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QUALIFI Level 3 Diploma in Data Science

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What you'll learn

The Qualifi Level 3 Diploma in Data Science is a comprehensive program designed to provide learners with foundational knowledge and practical skills in data science. This course covers essential topics such as data analysis, visualisation, statistical techniques, and machine learning concepts. It aims to equip students to collect, clean, and analyse data to derive meaningful insights and make informed decisions. Additionally, learners will gain proficiency in using relevant software and tools for data manipulation and visualisation. This diploma is an excellent starting point for individuals pursuing a career in data science or related fields.

  • QUALIFI Level 3 Diploma in Data Science offers an introduction to the field.
  • Covers data science, AI, and machine learning's evolution and challenges.
  • Introduces emerging areas: synthetic data and graph data science.
  • Teaches essential analytical tools and introductory Python for data analysis.
  • Enables learners to implement basic data science models.
  • Completion opens doors to further study or employment.

Completing the QUALIFI Level 3 Diploma in Data Science opens doors to advanced studies, specialised training, entry-level roles, internships, or freelance opportunities. Graduates may pursue further certifications, engage in professional development, or opt for higher education to advance their careers in data science.

Key Benefits

  • Acquire the capability to conduct essential data wrangling and exploratory analysis.
  • Gain the ability to execute fundamental data-cleaning tasks.
  • Understand the significance of visualising data and grasp its role in data analysis.
  • Comprehend the processes and various types of data analytics.
  • Grasp the distinctions between multiple types of data and their respective characteristics.
  • Gain an understanding of Python's design philosophy and its key features.
  • Grasp the fundamental issues within the field of data science.
  • Comprehend the various categories of graph data science and familiarise yourself with diverse graph algorithms.
  • Gain a comprehensive understanding of the synthetic data ecosystem.
  • Grasp the fundamental concerns surrounding data privacy and security.
  • Comprehend the theoretical foundations of k-means clustering.
  • Understand the concept of a decision tree in data science.
  • Grasp the basic theory behind logistic regression.
  • Comprehend regression metrics and the process of evaluating a regression model.
  • Understand the process of preparing data for machine learning models.
  • Grasp the fundamental concepts of basic supervised machine learning models.

About Awarding Body

Qualifi is a UK Government (Ofqual.gov.uk) regulated awarding organisation and has developed a reputation for supporting relevant skills in a range of job roles and industries, including Leadership, Enterprise and Management, Hospitality and catering, Health and Social Care, Business Process Outsourcing and Public Services. Qualifi is also a signatory to BIS international commitments of quality. The following are the key facts about Qualifi.

  • Regulated by Ofqual.gov.uk
  • World Education Services (WES) Recognised

What is Included?

  • Outstanding Tutor Support: EDVORO provides consistent and helpful guidance throughout the course. Learners can seek assistance through the EDVORO Support Desk Portal.
  • Cutting-Edge Learning Management Platform: Access to a modern platform that contains essential learning resources and facilitates communication with the support desk team.
  • Quality Learning Materials include well-structured lecture notes, study guides, and practical applications. Real-world examples and case studies are integrated to help learners apply their knowledge effectively. These materials are available in well-structured pathway books, letter notes, PDF, PowerPoint, or Interactive Text Content formats on the learning portal.
  • Formative Assessment Feedback: Tutors provide feedback on formative assessments, helping learners improve their achievements throughout the program.
  • Summative Assessment feedback: Tutors provide feedback on summative assessments; this kind of feedback can be instrumental in helping students achieve their academic goals.  
  • Accessible Assessment Materials: All assessment materials are conveniently accessible through the online learning platform.
  • Supervision for All Modules: This suggests that oversight and guidance are provided for every module of the course.
  • Multiplatform Accessibility: Learners can access course materials through various devices such as smartphones, laptops, tablets, or desktops. This flexibility allows students to study at their convenience.
  • Limitless Learning Opportunities: EDVORO offers a range of innovative online and blended learning experiences to help learners expand their knowledge.
  • Blended Learning Approach: A blend of online and classroom study for convenience.
  • Convenience, Flexibility, Support, and User-Friendliness: Core principles of online learning at EDVORO


  • Assignment based Assessment
  • No exam

Entry Requirements

  • Approved Centres evaluate the applicant's suitability for the qualification.
  • Consider available support for individual learner needs.
  • No artificial barriers for access; applicants must be 18 or older.
  • Entry through centre-led registration, possibly involving interviews.
  • Level 3 Diploma requires comfort with GCSE level mathematics.
  • Covers mathematical concepts with basic operations: addition, multiplication, division.
  • Prerequisites: GCSE Math (Grade B or higher), GCSE English (Grade C or higher).
  • No coding experience is needed, but a willingness to learn Python is essential.
  • Consideration for applicants with substantial experience but no formal qualifications through interview and demonstration of ability.


By achieving the QUALIFI Level 3 Diploma in Data Science, learners can:

  • Advance to the QUALIFI Level 4 Diploma in Data Science.
  • Seek admission to a UK university for an undergraduate degree.
  • Transition into employment within a related profession.

