic 2: Data Exploration and Statistical Analysis25%- 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
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
Topic 3: SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- 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
Topic 4: Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
Topic 5: Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

Answer: A,B


NEW QUESTION # 24
Why might using a pie chart to represent category proportions be less effective than a bar chart?

Answer: C


NEW QUESTION # 25
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: A


NEW QUESTION # 26
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

Answer: C


NEW QUESTION # 27
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

Answer: B,D


NEW QUESTION # 28
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