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Python Data Science with Numpy, Pandas and Matplotlib
One Education

Accredited by CPD QS | Instant Access | Free Digital Certificate of Completion | Engaging Learning Materials

Summary

Price
£19 inc VAT
Study method
Online, On Demand
Duration
6.2 hours · Self-paced
Qualification
No formal qualification
CPD
10 CPD hours / points
CPD What's this?
Certificates
  • Reed Courses Certificate of Completion - Free
  • CPD Accredited - Digital certificate - £9
  • CPD Accredited - Hard copy certificate - £15
Additional info
  • Tutor is available to students
147 students purchased this course

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Overview

In 2025, UK employers list Python in 42% of all data-related job postings, making it one of the most requested skills.
The UK data workforce is expected to grow by 11% this year, creating more demand for Python-based roles.
Average salaries for data roles using Python reached £55,000+ in 2025, showing strong career potential.

Python Data Science offers one of the most accessible ways for learners in the UK to enter the world of data-driven decision making. This field grows every year, and Python remains one of the most widely used tools for managing, analysing and presenting data. Python helps learners work with numbers, strings, lists and dictionaries with ease. Additionally, Python connects smoothly with libraries like NumPy, Pandas and Matplotlib. These libraries help learners clean data, manage missing values, combine data sources and create graphs. Learners can move through different tasks step by step. They can read CSV files, create arrays, manage indexes and group large datasets. They can also sort, merge and reshape information. Because Python works across many platforms, learners can apply the same skills at home or in the workplace. This makes Python Data Science a helpful skill for learners who want to move into data analysis, reporting, automation or research support roles. Python also gives learners a simple way to create charts that help people understand information quickly. Therefore, this field continues to attract attention across the UK in 2025. Many learners choose Python because it supports smoother workflows and clear results. With steady practice, learners can gain confidence with data and use it in real projects. This course covers many tasks that learners often perform during data preparation and reporting. Therefore, it offers a friendly path for those who want growth in the data world.

Moreover, Python Data Science helps learners carry out meaningful tasks in a clear and organised way. Learners explore variables, operators, indexing rules and different types of data structures. After that, they learn how to use NumPy for array management. This includes array creation, slicing and multi-dimensional work. Then they move to Pandas, which focuses on series and dataframes. Pandas supports reading files, converting columns, dropping entries and managing missing information. Because learners also explore hierarchical indexing, merging, pivoting and grouping, they gain a wide set of useful data skills. They work with JSON files, Excel sheets, lambda functions, random permutation, cross tabulation and data replacement rules. Then they finish with Matplotlib, which helps them produce graphs, plots and histograms. Each part connects to real data tasks learners might face in daily work. Therefore, learners complete the course with skills they can apply in many settings. They also gain confidence as they move through each topic step by step. This structure supports clear progress, making the learning journey smooth and rewarding. As a result, learners leave with stronger abilities that support real data roles. Because UK employers value Python, the topics in this course help learners stay relevant in 2025. Therefore, this course is a helpful stepping stone for anyone who wants to work with data or support teams with meaningful insights.

Learning Outcomes
  • Use Python strings, numbers, lists and dictionaries with ease.
  • Manage NumPy arrays for slicing, indexing and calculations.
  • Handle Pandas series, dataframes and file operations smoothly.
  • Clean, sort, merge and reshape data using Pandas tools.
  • Create clear graphs, plots and histograms using Matplotlib.

Certificates

Reed Courses Certificate of Completion

Digital certificate - Included

Will be downloadable when all lectures have been completed.

