Python Institute Latest PCAD-31-02 Braindumps Questions Exam | PCAD-31-02: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)–100% free

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The Python Institute PCAD-31-02 exam is one of the most valuable certification exams. The PCAD-31-02 exam opens a door for beginners or experienced Python Institute professionals to enhance in-demand skills and gain knowledge. PCAD-31-02 credential is proof of candidates' expertise and knowledge. To get all these benefits Python Institute you must have to pass the PCAD-31-02 Exam which is not an easy task. Solutions provide updated, valid, and actual Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) (PCAD-31-02) Dumps that will assist you in PCAD-31-02 preparation and you can easily get success in this challenging Python Institute PCAD-31-02 exam with flying colors.

Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
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. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- 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. Hypothesis testing and confidence intervals
  • 3. Statistical significance and interpretation
SQL and Database Integration10%- 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
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data formats and storage systems
  • 3. Data integration and merging

>> Latest PCAD-31-02 Braindumps Questions <<

New PCAD-31-02 Exam Topics | PCAD-31-02 Latest Mock Test

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

Answer: A


NEW QUESTION # 50
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

Answer: D


NEW QUESTION # 51
When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

Answer: D


NEW QUESTION # 52
Which practices are essential to ensure a fair evaluation of a machine learning model's performance?
(Choose two)

Answer: B,D


NEW QUESTION # 53
Which function is used to reshape a NumPy array without modifying the original data values?

Answer: D


NEW QUESTION # 54
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