Ultimate Guide to the PCAD-31-02 – Latest Oct 18, 2026 Edition Available Now
2026 Updated Verified Pass PCAD-31-02 Exam – Real Questions and Answers
Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section |
Weight |
Objectives |
| Topic 1: SQL and Database Integration |
10% |
– Relational database concepts
- 1. Tables, keys, relationships and normalization
– SQL querying
- 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- 2. Subqueries and filtering
– Python-database connectivity
- 1. Executing queries and retrieving results
- 2. Connecting to SQLite, MySQL or PostgreSQL
- 3. Error handling and best practices
|
| Topic 2: Data Acquisition and Preprocessing |
30% |
– Data preparation with Pandas and NumPy
- 1. Data structures: Series, DataFrame, ndarray
- 2. Data reshaping and aggregation
- 3. Indexing, filtering, sorting and grouping
– Data collection, integration and storage
- 1. Data integration and merging
- 2. Data collection methods and sources
- 3. Data formats and storage systems
– Data cleaning and validation
- 1. Data standardization and transformation
- 2. Quality assurance and validation techniques
- 3. Handling missing, duplicate and invalid values
|
| Topic 3: Data Visualization and Communication |
15% |
– Visualization with Matplotlib and Seaborn
- 1. Line, bar, scatter, histogram, box plots
- 2. Heatmaps, pair plots and correlation matrices
- 3. Customization and styling
– Data storytelling and reporting
- 1. Written and verbal presentation techniques
- 2. Structuring insights and conclusions
– Visualization principles and best practices
- 1. Choosing appropriate chart types
- 2. Audience-focused presentation
- 3. Color, layout and clarity
|
| Topic 4: Data Exploration and Statistical Analysis |
25% |
– 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
|
| Topic 5: Data Modeling and Machine Learning Basics |
20% |
– Supervised learning fundamentals
- 1. Regression: linear, multiple, polynomial
- 2. Classification: logistic regression, k-NN, decision trees
- 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
|
Dumps Moneyack Guarantee – PCAD-31-02 Dumps Approved Dumps: https://www.passtestking.com/Python-Institute/PCAD-31-02-practice-exam-dumps.html
Related Links: myportal.utt.edu.tt www.stes.tyc.edu.tw myportal.utt.edu.tt myportal.utt.edu.tt myportal.utt.edu.tt myportal.utt.edu.tt
