PCAD-31-02 Latest Exam Cram, PCAD-31-02 New Dumps Ebook

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data formats and storage systems 2. Data collection methods and sources 3. Data integration and merging - Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Quality assurance and validation techniques 3. Data standardization and transformation
SQL and Database Integration	10%	- Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Relational database concepts 1. Tables, keys, relationships and normalization
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 - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers
Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Customization and styling 3. Heatmaps, pair plots and correlation matrices - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
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. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

A. Logistic regression
B. Polynomial regression
C. Decision tree regression
D. Linear regression

Answer: A

NEW QUESTION # 45
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

A. Advertising has no effect on sales
B. The data shows no apparent relationship
C. There is a strong positive correlation
D. The correlation is weak and likely negative

Answer: C

NEW QUESTION # 46
What is the correct SQL statement to permanently remove a table named customers from a database?

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

Answer: C

NEW QUESTION # 47
Which practices are essential to ensure a fair evaluation of a machine learning model's performance?
(Choose two)

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

Answer: C,D

NEW QUESTION # 48
Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?

A. filter() function with map
B. while loop with try block
C. for loop with if condition
D. lambda expression with reduce

Answer: C

NEW QUESTION # 49
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