Faculty of Information Technology Intelligence – National Higher Certificate – Data Management Sciences NQF Level 5 – 120 Credits
National Higher Certificate – Data Management Sciences NQF Level 5
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data.
Data science is a “concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that “everything about science is changing because of the impact ofk information technology” and the data deluge
Course annual Cost : R25 000
Monthly Payments : R2 000 (12 Months)
Once off registration Fee : R2 000
First Semester Theoretical Modules: February – July
Module 1 – Introduction to Data Management Sciences (DMS 111)
History of Data Science
Understanding the of Significance Managing Data
Understanding Data Security Management
Understanding Databases and Data Centers
Understanding Data Interpretation and Data Visualization
Data Mining and Artificial Intelligence
Data storage and Data Protection
Data laws and Regulations
Module 2 – Understanding Data Center and Servers (DCS 111)
Understanding Information Sytems and Computer sciences
Understading Data Storage Facilities and Hardware
Maintaining and Protecting Data Storage Facilities
Understanding Servers and Information Technology
Maintaining and Upgrading Servers
Networking and Servers
Module 3 – Data Mining and Artificial Intelligence (DMAI 111)
Introduction to Data Mining
Introduction to Machine Learning and Artificial Intelligence
Understanding Statistics and Mathematics
Data Collections and Interpretation
Data Mining Software and SDKs
Module 4 – Data and E – Cormmerce (DEC 111)
Data management and Business Management
Data management and Basic Accounting
Data management and basic Financial Management
Data management and Economics
Data Science and BlockChain Technology
Module 5 – Data Security, Laws and Regulation (DSLR 111)
Data Science and Cyber Security
Data Privacy vs Data Mining
Global Laws and Regulations – Data Sharing and Security
Data Security Governance
Data Protection and Nations Security
Critical Infrastructure Data Protection
Module 6 – Understanding and Programming Databases and MySQL (ProData 111)
Introduction to Databases
Microsoft Access And MySQL
Database Programming and Security
Database Management and Update
Databases and Data Storage
Module 13– Communications and Self Development (COMSELF 111)
Learning and Reading
How to Study Effectively
Virtual and Face to Face Presentations
Time Management Tactics
Financial Management Tactics
Understanding Self Development
Ethics and Psychology
Entrepreneurship and Innovation
Mid Term Exams – Theoretical Learning
Second Semester – Practical Work Integrated Learning Modules
Module 7 – Practical Data Management Tactics (PDMT 211)
Module 8 – Creating and Managing Databases (MYSQL 211)
Module 9 – Managing and Maintaining Data Centers and Data Storage facilities (DMAN 211)
Module 10 – Data mining and Analytics (DMINE 211)
Module 11 – Data Visualization and Data Exploration (DVISE 211)
Module 12 – Understanding data privacy laws and regulations ((DREGU 211)
Final Term Exams – Theoretical and Work Integrated Learning
National Senior Certificate (NSC) with diploma or Higher Certificate entry or an equivalent foreign secondary qualification or international school-leaving certificate on an NSC level (NQF 4) confirmed by SAQA.
National Certificate (Vocational) level four issued by the Council of General, Further Education, and Training.
This course takes a minimum of one year and maximum of three years.
Theoretical work is covered over a period of Six Months from enrollment from January until June. Practical work takes six months and begans in August Until January the next year.
Students enrolling in July start theoretical work from July until December. Practical work commences in January and ends in June.
Course Learning Outcome
At the end of the course, graduates will have the technical skills to collect, analyse and manage data using statistical and mathematical tools.
Graduates will be equipped with the technical tools to maintain and manage data centers, servers and other storage facilities
Data Science Career Options.
As more and more industries see the benefit of using analytical data to improve business practices, big data and data science career opportunities are exploding. Employment in data science related careers is projected to grow 11 percent from 2014 to 2024, according to the Bureau of Labor Statistics (BLS), which is much faster than other careers.
Data science related occupations are likely to enjoy excellent job prospects, as many companies report difficulties finding highly skilled workers. The good news is that there are a number of different kinds of paths that a data science career can take. The challenge is that it can sometimes be difficult to understand how these careers differ and what kinds of skill sets are required for each.
