Professional certification in IT Support & Solutions, IT Technical

Data Technician Apprenticeship

Our IT training courses are developed with industry-standards and career-focused technologies.


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Award:
Certification
Duration & Study Mode:
Full time, Part-time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

Apprenticeships are typically designed to provide individuals with a combination of on-the-job training and formal education to develop the skills and knowledge needed for a specific career.

Here is a general overview of what a Data Technician Apprenticeship might entail:

1. Program Structure:

  • Duration: Apprenticeship programs can vary in length, but they often last for a specified period, combining classroom instruction with practical, hands-on experience.
  • Components: The program may include a mix of classroom training, workshops, and real-world work experience under the supervision of experienced professionals.

2. Curriculum:

  • Core Subjects: The curriculum is likely to cover fundamental topics related to data management, analysis, and technology.
  • Technical Skills: Apprentices may learn about database management, data processing, data analysis tools, programming languages, and other relevant technologies.
  • Soft Skills: Communication, problem-solving, teamwork, and other soft skills may also be emphasized to ensure well-rounded professional development.

3. On-the-Job Training:

  • Practical Experience: Apprenticeships typically involve hands-on work in a real-world setting. This could include tasks related to data collection, data cleaning, analysis, and other data-related activities.
  • Mentorship: Apprentices may work closely with experienced data technicians or data scientists who provide guidance and mentorship.

4. Assessment and Certification:

  • Evaluation: Apprenticeship programs often include assessments to gauge the apprentice’s progress and understanding of the material.
  • Certification: Successful completion of the program may lead to a certification or qualification recognized in the industry.

5. Industry Relevance:

  • Adherence to Standards: The program may align with industry standards and best practices to ensure that apprentices acquire skills that are relevant and in demand.

6. Employer Involvement:

  • Collaboration: Employers often play a significant role in apprenticeships, providing opportunities for hands-on experience and contributing to the development of the curriculum to meet industry needs.

7. Support and Guidance:

  • Support System: Apprenticeships may include support mechanisms such as mentorship, counselling, or regular check-ins to help apprentices overcome challenges and succeed in the program
Group-Icon2
Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

Here is a general outline of topics that are commonly covered in data-related apprenticeships. Keep in mind that the actual syllabus may differ. Here's a broad overview:

  1. Introduction to Data Science and Data Technologies:
  • Overview of data science and its applications.
  • Introduction to data technologies, databases, and data storage.
  1. Data Analysis and Visualization:
  • Data exploration and analysis techniques.
  • Visualization tools and techniques for presenting data.
  1. Database Management:
  • Understanding and working with relational databases.
  • SQL (Structured Query Language) for data retrieval and manipulation.
  1. Data Cleaning and Preprocessing:
  • Techniques for cleaning and preprocessing raw data.
  • Handling missing data and outliers.
  1. Programming Skills:
  • Introduction to programming languages commonly used in data-related tasks (e.g., Python, R).
  • Basics of scripting for data manipulation and analysis.
  1. Statistical Concepts:
  • Basic statistical concepts for data analysis.
  • Probability distributions and hypothesis testing.
  1. Machine Learning Basics:
  • Overview of machine learning concepts and algorithms.
  • Application of machine learning in data analysis.
  1. Data Security and Privacy:
  • Understanding data security and privacy issues.
  • Compliance with relevant regulations and best practices.
  1. Data Ethics:
  • Ethical considerations in data handling and analysis.
  • Responsible use of data.
  1. Project Work and Case Studies:
  • Real-world projects to apply learned skills.
  • Case studies to understand practical challenges and solutions.
  1. Communication Skills:
  • Effective communication of findings and insights.
  • Collaborative work and teamwork in a data-related environment.

Remember, this is a general outline, and the specific content and depth of each topic can vary based on the program and its goals.

When would you like to start?

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5/02/202410:00 – 18:00Mon - Fri5 Days Classroom / OnlinePlaces available Future Dates/Request Price
12/02/202410:00 – 18:00Mon - Fri5 DaysClassroom / OnlinePlaces available Future Dates/Request Price
17/02/202410:00 – 16:00Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
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