CPD Accredited - Digital certificate

Digital certificate - £9

CPD Accredited - Hard copy certificate

Hard copy certificate - £15

CPD

10 CPD hours / points
Accredited by CPD Quality Standards

Curriculum

1
section
61
lectures
6h 14m
total

    • 1: 0. Course Intro and Table of Contents 09:29
    • 2: 1. Introduction to Python 06:53
    • 3: 2. Preparing Computer - Installing Anaconda 08:25
    • 4: 3. Python Strings - Part 1 10:47
    • 5: 4. Python Strings - Part 2 08:50
    • 6: 4. Python Numbers and Operators - Part 1 06:29
    • 7: 4. Python Numbers and Operators - Part 2 07:00
    • 8: 5. Python Lists - Part 1 05:09
    • 9: 5. Python Lists - Part 2 06:08
    • 10: 5. Python Lists - Part 3 05:28
    • 11: 5. Python Lists - Part 4 06:54
    • 12: 5. Python Lists - Part 5 07:28
    • 13: 6. Python Tuples 05:35
    • 14: 6. Python Sets - Part 1 05:01
    • 15: 6. Python Sets - Part 2 03:58
    • 16: 7. Python Dictionary - Part 1 06:36
    • 17: 7. Python Dictionary - Part 2 06:55
    • 18: 8. Numpy Library - Introduction - Part 1 04:51
    • 19: 8. Numpy Library - Introduction - Part 2 05:25
    • 20: 8. Numpy Library - Introduction - Part 3 06:28
    • 21: 9. Numpy Array Operations and Indexing - Part 1 04:11
    • 22: 9. Numpy Array Operations and Indexing - Part 2 05:34
    • 23: 10. Numpy Multi-Dimentional Arrays - Part 1 07:15
    • 24: 10. Numpy Multi-Dimentional Arrays - Part 2 05:34
    • 25: 10. Numpy Multi-Dimentional Arrays - Part 3 05:19
    • 26: 11. Introduction to Pandas Series 08:10
    • 27: 12. Introduction to Pandas Dataframes 07:08
    • 28: 13. Pandas Dataframe conversion and dropping - Part 1 06:16
    • 29: 13. Pandas Dataframe convert drop - Part 2 05:34
    • 30: 14. Pandas Dataframe summary select - Part 1 05:45
    • 31: 14. Pandas Dataframe summary select - Part 2 05:31
    • 32: 14. Pandas Dataframe summary select - Part 3 06:58
    • 33: 15. Pandas Missing Sort - Part 1 06:37
    • 34: 15. Pandas Missing Sort - Part 2 06:42
    • 35: 16. Pandas Heirarchial-Multi Indexing 05:56
    • 36: 17. Pandas CSV File Read Write - Part 1 05:28
    • 37: 17. Pandas CSV File Read Write - Part 2 06:52
    • 38: 18. Pandas Json read write 06:41
    • 39: 19. Pandas Concatnation Merging and Joining - Part 1 04:39
    • 40: 19. Pandas Concatnation Merging and Joining - Part 2 04:16
    • 41: 19. Pandas Concatnation Merging and Joining - Part 3 04:22
    • 42: 20. Pandas Stack and Pivot - Part 2 06:12
    • 43: 20. Pandas Stacking and Pivoting - Part 1 05:22
    • 44: 21. Pandas Duplicate Data Management 07:19
    • 45: 22. Pandas Mapping 04:06
    • 46: 23. Pandas groupby 05:45
    • 47: 24 Pandas Aggregation 08:33
    • 48: 25 Pandas Binning or Bucketing 07:35
    • 49: 26 Pandas Reindex Rename - Part 1 04:04
    • 50: 26 Pandas Reindex Rename - Part 2 04:56
    • 51: 27 Pandas Replace Values 04:37
    • 52: 28 Pandas Dataframe Metrics 06:48
    • 53: 29 Pandas Random Permutation 08:15
    • 54: 30 Pandas Excelsheet Import 07:13
    • 55: 31 Pandas Condition Selection and Lambda Function - Part 1 04:34
    • 56: 31 Pandas Condition Selection and Lambda Function - Part 2 04:51
    • 57: 32. Pandas Ranks Min Max 06:02
    • 58: 33. Pandas Cross Tabulation 06:32
    • 59: 34. Graphs and plots using matplotlib - Part 1 06:25
    • 60: 34. Graphs and plots using matplotlib - Part 2 02:17
    • 61: 35. Histograms using matplotlib 03:21

