Python for Data Analytics - PCED

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