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Qualifi Level 7 Diploma in Data Science

What you'll learn

The Qualifi Level 7 Diploma in Data Science is an advanced program that provides learners with specialized knowledge and practical skills necessary for leadership positions in data science. The curriculum covers various topics, including advanced data analysis, machine learning, big data technology, and data visualization.

Key Benefits

  • Achieve thorough mathematical and statistical proficiency for advanced data analysis.
  • Excel in programming languages like R, Python, and SQL for proficient data analysis.
  • Cultivate robust data management abilities, encompassing data evaluation, structuring, and cleansing.
  • Acquire effective data visualisation techniques and tools.
  • Develop a deep understanding of classical data analytics, including statistical inference, predictive modeling, time series analysis, and data reduction.
  • Apply common machine learning techniques to address business and real-world challenges.
  • Grasp fundamental concepts from contemporary business themes.
  • Effectively apply data science and analytics within business and organisational contexts.

About Awarding Body

QUALIFI, recognised by Ofqual awarding organisation has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Leadership, Hospitality & Catering, Health and Social Care, Enterprise and Management, Process Outsourcing and Public Services. They are liable for awarding organisations and thereby ensuring quality assurance in Wales and Northern Ireland.

What is Included?

  • 24/7 to our compressive learning management platform, where you will be able to access vital learning resources and communicate with the support desk team.
  • Quality learning materials such as structured lecture notes, study guides, and practical applications, which include real-world examples and case studies, will enable you to apply your knowledge.
  • A dedicated tutor for individual students to help and provide suggestions throughout the course.
  • Provide formative assessment with formative feedback will be supporting the learner to improve the achievements.
  • Independent accessibility of Online learning portal, ASML delivers the course straight to the student’s smartphone, tablet, laptop or desktop through which will enable them to study at their convenience.

Assessment

  • Time-constrained scenario-based assignments
  • No examinations

Entry Requirements

The qualification has been designed to be accessible without artificial barriers that restrict access. In order to enrol for this programme, applicants must be aged 18 years or over. There are few other criteria as well, please see below:

  • Possess a minimum of a Level 6 qualification in a related sector.
  • Hold a Bachelor's degree.
  • Demonstrate at least three years of relevant work experience showcasing current and pertinent industry knowledge.
  •  In the case of applicants whose first language is not English, then IELTS 6 (or equivalent) is required.

Progression

After completing the Qualifi Level 7 Diploma in Data Science, learners can choose from the following pathways:

  • Enter the workforce directly within a related professional field.

Why gain Qualification

QUALIFI qualifications aim to support learners to develop the necessary knowledge, skills and understanding to support their professional development within their chosen career and or to provide opportunities for progression to further study. Our qualifications provide opportunities for learners to:

  • apply analytical and evaluative thinking skills
  • develop and encourage problem solving and creativity to tackle problems and challenges
  • exercise judgement and take responsibility for decisions and actions
  • develop the ability to recognise and reflect on personal learning and improve their personal, social, and other transferable skills.
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Course Content

Reference No : L/618/4971

This unit imparts a thorough comprehension of statistical distribution and hypothesis testing. It encompasses distributions like Binomial, Poisson, Normal, Log Normal, Exponential, t, F, and Chi-Square. Both parametric and non-parametric tests relevant to research are included. The unit guides learners in formulating research hypotheses, choosing suitable hypothesis tests, primarily utilising R programs for testing, and drawing inferences from the generated output. Additionally, planned experiments are part of the curriculum.

Credit : 12 || TQT : 120

Reference No : J/618/4970

This unit offers a comprehensive grasp of R and Python programming alongside fundamental statistics. It encompasses writing commands in R and Python for data handling and basic statistical analysis. The unit enables learners to execute descriptive statistics and present data using suitable graphs/diagrams and is a stepping stone for advanced analytics. Exploratory Data Analysis forms the basis of many industry analyses, and a thorough study of this equips learners to conduct data health assessments and offer initial business insights.

Credit : 8 || TQT : 80

Reference No : R/618/4972

This unit establishes a robust groundwork for predictive modelling. It aims to outline the entire modelling process through real-life case studies. Since numerous concepts in predictive modelling methods are widely applicable, this unit delves into them in depth. Proficiency in predictive modelling is crucial for a discerning data scientist, as many business challenges hinge on accurately forecasting future outcomes.

Credit : 15 || TQT : 150

Reference No : Y/618/4973

This unit introduces learners to developing models for categorical dependent variables. It focuses on the intricacies of constructing models for binary dependent variables, prevalent in domains like risk management, marketing, and clinical research. Additionally, the unit will delve into multinomial models and ordinal scaled variables.

