With the emergence of cloud computing, big data and artificial intelligence, data science has become a key fourth generation profession. The Level 7 Postgraduate Diploma in Data Science has been developed to prepare aspiring Data Scientists, Data Analysts and Artificial Intelligence specialists to take advantage of the growing business and employment opportunities in these fields.
目錄
ToggleThe Diploma is designed to enable learners to gain skills in maths, statistics and programming in R, Python and SQL. The Diploma also provides a sound basis for a progression to Masters Degrees in a number of relevant disciplines.
課程名稱
QUALIFI Level 7 Diploma in Data Science
課程等級
文憑 (第7級)
課程學分
120學分
評估方法
以功課為主
開始時間
每兩個月
頒發證書
Qualifi
銜接大學
UK University
學習模式
你可以100%通過遙距模式(網上)學習第7級數據科學文憑。 您將獲得學習資料,並有一對一的網上輔導進行作業反饋。 此外,您還將獲得入讀英國碩士學位最後階段的高級入學資格。
Unit Reference | Mandatory Units | Level | Credit |
J/618/4970 | Exploratory Data Analysis | 7 | 8 |
L/618/4971 | Statistical Inference | 7 | 12 |
R/618/4972 | Fundamentals of Predictive Modelling | 7 | 15 |
Y/618/4973 | Qualitative Research Advanced Predictive Modelling | 7 | 15 |
D/618/4974 | Time Series Analysis | 7 | 15 |
H/618/4975 | Unsupervised Multivariate Methods | 7 | 15 |
K/618/4976 | Machine Learning | 7 | 15 |
M/618/4977 | Further Topics in Data Science | 7 | 15 |
T/618/4978 | Contemporary Themes in Business Strategy | 7 | 10 |
Total | 120 |
QUALIFI Level 7 Diploma in Data Science第7級數據科學文憑
》 Unit DS01: Exploratory Data Analysis
This unit provides learners with an in-depth understanding of R and Python programming and the fundamentals of statistics. This includes writing R and Python commands for data management and basic statistical analysis. The unit will help the learner to understand and perform descriptive statistics and present the data using appropriate graphs/diagrams and serves as a foundation for advanced analytics. Most industry analysis starts with Exploratory Data Analysis and a thorough study of this will help learners to perform data health checks and provide initial business insights.
》 Unit DS02: Statistical Inference
This unit provides learners with an in-depth understanding of statistical distribution and hypothesis testing. Statistical distributions include Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square. Parametric and non-parametric tests used in research problems are covered in this unit. The unit will help learners to formulate research hypotheses, select appropriate tests of hypothesis, write mainly R programs to perform hypothesis testing and to draw inferences using the output generated. Learners will also study planned experiments as part of the unit
》Unit DS03: Fundamentals of Predictive Modelling
This unit provides a strong foundation for predictive modelling. Its objective is to define the entire modelling process with the help of real life case studies. Many concepts in predictive modelling methods are common and therefore, these concepts will be discussed in detail in this unit. A good understanding of predictive modelling leads to a smart data scientist as many business problems are related to successfully predicting future outcomes.
》 Unit DS04: Advanced Predictive Modelling
In this unit, learners are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this unit covers detailed model building processes for binary dependent variables. In addition, multinomial models and ordinal scaled variables will also be discussed.
》 Unit DS05: Time Series Analysis
The objective of this unit is to discuss time series forecasting methods. Learners will analyse and forecast macroeconomic variables such as GDP and inflation. Panel data regression methods will also be discussed in this unit.
》 Unit DS06: Unsupervised Multivariate Methods
Data reduction is a key process in business analytics projects. In this unit, learners will learn data reduction methods such as PCA, factor analysis and MDS. They will also learn to form segments using cluster analysis methods. Forming segments and then analysing is a key technique for large groups of data and their intrinsic information comes out in detail once segmented thoughtfully.
》 Unit DS07: Machine Learning
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this unit, learners will understand applications of various machine learning algorithms for classification problems
》 Unit DS08: Further Topics in Data Science
In this module, learners will learn how to analyse unstructured data using text mining. The focus will be on sentiment analysis of text data, including data available on social media. For building interactive web apps straight from R, the concept of the “SHINY” package will be introduced. Big Data concepts and artificial Intelligence will be covered in the unit, as well as an introduction to SQL programming and how it is used to handle data.
》 Unit DS09: Contemporary Themes in Business Strategy
The convergence of Cloud computing, Big Data, Artificial Intelligence and The Internet of Things will see organisations of all shapes and sizes either survive and thrive or face extinction. New operational and strategic norms, types of organisations, the nature of work and employment are changing fundamentally across vast parts of the global economy. This unit introduces learners to the strategic and managerial challenges generated by the impact of digital technology on business and organisations.
Recognition of Prior Learning
Recognition of Prior Learning (RPL) is a method of assessment (leading to the award of
credit) that considers whether learners can demonstrate that they can meet the assessment
requirements for a unit through knowledge, understanding or skills they already possess
and so do not need to develop through a course of learning.
入學要求
》19歲以上
》大專畢業或相關學歷
》英語水平良好