Why gain Qualification

  • QUALIFI diplomas provide industry-recognised certification, showcasing expertise.
  • They facilitate career growth, enabling access to higher-level positions and increased earnings.
  • Specialised knowledge is a hallmark of QUALIFI diplomas, valued in niche industries.
  • Practical training ensures hands-on skill development for immediate workplace application.
  • Flexible learning options, including part-time and online courses, accommodate diverse schedules.
  • QUALIFI qualifications are globally recognised, facilitating international work and education opportunities.
  • Business-focused diplomas are beneficial for aspiring entrepreneurs and those starting ventures.
  • Networking, regulatory compliance, staying updated with industry trends, and enhanced confidence are additional benefits of a QUALIFI diploma.
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Course Content

Reference No : J/650/4952

This unit offers learners an introduction to Python programming tailored for data science. Assuming no prior coding knowledge or familiarity with Python, it commences by elucidating the fundamentals of Python, encompassing its design principles, syntax, naming conventions, and coding norms. The unit acquires learners with elementary Python data types, including integers, floats, strings, complex numbers, and booleans. It elaborates on how these data types can be generated, modified, manipulated, and computed using standard mathematical functions, logical operators, and Python's built-in methods and functions. Furthermore, the unit introduces more intricate data structures crucial for numerous data analytics and data science tasks, such as "lists," "tuples," "sets," and "dictionaries." Additionally, it instructs on utilising control and flow statements like branching and looping and the basics of crafting user-defined Python functions. These are foundational skills required for subsequent data analysis and successful coding of data science models.

Credit : 9 || TQT : 90

Reference No : H/650/4951

This unit provides learners with an overview of the evolution of data science, starting from the emergence of artificial intelligence and machine learning in the late 1950s, leading to the onset of the "big data" era in the early 2000s. It further explores contemporary applications of AI, machine learning, deep learning, and associated challenges.

Credit : 6 || TQT : 60

Reference No : K/650/4953

This unit introduces learners to fundamental charts and visualisations, including creation and interpretation methods. It begins by elucidating visualisations' significance in comprehending data and distinguishing between effective and ineffective visual representations. The unit introduces learners to various basic chart and plot types, clarifying their intended use, interpretation guidelines, and situations where they are most and least appropriate. Subsequently, the unit zeroes in on the technology for generating charts and visualisations in Python, encompassing tools like Seaborn, Matplotlib, and other Python libraries.

Credit : 3 || TQT : 30

Reference No : L/650/4954

This unit is designed to give learners a foundational understanding of descriptive statistics, pivotal in data analysis and science. It covers various types of data and introduces key descriptive statistics, including measures of central tendency, multiple indicators of data spread (such as range, percentiles, variance, and standard deviation), measures of symmetry (skewness and kurtosis), and actions of co-variation (correlation and covariance). The unit also elucidates which descriptive statistics apply to data measured on different scales. Throughout this unit, learners will actively engage in the hands-on practice of manually calculating descriptive statistics for small datasets.

Credit : 6 || TQT : 60

Reference No : M/650/4955

This unit introduces the fundamental concepts of data analytics. It aims to enable learners to distinguish between the roles of a Data Analyst, Data Scientist, and Data Engineer. Additionally, learners will gain an understanding of the data ecosystem, which encompasses databases and data warehouses, and become acquainted with key vendors within this ecosystem, along with exploration of various tools. The unit also covers essential tasks and processes in the data discovery phase, including data cleaning, approaches to address data quality issues, and methods for standardising data in preparation for analysis.

Credit : 3 || TQT : 30

Reference No : R/650/4956

In this unit, learners are introduced to foundational data analysis using Python. Core concepts like Pandas DataFrames and Series and techniques for merging and joining data are covered. The unit further extends previous modules by instructing on data importing, employing Python for generating descriptive statistics for analysis and interpretation. Additionally, learners will acquire skills in utilising Python to enhance data quality and standardise data, particularly in preparing it for machine learning models.

Credit : 3 || TQT : 30

Reference No : T/650/4957

This unit offers a broad overview (as opposed to an in-depth examination) of the three primary categories of machine learning: supervised, unsupervised, and reinforcement learning. It delves into the practical applications and real-world issues each method can address. Additionally, the unit provides a concise summary of each approach's distinctive features and potential challenges.

Credit : 3 || TQT : 30

Reference No : Y/650/4958

This unit introduces the multifaceted steps and procedures in constructing and assessing machine learning models. It explains the fundamental elements of the machine learning process, from data preparation and choosing the appropriate machine learning algorithm to partitioning data into training, testing, and validation sets to mitigate the underfitting risks. The unit also addresses identifying and resolving class imbalance, emphasising when such strategies are necessary. Given that many encountered machine learning models are supervised classification models, the unit acquaints learners with common performance metrics and how to interpret them. Lastly, the unit briefly discusses methods for managing model bias and variance

Credit : 3 || TQT : 30

Reference No : A/650/4959

This unit provides a foundational understanding of simple linear regression models, which are essential for predicting the value of one continuous variable based on another. Learners will gain the ability to estimate the line of best fit by computing regression parameters and comprehend the accuracy of this line. Furthermore, the unit expands on simple linear regression by introducing multiple and polynomial regression models, enabling the examination of relationships involving numerous variables. It outlines constructing simple, multiple, and polynomial linear regression models using Python, leveraging libraries like sci-kit-learn.

