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Data Science and Machine Learning using Python - A Bootcamp cover image

Data Science and Machine Learning using Python - A Bootcamp
One Education

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

Summary

Price
Save 17%
£19 inc VAT (was £22.99)
Offer ends 30 April 2026
Study method
Online, On Demand
Duration
25.3 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
360 students purchased this course

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Overview

Data science job vacancies in the UK rose by 22% in 2025, showing strong demand across sectors.
UK employers report that 70% of digital roles now require Python-based data skills.
Average salaries for data roles increased by 12% in 2025, making this a high-growth pathway.

Data science and machine learning continue to grow rapidly across the UK, and Python remains the most preferred skill for data roles in almost every sector. This course helps learners build strong confidence with Python, data analysis, data visualisation and machine learning methods that are used widely in real projects. Learners follow a clear and easy route starting from Python essentials before moving to NumPy, pandas and visualisation tools that shape data projects in many UK workplaces. The lessons stay simple to follow and support learners step-by-step, so they grow their skills steadily. The course mixes coding, data handling, visual charts and machine learning models in a smooth learning order. This gives learners a direct way to explore structured data, create insights and work on real patterns that appear in the business world. The course also supports learners with an additional section on NLP and recommender systems, which helps them explore trending topics. The structured flow helps learners gain confidence with each topic, and they learn how these tools connect. Because Python is widely used in the UK, this course gives learners a clear route to grow their skills and move towards data-driven roles.

The course starts with the environment set-up and moves to Python basics, which introduce clear coding steps. Then it progresses to NumPy and pandas, which help learners work with arrays, tables and data sets that appear in business data tasks. Through matplotlib, Seaborn and pandas plotting, learners create charts for reports and presentations. Many UK organisations want clear visual insights, and this course supports that directly. Plotly and Cufflinks add interactive features, and this helps learners present insights in a more engaging way. The capstone project allows learners to apply these tools across a full analysis and visualisation workflow. Machine learning sessions then follow, covering linear regression, logistic regression, KNN, decision trees, random forests, SVMs, clustering and PCA. These methods help learners explore predictions, classifications and patterns. Extra sections on recommender systems and NLP help learners understand trending tools. Because the course keeps the flow simple and clear, learners stay confident as they move from analysis to machine learning. With constant step-by-step learning, this course supports beginners and those who want to strengthen their skills.

Learning Outcomes

  • Use Python to write clean code for data tasks
  • Analyse data using NumPy and pandas
  • Create charts using matplotlib, Seaborn, pandas and Plotly
  • Apply machine learning models for prediction and grouping
  • Build a capstone project using data analysis and visual tools

Please note: The TOTUM card is not included in the course price. Additional fees may be required if you choose to apply for or claim a TOTUM card.

