Develop a working understanding of how Python supports data-led tasks, from structuring code to preparing, examining and presenting information during this Introduction to Python and Data Analysis during this course. You’ll learn how to apply core programming concepts to analyse activities, using tools such as Pandas and Matplotlib to handle datasets with greater accuracy. You’ll also build the technical capability and confidence to apply learnings in their day-to-day data analysis activities.
Introduction to Python and Data Analysis
In-demand Python skills for data professionals
This four-day course is for professionals with basic programming knowledge who want to use Python more effectively in data-focused roles. You’ll work with core language features before moving into DataFrames, aggregation and visual output using Pandas, Matplotlib, JupyterLab and the Anaconda environment. It suits early-career or developing data professionals who need to handle datasets more accurately, reduce manual data tasks and present findings in a clearer, more repeatable way.
Learning objectives
- Write structured Python scripts
- Use containers and functions
- Work with Pandas DataFrames
- Load and refine datasets
- Group and aggregate data
- Create Matplotlib charts
- Use JupyterLab effectively
- Apply Python to data tasks
Key facts
Certification
This is a skills and knowledge-based course without formal accreditation.
Who it’s for
This course is designed for professionals working with data who want to build practical Python skills and improve their ability to analyse and interpret information.
Prerequisites
Experience coding small programmes using variables, lists or arrays, conditions, loops and functions. Introduction to Programming - Python can support those needing this knowledge.
Exam information
This course does not include a formal exam but focuses on practical skill development through hands-on exercises.
Optional extras
There are no optional extras for this course.
Pre-course
No formal pre-course work is required, although reviewing basic programming concepts will help you get the most from the training.
Course syllabus
Dive into the detail of the course by looking at the syllabus below.
Day one
- Session 1: Introduction
- Python as an interpreted language
- Script mode by example
- Interactive mode
- Statements
- Comments
- Whitespace and indentation
Day one
- Session 2: Python: variables and scalar types
- Numerical types
- Text
- Boolean
- Variables as references
- The type() function
Day one
- Session 3: Operators and expressions
- Arithmetic operators
- Assignment operators
- Comparison operators
- Logical operators
- Membership operators
Day one
- Session 4: Containers
- Lists
- Tuples
- Sets
- Dictionary
Day two
- Session 5: Conditions and loops
- Basic if statement
- else clause
- for loop
- while loop
- The range function
- Iterating over a list
- break
- continue
Day two
- Session 6: Functions
- Inbuilt functions: len(), sum(), min(), max(), sorted()
- Defining functions
- Positional arguments
- Named arguments
- Default value arguments
Day two
- Session 7: Objects
- What is a class?
- Data attributes and methods
- A simple example
- Some methods of inbuilt containers
- Shape
Day three
- Session 8: Introduction to DataFrames
- What is a DataFrame?
- DataFrame attributes
- Loading and writing DataFrames
- Exploratory functions
- Subsetting
- Conditional subsetting
- Adding and dropping columns
- Inbuilt aggregating functions
- Missing values
- Shape
Day four
- Session 9: GroupBy and aggregation: split-apply-combine
- GroupBy one column and aggregate using single inbuilt function
- GroupBy two columns and aggregate using single inbuilt function
- GroupBy one column and aggregate using separate function for each column
Day four
- Session 10: Plotting with Matplotlib
- Bar chart
- Histogram
- Line plot
FAQs
This four-day course introduces Python programming before applying Pandas and Matplotlib to data analysis tasks. It covers core coding concepts, DataFrames, grouping, aggregation and chart creation, giving you a structured route into using Python for tabular data work.
What knowledge should I have before attending?
You should already understand basic programming constructs, including variables, arrays or lists, conditional statements, loops and functions. You should also be confident with numeracy, including interpreting averages, standard deviations, charts and graphs. Introduction to Programming - Python is listed as a pre-requisite course for delegates who need this foundation.
How is the course structured across four days?
The course starts with Python fundamentals, including variables, scalar types, operators, containers, conditions, loops, functions and objects. It then progresses into DataFrames, before covering grouping, aggregation and plotting with Matplotlib. This structure helps delegates build the coding base needed before applying Python to data analysis activity.
What role do Pandas and Matplotlib play?
Pandas is used for working with tabular data, including reading CSV and Excel files, managing missing values, manipulating data and producing summaries. Matplotlib is used to create bar charts, histograms and line graphs, helping delegates turn analysis outputs into visual formats that are easier to review.
What does the hands-on approach involve?
Delegates code along with the instructor and use the resources, data, examples and exercises provided during the course. The aim is to reproduce the analysis shown in the course manuals while also understanding the methods behind each step, so the learning is practical rather than only conceptual.
How can this course support my business?
This course can help teams build a shared foundation in Python-based analysis, reducing reliance on isolated manual methods when working with tabular data. We can also deliver it as a closed event if you have several delegates, get in touch to learn more.
What can I study after this course?
Python Programming 1 is recommended as further learning. It is a relevant next step if you want to continue developing your Python capability, after building a foundation in programming concepts, Pandas, Matplotlib and JupyterLab-based data analysis.
What our customers say
“Stuart was very good in explaining, and very patient, finding alternative ways to explain concepts where understanding was not grasped on the first attempt. Would recommend to all colleagues with an interest in data analysis.”
Stuart, Introduction to Python and Data Analysis learner
“Thoroughly enjoyed the course, very informative and a good balance between instruction and practical work.”
Megan, TSG learner
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