Professional Training

Prerequisites

There are no formal prerequisites. Participants will benefit from basic computer literacy, some familiarity with spreadsheets or structured data, foundation-level mathematics and statistics, and basic Python knowledge equivalent to PCEP level.

Who should attend

• Aspiring or junior data analysts
• Students and career changers entering data analytics
• Business professionals who need to analyse data using Python
• Reporting, operations and research staff
• Python beginners seeking an entry-level data analytics certification
• Candidates intending to progress to the PCED-30-02 examination

Learning Objectives

At the conclusion of this course, attendees will be able to:

• Explain how raw data becomes useful information and insight
• Classify quantitative, qualitative, structured and unstructured data
• Describe common data sources, collection methods and storage options
• Explain the data lifecycle and principal types of analytics
• Recognise ethical, privacy and legal considerations when handling data
• Use Python variables, data types, expressions and string operations
• Work with lists, tuples, sets and dictionaries
• Write functions and control program flow using conditions and loops
• Handle common Python errors and exceptions
• Read and write text and CSV files
• Clean, convert and normalise data for analysis
• Calculate aggregates and descriptive statistics
• Use NumPy arrays for introductory numerical analysis
• Identify patterns, frequencies, correlations and possible outliers
• Select and interpret appropriate data visualisations
• Communicate findings through clear reports and presentations

Day 1 - Data and Data Analysis Fundamentals

Core data concepts, sources, storage, lifecycle, analytics types and responsible data handling.

Understanding Data

Data, information, knowledge and insight
The role of data in decision-making
Quantitative and qualitative data
Structured, semi-structured and unstructured data
Transforming raw data into meaningful information

Data Sources and Collection

APIs, web pages and databases
Surveys, interviews and observations
Application logs, automated systems and IoT devices
Web scraping concepts
Representative sampling
Biased, incomplete and unrepresentative data
Comparing data-collection methods

Data Storage and Organisation

CSV, JSON and Excel formats
Relational databases
Data warehouses and data lakes
The role of metadata
Selecting storage based on structure, scale and purpose

The Data Lifecycle

Collection, storage and processing
Analysis and reporting
Archiving and deletion
Data quality, security and compliance
How errors affect subsequent stages

Data Analytics Concepts

Data analysis, data analytics and data science
Descriptive, diagnostic, predictive and prescriptive analytics
The data analytics workflow
Common roles and responsibilities

Ethics and Legal Considerations

Privacy, consent and transparency
Fairness and accountability
GDPR, CCPA and HIPAA awareness
Anonymisation and encryption
Responsible use of personal and sensitive data

Practical work: classify real-world datasets and design a suitable data lifecycle for a small analytics project.

Day 2 - Python Basics for Data Analysis

Python foundations and the core collections and string operations used in entry-level data analysis.

The Python Environment

Running Python interactively and from scripts
Python syntax and indentation
Comments and readable code
Interpreting simple Python programs

Variables and Data Types

Creating and assigning variables
Integers, floating-point values, strings and Boolean values
Arithmetic and string operations
Using type() and isinstance()
Converting between data types
Formatting values with f-strings

Lists

Creating and accessing lists
Indexing and slicing
Adding, updating and removing values
Sorting and reversing
Counting and locating values
List comprehensions for transformation and filtering

Tuples and Sets

Creating and accessing tuples
Tuple immutability
Creating and modifying sets
Union, intersection and difference
Removing duplicate values
Membership testing

Dictionaries

Keys and values
Adding, updating and deleting entries
Iterating through dictionaries
Counting, grouping and lookup operations
Representing records as lists of dictionaries

Working with Strings

Strings as sequences
Indexing and slicing
Searching and testing strings
Changing case and formatting text
startswith(), endswith(), find(), isdigit() and isalpha()

Practical work: develop a Python program that validates, categorises and summarises a collection of data records.

Day 3 - Python Logic, Functions, Modules and Files

Functions, program flow, error handling, modules and file-processing techniques for robust data workflows.

Functions

Defining and calling functions
Positional, keyword and default arguments
Returning values
Understanding None
Placeholder functions using pass
Local and global scope
Name shadowing

Conditions and Boolean Logic

Comparison and logical operators
Boolean expressions
if, elif and else
Nested conditions
Identifying missing, invalid and out-of-range values
Filtering data using conditions

Loops

for and while loops
break and continue
The loop else clause
Combining loops and conditions
Applying repeated operations to data

Exception Handling

Common runtime errors
TypeError, ValueError and IndexError
Using try and except
Handling file-related errors
Producing useful error messages
Writing robust data-processing programs

Modules and Packages

import and from...import
Module aliases
math, random, statistics and collections
os and datetime
Built-in modules and third-party packages
Installing packages with pip
Introducing NumPy

Working with Files

Opening, reading and writing text files
Safe file handling with with
File paths and os.path.exists()
Reading CSV data with csv.reader()
Writing CSV data with csv.writer()
Parsing delimited text
Producing formatted summaries

Practical work: import a CSV dataset, validate its records, handle file errors and produce a formatted summary file.

