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Python Institute PCAD-31-02 Actual Tests : Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. What is the function of the melt() operation in Pandas when reshaping wide-format data?

A) Converts column values into rows
B) Transposes the DataFrame
C) Combines multiple DataFrames
D) Sorts the DataFrame based on a column


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

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


3. Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

A) df.loc[:, 'col1']
B) df.loc[2:7, ['col1', 'col2']]
C) df.iloc[0:5, 'col2']
D) df['col1', 'col2']


4. What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A) Increased memory usage
B) Slower database performance
C) Exposure to SQL injection attacks
D) Missing foreign key constraints


5. What is the main purpose of validating a dataset before applying statistical analysis?

A) To visualize patterns more clearly
B) To ensure the dataset meets expected quality and structure
C) To generate more columns from the dataset
D) To sort the data alphabetically


Solutions:

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

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