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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
Topic 2: Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles
Topic 3: Data Visualization and Communication15%- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
Topic 4: SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
Topic 5: 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. Model training, testing and evaluation
  • 3. Classification: logistic regression, k-NN, decision trees

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

Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

  • A. Checking for duplicate rows
  • B. Replacing NULL values with placeholders
  • C. Sorting the data frame by index
  • D. Confirming numeric columns contain only integers
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

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

  • A. plt.legend()
  • B. plt.xlabel()
  • C. plt.annotate()
  • D. plt.title()
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

What is a major challenge in aggregating data from multiple sources?
Response:

  • A. Duplicate records and format inconsistencies
  • B. Increased algorithmic complexity
  • C. Excessive disk space usage
  • D. Overfitting in predictive models
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

  • A. sqlalchemy
  • B. sqlite3
  • C. psycopg2
  • D. pymysql
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Which of the following are key differences between Pandas and NumPy arrays?
(Choose two)

  • A. NumPy supports string indexing; Pandas does not
  • B. Pandas integrates better with tabular data and missing values
  • C. Pandas Series are more memory efficient than NumPy arrays
  • D. Pandas handles labeled data; NumPy does not
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

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