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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q29-Q34):

NEW QUESTION # 29
What will be the output of the following code?

Answer: A

Explanation:
The function declares the variable as global and assigns it the value 5. When the function is called, it updates the global variable and returns 5, so both the returned value and the global variable printed afterward are 5.


NEW QUESTION # 30
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 # 31
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

Answer: B

Explanation:
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.


NEW QUESTION # 32
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

Answer: B

Explanation:
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.


NEW QUESTION # 33
A script defines a variable x = None and checks its truth value using a conditional statement. The developer wants to understand how Python evaluates None in Boolean contexts. What will bool(x) return?

Answer: D

Explanation:
The value None represents the absence of a value and is treated as False in Boolean expressions. Therefore, calling bool(None) returns False, similar to empty containers or zero values.


NEW QUESTION # 34
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