That’s where the Data Science Career Center comes in. The goal of this resource is to introduce the different types of careers in the data science field and summarize the skill set needed for each. For more comprehensive information about detailed roles, skills required, salary information, and job outlook, click on each individual career link.
The title “data scientist” is relatively new and is not yet clearly defined. Due to the fact that it lacks specificity it can sometimes be perceived as an elevated synonym for “data analyst.” But that’s not the case. A data scientist possesses a combination of analytic, machine learning, data mining, and statistical skills in addition to experience with algorithms and coding. Data scientists also have expertise in the following programs: R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark. But maybe the most important skill that a data scientist possesses is the ability to explain the significance of data in a way that can be easily understood by others. Check out our Data Scientist Salary Guide.
A data analyst’s role is to collect, process, and perform statistical data analyses with the goal of helping companies make better business decisions. Data analysts are most often responsible for transforming data sets into usable forms, such as reports or presentations. Depending on the industry, this may involve gleaning insight from consumer data sets, making strategic recommendations based on dense financial data, or simply organizing messy data into a more accessible format. A qualified data analyst will have a solid understanding of R, Python, HTML, C/C++ and SQL. These positions are often on the lower end of the organizational chart; however, those are who just entering the data science field will find these roles to be some of the easiest to qualify for and you will have ample opportunity to learn and advance into higher level roles. Visit our Data Analyst Salary Guide.
Data engineers are the designers, builders, and managers of the information or big data infrastructure. They assist in developing the architecture that helps analyze and process data in a manner best suited for their organization. It is their role to also make sure those systems are performing smoothly. Data engineering differs from other data science careers in that it is focused on the systems and hardware that facilitates a company’s data activities, rather than analysis of the data itself. A data engineer has a background in software engineering as well as skills in the following languages: SQL, HIVe, Pig, R, Matlab, SAS, SPSS, Python, Java, and Ruby. Their duties also involve providing the company with valuable data warehousing solutions. This role is considered a senior position and requires an advanced degree and years of experience.
The business analyst is often less technically oriented, but has a deep knowledge of the different business processes and embodies business intelligence. The role of the business analyst is to improve business processes by serving as a liaison between business and IT with a clear directive to focus on advancing strategic business objectives. Most business analysts are focused on producing usable deliverables, such as reports and presentations, that can be easily understood by others in the organization who aren’t data scientists themselves. Business analysts possess the basic skills of data visualization tools and data modeling, however their educational background is in business. The duties of a business analyst are very similar to those of a data analyst. Business analysis is an excellent career choice for someone that has a strong foundation in numbers and an active interest in business management or development. Visit our Business Analyst Salary Guide.
A market analyst studies information to better assist companies in making informed decisions about market opportunities. The goal is to determine which product a company should produce and how to sell it. A market analyst uses statistical, math, and analytical skills while interpreting large data sets. This career is more of an entry level job in data science.
Thanks to the increasing importance of big data, data architect roles are becoming more common. This position creates the blueprints for data management systems to integrate, centralize, protect, and maintain data sources. They understand the languages of SQL, XML, Hive, Pig, and Spark as well as the skills of warehouse solutions, systems development, and database architecture. It is a natural evolution from data analyst to database designer, combining both skill sets. The position requires an advanced degree and many years of experience.
Data and analytics managers lead data science teams. These management positions not only possess data science technical skills, but also leadership and project management experience. They manage a variety of positions including: data engineers, data scientists, data analysts, and often serve as the spokesperson for the department. This role is a senior position that requires an advanced degree as well as many years of supervisory experience.
Business intelligence analysts gather data in a variety of ways, some of which include: mining a company’s computer data through software; reviewing competitor data and industry trends to develop an understanding of where the company stands in the overall picture; and identifying ways in which they can improve and reduce costs. This position requires an advanced degree and years of experience as a business analyst.
A data mining specialist is responsible for identifying patterns and relationships to help a company predict future behaviors. Through the process of transforming data into insights, a data mining specialist can help businesses make more intelligent, data driven decisions. To accomplish this, a data mining specialist uses statistical software to help research, mine data, and model relationships.
A statistician is someone who works with mathematical techniques to help analyze and interpret data to solve real world problems. Statistician’s can work in a variety of fields including (but not limited to) academia, government, healthcare, business, engineering, and marketing. A statistician can choose to work as a generalist but specialization within a specific field can also help them stand out to potential employers.