Day 4 - Data Preparation and Analysis

Cleaning, transforming, summarising and exploring data with Python and NumPy.

Cleaning and Converting Data

Detecting missing and null values
Removing or replacing missing values
Validating types, formats and ranges
Detecting and removing duplicates
Cleaning text with string methods
Splitting and joining values
Converting between strings, numbers and Boolean values
Formatting numeric values

Dates and Times

Parsing dates with datetime.strptime()
Formatting dates with strftime()
Validating and standardising date values

Normalising Data

Understanding differences in scale
Manual min-max normalisation
Indexed transformations using enumerate()
Preparing clean data for analysis

Aggregations and Descriptive Statistics

len(), sum(), min(), max() and round()
Counting values
Mean, median and standard deviation
Square root, ceiling and floor operations
Frequency analysis using collections.Counter
Conditional metrics
Grouping and summarising categories

NumPy Fundamentals

Creating NumPy arrays
NumPy arrays compared with lists
Generating sequences
Sum, mean, median and standard deviation
Sorting and filtering arrays
Boolean indexing
Finding unique values

Exploratory Data Analysis

Sorting and filtering records
Identifying patterns and trends
Calculating frequencies
Grouping by category
Introductory correlation analysis
Correlation versus causation
Detecting possible outliers
Interpreting analytical findings

Practical work: clean and explore a realistic dataset, calculate statistics and document significant patterns, relationships and possible outliers.

Day 5 - Communicating Insights and Integrated Project

Visualisation, data storytelling, analytical reporting and an integrated end-to-end data project.

Data Visualisation Principles

Bar, line and pie charts
Choosing a suitable chart
Communicating comparisons, trends and proportions
Strengths and limitations of common chart types
Effective titles, labels, colours and font sizes
Recognising misleading or unclear visualisations
Improving charts to support the intended message

Data Storytelling

Moving from analytical results to a clear message
Introduction, insights and conclusion
Leading with the principal finding
Supporting claims with evidence
Transitions and signposting
Adapting tone and detail for different audiences

Analytical Reports

Defining the business or analytical problem
Describing the analysis
Presenting insights
Making evidence-based recommendations
Writing concise summaries
Organising content with headings and bullet points
Combining narrative, data and visual evidence

Presenting Findings

Explaining charts and analytical results
Presenting to technical and non-technical audiences
Using effective visual and verbal techniques
Responding to questions using evidence
Recognising unsupported conclusions

Integrated Data Analysis Project

Defining an analytical question
Importing a CSV dataset
Validating and cleaning its records
Calculating descriptive and conditional metrics
Identifying trends, frequencies and possible outliers
Selecting appropriate visualisations
Producing a concise analytical report
Presenting findings and recommendations
Receiving instructor feedback

Optional Day 6 - PCED Examination Preparation Workshop

A separate one-day instructor-led workshop for candidates who have completed the five-day course or possess equivalent knowledge.

PCED Examination Overview

Examination structure and question formats
The four PCED syllabus blocks
Weighting of each examination block
Current passing requirements
Single-select questions
Multiple-select questions
Scenario-based questions
Managing the available examination time

Structured Syllabus Review

Data and data-analysis concepts
Python fundamentals for data analysis
Data preparation and simple analytical techniques
Communicating insights and reporting
High-frequency concepts and common areas of confusion
Identifying individual knowledge gaps

Question-Answering Techniques

Reading questions precisely
Identifying what a scenario is testing
Evaluating code without executing it
Tracing variables, conditions and loops
Eliminating implausible answers
Handling questions with multiple correct selections
Avoiding unsupported assumptions
Recognising common distractors

Guided Practice

Topic-based examination questions
Python code-tracing exercises
Data-cleaning and statistical scenarios
Visualisation interpretation questions
Instructor explanations and group discussion

Mock Examination

Timed 40-question mock examination
Simulation of examination conditions
Review of every question and answer
Explanation of incorrect alternatives
Analysis of performance by syllabus block
Identification of final revision priorities

Personal Preparation Plan

Individual feedback
Targeted revision recommendations
Examination-day preparation
Time-management strategy
Recommended follow-up practice and study resources