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Qualifi Ltd

Level 7 Diploma in Data Science

At A Glance

Course Title

Level 7 Diploma in Data Science

Course Level

Postgraduate Diploma

Course Credits

120 Credits

Study Method

Distance Learning

Start Date

Please make an enquiry

Course Duration

12 months

Awarded By

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Delivered By

Digita Logo

Course Description

With the emergence of cloud computing, big data and artificial intelligence, data science has become a key fourth generation profession. The convergence of these technologies has given rise to new and powerful approaches to developing business insights and decision making based on the analysis of vast amounts of data using advanced statistical techniques and complex machine learning algorithms.
Developed at a postgraduate level, Digita’s diploma programme enables you to gain skills in maths, statistics and programming to organise, analyse and visualise data to uncover hidden solutions that challenge traditional business assumptions and produce entirely new operating and strategic models. Once you graduate, you’ll be in a position to take advantage of the rapidly growing opportunities in a wide range of data science and advanced analytics roles throughout all kinds of organization.
Upon completing this qualification, you will be able to progress directly into Master’s degrees in Data Science and Big Data Analytics.

Course Content

Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of R and Python programming, as well as the fundamentals of statistics. This includes writing R and Python commands for data management, basic statistical analysis, performing descriptive statistics and presenting data using appropriate graphs and diagrams. This unit serves as a foundation for advanced analytics.
In this unit you will gain an in-depth understanding of statistical distribution and hypothesis testing. This includes Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square distributions, as well as parametric and non-parametric tests used in research problems. The unit will help you to formulate research hypotheses, select appropriate tests for them, write R and Python programs to perform hypothesis testing and draw inferences using the outputs generated.
A good understanding of predictive modelling is an essential part of being an effective data scientist as many business problems are related to successfully predicting future outcomes. This unit provides a strong foundation for predictive modelling and covers the entire modelling process in the context of real life case studies. Many concepts in predictive modelling methods are commonly used in business and therefore these concepts will be discussed in detail.
In this unit you will learn model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and detailed model building processes for binary dependent variables are covered. In addition, multinomial models and ordinal scaled variables will also be discussed.
In this unit, time series forecasting methods are introduced and explored. You will analyse and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing and other models.
Data reduction is a key process in business analytics projects and you will learn to apply data reduction methods such as principal component analysis, factor analysis and multi- dimensional scaling. They will also learn to segment and analyse large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
Machine learning algorithms are new generation algorithms and used in conjunction with classical predictive modelling methods. In this unit learners will understand applications of various machine learning techniques including the Naïve Bayes Method, Support Vector Machine Algorithm, Decision Tree, Random Forest, Association Rules and Neural Networks.
In this unit, learners are introduced to further key knowledge areas associated with data science. This includes analysis of unstructured data using Text Mining, handling data with SQL and building interactive web apps straight from R using the Shiny package. Learners also explore the Hadoop framework and further concepts in Big Data Analytics and Artificial Intelligence.
This is a business focussed unit that introduces you to key business concepts at a postgraduate level to complement your data science skills and knowledge. It covers topics including leadership skills, entrepreneurship, innovation, ethics and sustainability, globalisation and organisational culture, encourging you to evaluate data science within wider contexts.

Study Method

The Qualifi Level 7 Diploma in Data Science is studied 100% online through Digita. You will be provided access to study materials, dedicated one-on-one tutoring and assignment feedback through qualified tutors.

On successful completion of your studies your certificate and transcript will be processed within 10 working days. Moreover, you will gain advanced entry into Master’s degrees in Data Science and Big Data Analytics.

Course Fees

Please get in touch with us to get the most up-to-date course fee for this programme.

Entry Requirements

  • Your existing educational qualifications and work experience will be directly taken into account, allowing for module exemptions whenever possible. Please make an enquiry below.

Enrol Now

Due to high demand, this postgraduate diploma programme has intakes every 2 months. Click the Enrol Now button and share your information so that one of our Academic Advisors will get in touch with you promptly.

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