Course media

Description

This course begins with a simple introduction and a clear table of contents. Learners then explore Python, Pandas and NumPy along with basic system setup. After that, they move through Python strings, numbers, operators, lists, tuples, sets and dictionaries. Once they complete the core Python topics, they begin the NumPy section. This includes array operations, indexing rules and multi-dimensional arrays. Then learners advance to Pandas, starting with series and dataframes. The course then explains dataframe conversion, dropping entries, summaries and selection. Learners also manage missing data and sorting. After this, they explore hierarchical indexing. Then they read and write CSV files and JSON files. They also work with concatenation, merging and joining. Stacking and pivoting follow next. Duplicate data management also appears in the curriculum. Pandas mapping, grouping, aggregation, binning, re-indexing and renaming help learners manage different forms of data. Learners also replace values, review dataframe metrics and use random permutation. They continue with Excel import, condition selection and lambda functions. They then learn ranks, min, max and cross tabulation. Finally, learners create graphs, plots and histograms using Matplotlib.

Python Data Science with Numpy, Pandas and Matplotlib

  • Course Introduction and Table of Contents
  • Introduction to Python, Pandas and Numpy
  • System and Environment Setup
  • Python Strings
  • Python Numbers and Operators
  • Python Lists
  • Tuples in Python
  • Sets in Python
  • Python Dictionary
  • NumPy Library - Introduction
  • NumPy Array Operations and Indexing
  • NumPy Multi-Dimensional Arrays
  • Introduction to Pandas Series
  • Introduction to Pandas Dataframes
  • Pandas Dataframe conversion and drop
  • Pandas Dataframe summary and selection
  • Pandas Missing Data Management and Sorting
  • Pandas Hierarchical-Multi Indexing
  • Pandas CSV File Read Write
  • Pandas JSON File Read Write
  • Pandas Concatenation Merging and Joining
  • Pandas Stacking and Pivoting
  • Pandas Duplicate Data Management
  • Pandas Mapping
  • Pandas Grouping
  • Pandas Aggregation
  • Pandas Binning or Bucketing
  • Pandas Re-index and Rename
  • Pandas Replace Values
  • Pandas Dataframe Metrics
  • Pandas Random Permutation
  • Pandas Excel sheet Import
  • Pandas Condition Selection and Lambda Function
  • Pandas Ranks Min Max
  • Pandas Cross Tabulation
  • Matplotlib Graphs and plots
  • Matplotlib Histograms

Who is this course for?

This course is for learners who want a friendly path into Python-based data work. It supports beginners who want to explore strings, numbers, lists and dictionaries. It also suits learners who want to use NumPy or Pandas for data tasks. Anyone who wants to manage files, clean data, create arrays or plot charts will benefit. Career changers, students, freelancers and office workers can all learn from this content. The course also helps people who want smoother workflows using dataframes, indexing, merging or grouping. Because the topics cover many forms of data tasks, the course suits a wide range of interests. Therefore, this course is a helpful choice for anyone who wants to grow in data-related fields.

Requirements

Learners do not need formal entry requirements. Anyone aged 16 or above can join. Good English, numeracy and IT skills help learners follow the content smoothly. Access to a computer and an internet connection supports steady progress. Because the course includes Python, NumPy, Pandas and Matplotlib, learners should feel ready to practise regularly. With time and simple effort, they can follow each step easily.

Career path

  • Data Analyst – £35,000 per year
  • Python Developer – £45,000 per year
  • Data Technician – £32,000 per year
  • Reporting Analyst – £38,000 per year
  • Business Data Assistant – £30,000 per year
  • Junior Data Scientist – £42,000 per year

Questions and answers

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Do you have to be a student to do these courses? Thanks

Answer:

Dear Casey, Thank you for contacting us. There are no specific prerequisites to enrol in this course. Anyone and everyone can take this course. Stay Safe Stay Healthy.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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