FA-0760Data & Analytics

Contact-Centre Data Analytics with Python and pandas

Prepare interaction data and communicate useful operational findings

Introduction

Why this course

Develop practical skills for turning raw contact-centre data into a usable analytical dataset. Work with calls and permitted customer-interaction exports, inspect data quality and present findings for operational decisions.

The course assumes basic analytics and pandas familiarity. Contact, screen and speech analytics are discussed as wider programme considerations; hands-on practice centres on the supplied tabular and text data.

Learning outcomes

Identify suitable call, email, chat and other interaction-data sources.
Collect, parse, clean and preprocess a prepared dataset.
Use pandas indexing, DataFrames and file readers appropriately.
Explore patterns, interpret findings and select clear charts or tables.
Explain how interaction analytics can support service review and agent coaching without assuming guaranteed performance improvements.
Recognise programme-design challenges and critically compare reported contact-centre practices.

Prerequisites

Familiar with data analytics.
Familiar with analytics tools such as Pandas.
Basic practical analytics skills.

Basic Python familiarity and access to authorised anonymised or synthetic training data.

Training outline

5 modules

01Module 1 — Analytics context and contact-centre sources3 topics

Introduction to the World of Business and Data
Relevant Terms Explained
Data Analyst Compared to Other Data Jobs

Call and customer-interaction data sources and collection tactics.
Prepare datasets for operational questions; define assumptions and limitations.
Discuss contact, screen and speech analytics as complementary capabilities, not automatically available in pandas.

02Module 2 — pandas foundations for practical work3 topics

Label-based .loc versus position-based .iloc indexing.
Python indices, methods, parameters and arguments.
Datasets and DataFrames; create a DataFrame from scratch.

03Module 3 — Reading text and flat data7 topics

File vs File Object, Read vs Parse
Structured vs Semi-Structured and Unstructured Data
Data Connectivity through Text Files
Fixed-width Files
CSV Files with pandas
Importing Data with the "index_col" Parameter
Importing *.json Files

04Module 4 — Collection, cleaning and preprocessing3 topics

Implement a permitted sample-data collection workflow.
Inspect quality, clean records and prepare suitable analytical fields.
Document transformations and validate the resulting dataset before interpretation.

05Module 5 — Interpretation and presentation3 topics

Explore patterns and communicate operational findings with appropriate graphs, charts or tables.
Discuss potential service issues, coaching questions and decision-making limitations.
Review analytics-programme design challenges and compare illustrative practices without claiming unverified external case studies.

A programme built around your team.

Share your training goals and requirements.