Python Programming for Data Analysis

Learning Facility

• Python Programming for Data Analysis 46:00

  • 1: Module 1 Introduction to Python 05:00
  • 2: Module 2 Data Structures in Python 06:00
  • 3: Module 3 Control Flow and Functions 05:00
  • 4: Module 5 Data Cleaning and Preparation 05:00
  • 5: Module 6 Data Analysis with Pandas 05:00
  • 6: Module 7 Data Visualization 05:00
  • 7: Module 8 Working with External Data 05:00
  • 8: Module 9 Statistical Analysis 05:00
  • 9: Module 10 Applied Data Analysis Projects 05:00
  • 10: Assignment -

• Review Request 01:00

  • 11: Review Request 01:00

• Claim your Certificate 01:00

  • 12: Claim Your Certificate 01:00

The course starts with an introduction to Python programming, followed by data structures, control flow and functions. These topics establish how a program stores information, applies logic and organises reusable instructions. It then moves into data cleaning and preparation before introducing analysis with Pandas, a Python library used to work with tabular datasets.

Further topics cover visualisation, external data and statistical analysis. Together, they outline a common data analysis workflow: bring in information, prepare it, examine patterns and present findings. The applied data analysis projects section provides context for connecting Python programming concepts with analytical questions, while the course also lists an assessment.

Python programming and data analysis are relevant in business, research and public-sector teams that use evidence to inform decisions. Learners already preparing reports or working with datasets can build on their existing knowledge; those considering analytics or computing courses can use this course as an introductory step. Progressing into specialist roles usually calls for deeper technical, statistical and subject knowledge.

Skills and Knowledge You’ll Develop

• Navigate Python programming concepts, including data structures, control flow and functions.
• Recognise the purpose of data cleaning and preparation before analysis.
• Explain how Pandas can be used to explore tabular information.
• Describe how Python programming can contribute to data visualisation and statistical analysis.
• Outline approaches to working with external data within a wider analytical workflow.
• Connect Python programming methods to the stages of an applied data analysis project.

• Beginners seeking an introduction to Python programming and data analysis, with no formal qualification listed as a requirement.
• Aspiring data analysts who want to understand Python programming workflows before pursuing broader study or job-specific experience.
• Professionals handling reports, spreadsheets or datasets in business, administration, research or public services who want to broaden their data-analysis skills.
• Existing programmers or spreadsheet users interested in Pandas, visualisation and statistical analysis.
• Learners pursuing personal development or further study in analytics or computing who want a foundation in Python programming.

• No formal qualification is required to start the Python Programming for Data Analysis course.
• The listing does not state that prior Python programming experience is required; the course begins with Python fundamentals.
• To access the online, you’ll need internet access and a device.

• Data analyst-statistician – £28,000–£65,000 per year
• Business analyst – £23,000–£55,000 per year
• Market research data analyst – £24,000–£50,000 per year
• Software developer – £30,000–£75,000 per year
• Data scientist – £32,000–£83,000 per year

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