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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to Data and Data Analysis Concepts22.5%- Data lifecycle and ethical considerations
  • 1. Data collection, storage, processing, usage, and sharing
  • 2. Privacy, security, bias, and fairness in data
- Data types and measurement scales
  • 1. Qualitative vs quantitative data
  • 2. Nominal, ordinal, interval, ratio scales
- Basic statistical concepts
  • 1. Population, sample, variable, observation
  • 2. Descriptive vs inferential statistics
- Definition and classification of data
  • 1. Role of data in decision-making and business
  • 2. Difference between data, information, and knowledge
  • 3. Process of turning raw data into insights
Topic 2: Data Visualization and Communication20%- Interpreting and presenting results
  • 1. Reporting findings clearly and concisely
  • 2. Deriving conclusions and insights
- Principles of effective data visualization
  • 1. Clarity, simplicity, and accuracy
  • 2. Choosing appropriate chart types
- Creating basic visualizations
  • 1. Line charts, bar charts, histograms, pie charts
  • 2. Using text and simple plotting tools
Topic 3: Python Basics for Data Analysis32.5%- Control flow and functions
  • 1. Basic exception handling
  • 2. Conditional statements, loops, iteration
  • 3. Defining and calling functions, parameters, return values
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
Topic 4: Working with Data and Performing Simple Analysis25%- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Exploratory data analysis
  • 1. Calculating mean, median, mode, range, variance, standard deviation
  • 2. Identifying patterns, trends, and outliers
- Data cleaning and preparation
  • 1. Formatting and standardizing values
  • 2. Filtering, sorting, transforming data
  • 3. Handling missing values, duplicates, and errors
- Data acquisition and loading
  • 1. Importing data from external sources
  • 2. Reading text, CSV, and structured files

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q34-Q39):

NEW QUESTION # 34
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

Answer: B

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.


NEW QUESTION # 35
How do the analysis and visualization stages of the data lifecycle typically work together?

Answer: C

Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.


NEW QUESTION # 36
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: C

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".


NEW QUESTION # 37
Below are six possible summaries for your presentation:
- Summary 1: "The data revealed a moderate positive correlation between age and recycling frequency. While 72% of students reported recycling paper regularly, only 39% reported recycling plastic."
- Summary 2: "Analysis showed notable differences in recycling habits by material type, with paper being most recycled and plastic least recycled."
- Summary 3: "Older kids recycled more than younger ones. Paper was the most recycled item- plastic got forgotten a lot."
- Summary 4: "Student responses showed positive environmental habits overall, though participation varied by waste type."
- Summary 5: "A lot of students said they recycle. Some didn't answer every question, so we couldn't figure out everything."
- Summary 6: "We asked more than 300 students about recycling. Most said they recycle paper, but way fewer recycle plastic." Which communication style is the best fit for Group A (data scientists) and Group B (12-year- olds), respectively? Select the best answer.

Answer: E

Explanation:
The first summary uses precise statistical language and quantitative detail appropriate for data scientists. The third summary uses simpler, more conversational language that is easier for 12- year-olds to understand.


NEW QUESTION # 38
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

Answer: D

Explanation:
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".


NEW QUESTION # 39
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