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
110
lectures
25h 20m
total

    • 1: 0_0_ML_Introduction_Lecture 14:43
    • 2: 0_1_LRM_Lecture 14:40
    • 3: 0_K-Means_Clust_Lecture 11:04
    • 4: 0_KNN_Lecture 08:22
    • 5: 0_LogR_Lecture 10:26
    • 6: 0_Numpy_Lecture 03:04
    • 7: 0_pandas_Lecture 01:44
    • 8: 0_PCA_Lecture 09:13
    • 9: 0_PreFace_2_Final 02:25
    • 10: 0_SVM_Lecture 06:32
    • 11: 0_Trees_Lecture 17:57
    • 12: 1_0_ConfusionMatrix_changes.mp4 00:30
    • 13: 1_0_FutureWarnings 00:26
    • 14: 1_1_Interactive_plots 19:22
    • 15: 1_1_Linear_Reg 17:05
    • 16: 1_1_Log_Reg 16:55
    • 17: 1_1_matplotlib_essentials 13:13
    • 18: 1_1_Python_Essentials 20:57
    • 19: 1_2_Interactive_plots-Geo 13:36
    • 20: 1_2_Linear_Reg 19:25
    • 21: 1_2_Log_Reg 19:46
    • 22: 1_2_matplotlib_essentials 22:28
    • 23: 1_2_Python_Essentials 14:36
    • 24: 1_3_Log_Reg 11:23
    • 25: 1_3_matplotlib_essentials 21:36
    • 26: 1_3_Pickle_It 01:02
    • 27: 1_D-Tree_RFC 18:40
    • 28: 1_K-Means_Clustering 23:24
    • 29: 1_KNN 24:53
    • 30: 1_NLP_Lecture 12:46
    • 31: 1_Numpy_Essentials 27:45
    • 32: 1_Pandas_for_DV 33:32
    • 33: 1_Pandas_intro 02:10
    • 34: 1_PCA_Lecture 22:01
    • 35: 1_Proj_Oil_Bank_OvrVw 14:31
    • 36: 1_RS_Lecture 05:38
    • 37: 1_Seaborn_Intro 03:37
    • 38: 1_SVM 30:14
    • 39: 1_Tips_DS 00:40
    • 40: 2_1_DTree_Project_Ex 04:49
    • 41: 2_1_Insurance_LRM_Ex 08:23
    • 42: 2_1_Interactive_plots_Ex_Overview 10:32
    • 43: 2_1_K-Means_Clustering_Proj_OvrVw 07:16
    • 44: 2_1_KNN_Proj_OverVw 04:05
    • 45: 2_1_LogR_Project_Overview 04:43
    • 46: 2_1_matplotlib_exr_Overview 05:45
    • 47: 2_1_NLP_Spam_Ham_P_1 13:18
    • 48: 2_1_Pandas_DV_Ex 03:11
    • 49: 2_1_PCA_Project_Ex 01:33
    • 50: 2_1_Proj_Oil_Bank_Sol_P_1 17:40
    • 51: 2_1_Rec_Sys_P_1 17:56
    • 52: 2_1_SVMs_Project_OverView 06:47
    • 53: 2_2_DTree_Proj_Sol 15:26
    • 54: 2_2_Insurance_LRM_Sol 29:47
    • 55: 2_2_Interactive_plots_Ex_Sol 36:58
    • 56: 2_2_K-Means_Clustering_Proj_Sol 22:00
    • 57: 2_2_K-Means_Clustering_Proj_Sol_2 22:00
    • 58: 2_2_KNN_Proj_Sol 13:33
    • 59: 2_2_LogR_Proj_Sol 14:35
    • 60: 2_2_matplotlib_exr_Sol 20:53
    • 61: 2_2_NLP_Spam_Ham_P_2_2 18:37
    • 62: 2_2_Pandas_DV_Sol 13:10
    • 63: 2_2_PCA_Proj_Sol 17:11
    • 64: 2_2_Proj_Oil_Bank_Sol_P_2 18:28
    • 65: 2_2_Rec_Sys_P_2 19:01
    • 66: 2_2_SVMs_Project_Sol 20:16
    • 67: 2_3_Matploltlib_Optional 00:18
    • 68: 2_3_NLP_Spam_Ham_P_3 18:41
    • 69: 2_3_Proj_Oil_Bank_Sol_P_3 16:44
    • 70: 2_4_nlp_spam_ham_p_4 (1080p) 13:03
    • 71: 2_5_NLP_Spam_Ham_P_5 09:04
    • 72: 2_course_intro (1080p) 07:21
    • 73: 2_Numpy_Essentials 26:27
    • 74: 2_Python_Essentials 12:18
    • 75: 2_Seborn_Distributation_plot 25:20
    • 76: 2_Series 20:15
    • 77: 3_1_Python_Essentials 15:35
    • 78: 3_1_Seborn_Categorical_plots 20:50
    • 79: 3_1_setup_env_2020 09:10
    • 80: 3_2_Python_Essentials 20:05
    • 81: 3_2_Seborn_Categorical_plots 15:31
    • 82: 3_2_setup_env_2020 (1080p) 25:19
    • 83: 3_Dataframes 29:47
    • 84: 3_Heart_Disease_Cleveland_Optional_Project 01:48
    • 85: 3_Numpy_Essentials 07:18
    • 86: 4_1_Env_File 00:14
    • 87: 4_1_Python_Essentials_Exercises 02:04
    • 88: 4_2_EnvSetupOtherOptions 03:34
    • 89: 4_2_Python_Essentials_Exercises_sol 21:56
    • 90: 4_Hierarchical_Indexing 14:11
    • 91: 4_Numpy_Ex_overview 02:16
    • 92: 4_Overview_911_project 02:32
    • 93: 4_Seborn_Axis_Grids 25:04
    • 94: 5_Handling_Missing_Data 11:59
    • 95: 5_Imp_Note 00:18
    • 96: 5_Matrix_plots 13:25
    • 97: 5_Numpy_Ex_Sols 25:06
    • 98: 6_Data_Wrangling_Merging_Concat 20:14
    • 99: 6_Possible_Updates 00:24
    • 100: 6_Seborn_Reg_plots 11:29
    • 101: 7_GroupBy 10:16
    • 102: 7_Seborn_Figure_Aesthetics 10:26
    • 103: 8_1_Seaborn_Exercises 04:15
    • 104: 8_2_Seaborn_Ex_Sol 18:58
    • 105: 8_Useful_Methods 26:18
    • 106: 9_1_Cust_Purch_Ex_1_Overview 08:29
    • 107: 9_2_Cust_Purch_Ex_1_Sol 30:44
    • 108: 10_1_Chicago_Payroll_Overview 04:25
    • 109: 10_2_1_Chicago_Payroll_Ex_Solution 17:36
    • 110: 10_2_2_Chicago_Payroll_Ex_Solution 18:10

