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Python for Data (pandas and Visualisation)

This is Python for analysts, not for software engineers. Participants spend two days on the work an analyst actually does: opening a file that turns out to be messier than promised, reshaping it into something usable, joining it to another source that names the same customer three different ways, and getting a chart out at the end that survives a second look. Programming concepts appear only where they are needed to do that work.

Programme Agenda

Enough Python to Start

Variables, lists, dictionaries, loops and functions, introduced only as far as the analysis work requires. Working in a notebook, and the habits that keep one readable a month later.

Loading Real Data

CSV, Excel with multiple sheets, and a database query. Encoding problems, inconsistent headers, and the file where the actual data starts on row seven under a merged title.

Inspecting and Cleaning

Data types that arrived wrong, whitespace and case inconsistency, duplicates that are not exact duplicates, and outliers that are either errors or the most interesting rows in the set.

Missing Data

Understanding why a value is missing before deciding what to do about it. Dropping, filling, forward filling and flagging, and the effect each has on what you can then claim.

Selecting, Filtering and Transforming

Indexing and boolean filtering, deriving columns, conditional logic across rows, and applying a function without falling into the slow patterns that make an analysis crawl.

Joining and Reshaping

Merges and their join types, the row count check that catches a bad join immediately, and pivoting between wide and long formats. Concatenating files that should have been one.

Grouping, Aggregating and Time Series

Group-by aggregation, multiple aggregations at once, and window calculations. Parsing dates, resampling to a period, and rolling averages.

Charts and Reproducibility

Line, bar, scatter and distribution plots, labelling them properly, and choosing a chart that answers the question. Structuring a notebook so someone else can rerun it.

Learning Outcomes:
Write enough Python to load, inspect and manipulate a dataset
Read messy CSV, Excel and database sources into a usable dataframe
Clean type, whitespace, duplicate and outlier problems systematically
Handle missing data with a defensible choice rather than a default
Filter, derive and transform columns efficiently
Join datasets and verify the join did what you intended
Aggregate by group and work with time series data
Produce labelled charts inside a notebook another person can rerun

Duration: 2 Days (16 Hours)
Training Hours: 9:00 AM to 5:00 PM
Level: All levels
Training Mode: Physical, Online, or Hybrid
HRD Corp SBL-KHAS Claimable
Certificate of Completion included

Frequently Asked Questions

Analysts, finance and operations staff, researchers, and Excel power users hitting the limits of a spreadsheet. Also engineers and scientists who need to process data but have not used Python for it.

No. The first module builds the Python needed from nothing. Participants who already program will find that module quick and the rest at full pace.

Python earns its place when the data is too large, too messy or too repetitive for a spreadsheet, and when the analysis has to be rerun identically each month. Where Excel or Power BI is the better tool, the session says so.

For in-house delivery, yes, and it makes a considerable difference. Participants bring a real file and work it through the cleaning and joining modules, often leaving with an analysis they can use.

Yes, this programme is HRD Corp SBL-KHAS claimable. Our team can assist your HR department with the documentation required for the grant application.

Yes. Any programme can be booked as a team day. Everyone works the same brief together, so your people come away having built something and knowing each other better.

If you are claiming under HRD Corp, the session has to fall at least 14 days after your HRD Corp approval. If you are not claiming, the date is flexible and we work around your calendar.

Put them on the self-paced e-learning instead. Your team works through the modules on our LMS in their own time, sits the assessment, and earns the same certificate, so nobody has to clear a full day together.

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