Credit : 3 || TQT : 30

Reference No : H/650/4960

This unit serves as an introduction to logistic regression and its role as a classification algorithm. It delves into the fundamentals of binary logistic regression, including the logistic function, Odds ratio, and Logit function. Additionally, the unit clarifies the distinctions between linear and logistic regression. Learners will acquire the skills to construct and visually represent a logistic regression model using Python. The unit will also educate learners on when to opt for logistic regression over linear regression, how to accurately interpret the outcomes of logistic regression, and how to select the optimal logistic model that effectively characterises the relationship under examination.

Credit : 3 || TQT : 30

Reference No : J/650/4961

This unit introduces the fundamental theory and practical application of decision trees. It elucidates the construction of basic classification trees by utilising the standard ID3 decision-tree construction algorithm. The unit further details how nodes are divided based on concepts from information theory, such as Entropy and Information Gain. Additionally, learners will gain hands-on experience constructing and evaluating decision tree models using Python.

Credit : 3 || TQT : 30

Reference No : K/650/4962

This unit introduces an unsupervised machine learning algorithm: k-means clustering. It aims to impart learners with an understanding of the underlying principles of the k-means clustering algorithm, including methods for determining the optimal number of clusters. Additionally, learners will gain practical experience constructing and assessing k-means models using Python and acquire skills in visualising the resultant sets.

Credit : 3 || TQT : 30

Reference No : L/650/4963

This unit introduces the emerging field of data science - synthetic data and its role in enhancing data privacy and security. In the contemporary information age, data collected by entities like Google and Facebook and giving these datasets is paramount. Inadvertent disclosure or leakage of this data poses a significant dual privacy. The unit covers topics on confidentiality, including data privacy, the imperative for privacy, and the legal framework surrounding it. It delves into conventional methods of safeguarding data privacy, such as anonymisation. Then, it introduces learners to differential privacy and mental challenges in balancing data privacy and data utility.

Credit : 6 || TQT : 60

Reference No : M/650/4964

This unit introduces learners to another burgeoning field in data science - graphs and graph data science. It provides a beginner-friendly introduction to graph theory, a foundational concept underlying modern graph databases and analytics. The unit also explores the graph ecosystem, introducing concepts like Knowledge Graphs, Labelled Property Graphs, and RDF graphs for data storage and processing. Additionally, learners will be introduced to graph algorithms, essential tools for modelling, storing, retrieving, and analysing graph-structured data.

Credit : 6 || TQT : 60

Delivery Methods

The program is delivered by Edvoro, a flagship of the School of Business and Technology London, and awarded by QUALIFI; Edvoro offers a range of flexible delivery methods to cater to the diverse needs of its learners. These options include online and blended learning, allowing learners to select the mode of study that best suits their preferences and schedules. The program is designed to be self-paced and is facilitated through Edvoro's state-of-the-art Learning Management System.

Edvoro ensures learners can engage with their tutors through the Edvoro Support Desk Portal System. This platform enables learners to discuss course materials, seek guidance and assistance, and request feedback on their assignments, fostering a dynamic and interactive learning experience.

Edvoro stands out by providing exceptional support and infrastructure for online and blended learning formats. We have adopted an innovative approach to learning, replacing traditional classroom-based instruction with web-based learning while maintaining an exceptionally high level of support. Learners who enrol at Edvoro benefit from the dedicated guidance of a tutor throughout their learning journey, ensuring comprehensive support from beginning to end, whether they choose the online or blended learning option.

Resources and Support

Edvoro is committed to providing unwavering support throughout your educational journey. Our dedicated support team is a crucial link between tutors and learners, ensuring that guidance, assessment feedback, and additional study assistance are delivered promptly and effectively. When a learner submits a support request via the support desk portal for advice, assessment feedback, or any other service, one of the support team members assigns the request to an appropriate tutor. Once the support team receives a response from the allocated tutor, the information is promptly made accessible to the learner through the portal. This structured support system is designed to assist learners and streamline support processes efficiently.

Edvoro's competitive edge is enhanced by the high-quality learning materials crafted by industry experts. The learning materials encompass well-structured pathway books, letter notes, practical applications with real-world examples, and case studies that empower learners to apply their knowledge effectively. Learning materials are conveniently accessible in one of three formats, PDF, PowerPoint, or Interactive Text Content, through the learning portal, providing learners with versatile options for accessing and engaging with the content.

Study Options
  • Duration 12 Months
  • Credits 60
  • Accreditation Ofqual.Gov.UK
  • Intake Every Month
  • Study Mode Online / Blended
  • Course Materials: Well Structured
  • All Inclusive Cost Yes
  • Tutor Desk Yes
  • Support Desk Yes
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