Data Science Courses
Get Internationally Accredited & Recognized
Data Science Courses
Questions to ask yourself about the Data Science Courses.
Is Data Science in demand? – Definitely yes and very highly. As a matter of fact there’s a real shortage of data scientists in the national and global workplace. Not to mention this creates a massive demand for Machine Learning courses. The top 3 scarce jobs in South Africa today is Data Scientists (using python programming or programming in R ) and the need for Data Analysts.
Is what you learn in the Data Science Course relevant and of value? – Definitely yes and learn from experienced data scientists (Mentors). Not to mention strong, up to date relevant libraries and frameworks.
Can you get a job as a data scientist after? – Definitely yes, in addition study further towards your Microsoft Certified: Azure Data Scientist Associate, AWS Certified Data Analytics – Specialty and/or AWS Certified Machine Learning – Specialty exams (internationally accredited and recognized):
Why the Data Science Course?
Overall Data Science Makes Data Better. In turn companies require skilled Data Scientists to process and analyze their data. Furthermore they not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company. Overall master the python programming or programming in R languages. In turn this will let you also understand data science libraries such as: Matplotlib, SciKit Learn, NLTK and NumPy.
The Data Science Course Overview
Data Science at School of IT consists of several valuable knowledge sets and skills. In turn Data science is a “concept to unify statistics, data analysis 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. wiki
Who should attend the data science course?
Whether you are new to data analysis or need more advanced machine learning. In turn The introduction to data analysis will provide you the best beginner analytical skills. Moreover it will provide you with logic and the knowledge to start analyzing data and alter code.
Learn Data Science anytime, anywhere!
Overall learn Data science anytime and anywhere. We have 3 training options and we have award-winning coding courses. In turn identify and implement data plans and pipelines with any cloud based service: Microsoft Azure or AWS.
Fast forward your career in the IT industry with a part-time course at School of IT. In turn Part-time courses allow working professionals to transition into a new skill set while working. Moreover at School of IT we are agile and customize a course to the individual.
Ready to start a career in IT? Learn to Code as a full time student at School of IT. Thus beginning your career in data science.
Learn to Code and prepare for the future while you’re still in high school. Thus no matter where you are, we give you the analytical skills to pursue your dreams!
Learn to Code and up skill yourself or your company while you’re working. Thus no matter where you are, we give you the tools to move up in your company.
Data Science Courses Objectives.
By the end of the data management course, students will have usable knowledge of the following:
- Overall apply statistical methods to data
- Be able to understand Python or programming in R to explore and transform data.
- Understand and create, validate machine learning models with Azure Machine Learning.
- Thoroughly Understand Python/Programming in R code to build machine learning models.
- Apply data science techniques to common scenarios.
- Introduction to compute targets.
- Acquire the skills needed to find structure in data and how to use the python libraries.
- SciKit Learn.
- Implement a machine learning solution for a given data problem.
- Overall learn advanced Python or programming in R and Data Analysis to implement computer science algorithms.
- Designing and Implementing a Data Science Solution on Azure.
- Build AI solutions with Azure Machine Learning.
- Azure Machine Learning tools and interfaces.
- Train a machine learning model with Azure Machine Learning.
- Azure Machine Learning estimators.
- Working with Data in Azure Machine Learning.
- Introduction to compute targets and environments.
- Orchestrating machine learning with pipelines.
- Pass data between pipeline steps.
- Deploying machine learning models with Azure Machine Learning.
- Deploy models as a real-time service.
- Consume real-time inferencing service, including troubleshooting.
- In turn Automate machine learning model selection with Azure Machine Learning.
- Automated machine learning tasks and algorithms.
- Preprocessing and featurization.
- Thus Run automated machine learning experiments.
Overview of Data Analytics in AWS.
Overall the AWS data lakes and analytics services. In turn you build, design and maintain analytics solutions on AWS. Furthermore The exam can be taken at a testing center or from the comfort and convenience of a home or office location as an online proctored exam.
By the end of the AWS Data Analytics Course, students will have usable knowledge of the following:
- Overall define AWS data analytics services and understand how they integrate with each other.
- Be able to explain AWS data analytics services. Thus to fit in the data life cycle of collection, storage, processing, and visualization.
Overview of Machine learning in AWS.
Overall the AWS Machine Learning Specialty is for individuals who perform a development or data science role. In turn the goal of the course is the ability to design, implement, deploy. Including maintain machine learning (ML) solutions for organizations. Furthermore The exam can be taken at a testing center or from the comfort and convenience of a home or office location as an online proctored exam.
By the end of the AWS Machine Learning Course, students will have usable knowledge of the following:
- Overall choose and justify the appropriate ML approach for a given organization problem.
- Be able to Identify appropriate AWS services to implement ML solutions.
- Design and implement scalable, cost-optimized, reliable, and secure ML solutions.