Course media

Description

This course begins with a welcome session, an introduction and an environment set-up that help learners start smoothly. Learners move to Python essentials, which offer step-by-step guidance to write simple code. The course then shifts to NumPy for data analysis, where learners work with arrays and numeric operations. After this, learners use pandas to handle, clean and prepare data for reports. The visualisation part includes matplotlib, Seaborn and pandas plotting tools, which help learners create charts that support clear presentation. Learners also explore Plotly and Cufflinks for interactive and geographical visual outputs. The capstone project brings everything together as learners complete a full data analysis and visualisation task. The next part of the course focuses on machine learning using scikit-learn. Learners study linear regression and logistic regression models before progressing to K nearest neighbours, decision trees and random forests. Learners also explore SVMs, clustering with K means and dimensional reduction with PCA. The course includes two additional topics: recommender systems with Python and natural language processing using NLTK. These extra topics support learners with more ways to work with data. The course ends with a resources section that helps learners revise the full programme.

Who is this course for?

This course is for learners who want to grow their data skills in an easy and structured way. It suits individuals who want to use Python for data tasks, charts, analysis or machine learning. It also suits learners who plan to explore roles in data analysis, data visualisation and machine learning. Many learners in the UK choose this course to explore data roles in business, healthcare, finance, retail, marketing and other sectors. The flow of the course remains suitable for beginners as well as learners who already know some coding and want to improve. The course also supports learners who enjoy step-by-step tasks, guided projects and clear explanations. Because each topic follows a smooth order, many learners find it useful for building steady confidence.

Requirements

There are no formal requirements to enrol in this course. It supports learners aged 16 and above. Basic English skills help learners follow the lessons smoothly. Simple numeracy skills help learners work with values. Basic IT skills also help learners complete coding tasks and use the learning environment. A laptop or desktop computer helps learners follow Python lessons comfortably.

Career path

  • Data Analyst – £35,000 per year
  • Machine Learning Technician – £40,000 per year
  • Python Developer – £45,000 per year
  • Data Visualisation Assistant – £32,000 per year
  • Business Data Technician – £33,000 per year
  • AI Support Technician – £38,000 per year

Questions and answers

No questions or answers found containing ''.

Paula asked:

Good morning. How would sit the exams for these courses? Would I need to go to an exam centre, and would there be additional charges for exams?

Answer:

Dear Paula, Thank you for your questions. There will be an online MCQ-based exam at the end of the course and the exam fee is already included in the price. Thanks

This was helpful. Thank you for your feedback.
suk asked:

I need to pass this within the next 2 weeks and have a certificate is this possible?

Answer:

Dear Suk, Thank you for your question. Yes, this is possible. It is a self-paced course.

This was helpful. Thank you for your feedback.
Maria asked:

I need a Level 2 Functional Skills certificate in English and Maths to apply for a level 7 apprenticeship, is this certificate valid for that purpose?

Answer:

Hi Maria! Thanks for your query. Level 2 Functional Skills certificates in English and Maths can be a suitable qualification for entry into various apprenticeship programs, including Level 7 apprenticeships, depending on the specific requirements set by the apprenticeship provider or employer.

This was helpful. Thank you for your feedback.
Anab haji ali asked:

How can i apply this course

Answer:

Dear Anab, Thank you for your question. You can purchase the course online and the login details will be sent to you. You need to click on ADD TO CART button to proceed with the purchase.

This was helpful. Thank you for your feedback.
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Reviews

3.9
Course rating
75%
Service
80%
Content
80%
Value

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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