Credit : 15 || TQT : 150

Reference No : D/618/4974

This unit aims to explore time series forecasting techniques. Learners will engage in the analysis and prediction of macroeconomic variables like GDP and inflation. Additionally, the unit will cover panel data regression methods.

Credit : 15 || TQT : 150

Reference No : H/618/4975

Data reduction is a pivotal step in business analytics endeavours. This unit instructs learners on techniques like PCA, factor analysis, and MDS for data reduction. Additionally, they will learn to create segments using cluster analysis methods. This process of segmenting and analysing is crucial for handling large datasets, as it unveils detailed insights once the segmentation is done thoughtfully.

Credit : 15 || TQT : 150

Reference No : K/618/4976

In this unit, learners will explore the applications of various machine learning algorithms and the next-generation techniques used alongside traditional predictive modelling methods. The focus will be on their use in solving classification problems.

Credit : 15 || TQT : 150

Reference No : M/618/4977

This module will instruct learners to analyse unstructured data through text mining. The emphasis will be on sentiment analysis of text data, including content from social media platforms. Additionally, learners will be introduced to the "SHINY" package for creating interactive web applications directly from R. The unit will also cover Big Data concepts and artificial intelligence, along with an introduction to SQL programming and its application in data management.

Credit : 15 || TQT : 150

Reference No : T/618/4978

Integrating Cloud computing, Big Data, Artificial Intelligence, and The Internet of Things is reshaping organisations worldwide. It's a make-or-break moment: they must adapt to thrive or risk becoming obsolete. This unit familiarises learners with the strategic and managerial hurdles posed by the digital technology revolution in business and organisations. This transformation affects operational and strategic norms, organisational structures, work dynamics, and employment paradigms on a global scale.

Credit : 10 || TQT : 100

Delivery Methods

Ascent School of Management offers a wide variety of courses and you are given a choice to select any one as per your need and requirement. You would be given the benefit to choose the time and place of study and the study materials are accessible 24/7 via our Learning Management System. They can be accessed from any part of the world at any time. You will be guided by a personal Tutor and supported by our back-office support team who will give your assistance and advice about your course and assignments.

At ASML, our online courses are flexible and designed to fit full-time working professionals. So you don’t miss out on valuable income, experience and career progress while you study. Our online study mode lets you set your study hours, and you have access 24/7 to our compressive learning materials. While you may have some activities at fixed times, such as assessments, you can access and work through your course at your convenience. You will also get adequate guidance and support from tutors via our support desk portal.

When you study online with us, you can expect top-class support from our dedicated support desk team, which means you’re never alone in your studies. You will get guidance, support and assessment feedback from your tutors through our support desk portal. The blended learning at ASML is designed to meet the needs of learners who want to widen their knowledge online with dedicated one-to-one online live sessions with tutors at their convenience. ASML is keen to offer an immersive learning experience to our learners with an innovative blended learning approach, which replaces traditional classroom-based learning with block delivery of online live teaching sessions. With our blended learning, you will get everything that comes with our online learning and plus live classes with assigned tutors.

About Awarding Body

QUALIFI, recognised by Ofqual awarding organisation has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Leadership, Hospitality & Catering, Health and Social Care, Enterprise and Management, Process Outsourcing and Public Services. They are liable for awarding organisations and thereby ensuring quality assurance in Wales and Northern Ireland.

Resources and Support

At Ascent School of Management London, we are dedicated to providing exceptional support to our learners every step of the way. Our centralized tutor support desk portal ensures that our support team can provide guidance, assessment feedback, and any other study support promptly and efficiently. Our support team is committed to assisting learners as quickly as possible. Whenever a learner submits a support request via the support desk portal, our team assigns it to an appropriate tutor. Once the allocated tutor responds to the request, the support team makes it available to the learner in the portal.

We understand that efficient support is essential for the success of our learners. That's why we have a support desk system in place to streamline all support processes efficiently. Our learners can rely on us to provide effective assistance whenever they need it. Furthermore, our learning materials are created by industry experts, giving our learners a competitive edge. These materials are available in three formats, including PDF, PowerPoint, or Interactive Text Content on the learning portal. We are confident that our high-quality materials will help our learners achieve their goals.

Study Options

10 Monthly Payments - £98.44

Initial Deposit - £246.11

Total Discount Applied - 40%

10 Monthly Payments - £133.00

Initial Deposit - £332.51

Total Discount Applied - 40%

  • Duration 12 Months
  • Credits 120
  • 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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