Duration
Mode
Intakes
September
January
May
Number of Units
Tuition
$18,950
you might be eligible
$14,950 with advance payment
$17,940 with installment
Course Overview
Data Science enables learners to gain skills in maths, statistics and programming, Python and SQL to organise, analyse and visualise data to uncover hidden solutions that challenge traditional business assumptions and produce entirely new operating and strategic models.
- Apply analytical and evaluative techniques and to enhance those skills
- Investigate issues and opportunities
- Develop their awareness and appreciation of managerial, organisational and
environmental issues
- Develop their awareness and appreciation of managerial, organisational and
- Use management techniques and practices in imaginative ways
- Make use of relevant information from different sources
- Develop and encourage problem solving and creativity to tackle problems and
challenges
- Develop and encourage problem solving and creativity to tackle problems and
- Exercise judgement and take responsibility for decisions and actions
- Develop the skills to acknowledge and reflect on personal learning and improve personal, social and other transferable skills.
- Qualifi Award of L7 (9 subjects) equal credit toward your degree to Master of Data Science top up (dissertation process) at University of Chichester.
Learning Outcomes
Gain qualification from an internationally recognized awarding organization.
Learn from a curriculum supported by the most recent content relevant to a contemporary
business environment.
Develop new skills and knowledge that can be applied immediately and in the field of data science and analytics.
Progress along a pathway to gain a Master's degree or beyond.
Have assessments marked and moderated by respected academic and practitioner professionals with practical experience in data science and analytics.
Overall
- Gain the mathematical and statistical knowledge required to carry out basic and advanced data analysis.
- Develop sufficient skill in programming languages to use them to successfully carry out data analysis to an advanced level.
- Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis.
- Become familiar with and use the tools and techniques used in data visualisation
- Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, time series analysis and data reduction.
- Apply common machine learning techniques to business and other problems in order to uncover options and solutions for them.
- Develop an understanding of essential concepts from contemporary themes in business.
- Understand, evaluate and apply data science and analytics within business and organisational contexts
Career Opportunities
– Data Scientist
– Artificial Intelligence Specialist
– Data Analyst
– Business Analyst
– Statistical Analyst
The learning outcomes of the diploma are
1- Gain the mathematical and statistical knowledge required to carry out basic and advanced data analysis.
2- Develop sufficient skill in programming languages to use them to successfully carry out data analysis to an advanced level.
3- Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis.
4- Become familiar with and use the tools and techniques used in data visualisation
5- Develop a comprehensive knowledge of classical data analytics, including
statistical inference, predictive modelling, time series analysis and data reduction.
6- Apply common machine learning techniques to business and other problems in order to uncover options and solutions for them.
7- Develop an understanding of essential concepts from contemporary themes in business.
8- Understand, evaluate and apply data science and analytics within business and organisational contexts.
Subjects
- Exploratory Data Analysis
- Statistical Inference
- Fundamentals of Predictive Modelling
- Advanced Predictive & Modelling
- Time Series Analysis
- Unsupervised Multivariate & Methods
- Machine Learning
- Further Topics in Data & Science
- Contemporary Issues in
+ Dissertation Process
Admission Requirement
- Passport or National ID
- Resume or CV
- Personel statement
- Reference letter
- High School/Bachelor Transcript (English Translated)
- English Proficiency-Minimum requirements DUOLINGO 105/160 or IELTS 6.5/9 orTOEFL 80/120 or Cambridge English Placement Test CEPT) 40/50 etc.
Scholarship
Univaf offers a scholarship to all applicants up to %80.
Learn your scholarship eligibility.