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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 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 - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Indexing, filtering, sorting and grouping 3. Data structures: Series, DataFrame, ndarray
Topic 2: 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. Error handling and best practices 2. Connecting to SQLite, MySQL or PostgreSQL 3. Executing queries and retrieving results
Topic 3: Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Statistical significance and interpretation 2. Hypothesis testing and confidence intervals 3. Probability concepts and distributions
Topic 4: Data Visualization and Communication	15%	- Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - 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. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots
Topic 5: Data Modeling and Machine Learning Basics	20%	- 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. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q34-Q39):

NEW QUESTION # 34
What is the correct SQL statement to permanently remove a table named customers from a database?

A. DELETE TABLE customers;
B. REMOVE TABLE customers;
C. TRUNCATE TABLE customers;
D. DROP TABLE customers;

Answer: D

NEW QUESTION # 35
Why might using a pie chart to represent category proportions be less effective than a bar chart?

A. Pie charts cannot be created using Seaborn
B. Pie charts display individual data points more clearly
C. Bar charts are limited to one variable only
D. Human perception is less accurate at judging angles than lengths

Answer: D

NEW QUESTION # 36
Which practices are essential to ensure a fair evaluation of a machine learning model's performance?
(Choose two)

A. Splitting data into training and test sets
B. Randomizing the test dataset labels
C. Training on the entire dataset including test records
D. Using cross-validation to assess generalizability

Answer: A,D

NEW QUESTION # 37
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A. Replacing inconsistent labels with a standardized value
B. Normalizing using min-max scaling
C. Converting all values to integers
D. Removing rows with invalid labels

Answer: A,D

NEW QUESTION # 38
What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A. To filter rows based on index values
B. To split the data into subgroups for aggregation
C. To append new rows to the DataFrame
D. To sort the DataFrame by a specific column

Answer: B

NEW QUESTION # 39
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