Data Science Course

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CertifiedEducation quality

02/2023

Digital Skola

Data Science

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Data Science Course by Digital Skola is for adult upskilling. It is a 13 week online course.

Digital Skola is an Edutech platform with a vision to cultivate ready-to-work digital talents. Its flagship program, bootcamp, is an end-to-end training that covers hard skills, soft skills, and career preparation. Digital Skola uses an up-to-date curriculum, aligned with industry needs. Some of its training topics include Data Science, Data Engineer, Digital Marketing, Fullstack Development, UI/UX Design, Python, SQL, etc.

Each program is equipped with a comprehensive curriculum and facilities such as hands-on sessions, portfolio-making, internship, professional branding, career coaching & mentoring, and a job connector.

Age groups

Professional education

Languages

Bahasa Indonesia

Platform

Browser-based

Desktop Windows

Desktop Mac

Registration

Required

Offline play

Internet required

Pictures

Videos

Pedagogy

Educational Quality

Learning Goals

The pedagogical analysis covers how the product supports learning of the identified skills. The student’s role is assessed by four contrary pair parameters, which are selected to cover the most essential aspects on the use of the product.

Passive

Active

Digital Skola course's best asset is the various methods students can learn. There is work for individuals but also group work as well. In order to progress the user is required to acquire and use new information and pass the assignments in a certain level. The course has a variation of assignments, projects and summary activities (eg. quizzes).

Rehearse

Construct

The final project allows students to learn in a creative and critical manner, and mini-projects and other activities prepare for that well. They present cases and problems to solve, where success is based on the ability to adapt knowledge that is given in the lessons. The course has an interesting focus on professional branding, where the students learn and revise their knowledge while also building their profile in LinkedIn.

Linear

Non-linear/Creative

Learning is quite linear and progresses through clear steps. For students to succeed in the final and mini projects, students must be able to learn the topics in order and utilize previously learned with the new. Therefore, linear approach is needed and it is set out well here with various activities for students to practice their new understandings. The assessment criteria for tasks and the project are clear and focus also on soft skills.

Individual

Collaborative

Students need to collaborate to learn, especially when it happens online. This keeps students motivated. The collaboration happens on projects, where the collaboration is also peer-reviewed. Also professional branding activities create a learning community, that expand outside of the virtual classroom. There is a good amount of individual work as well, led by the tutor.

The following are the high educational quality aspects in this product.

The course introduces clear steps on how to build ones knowledge on data science, and learning with a cohort keeps the student motivated.

Learning through assignments, mini-projects, homework and final project is an active way of learning that requires adapting knowledge.

Professional branding as a part of the course is a good way to link ones learning to real life.

The supported learning goals are identified by matching the product with several relevant curricula descriptions on this subject area. The soft skills are definitions of learning goals most relevant for the 21st century. They are formed by taking a reference from different definitions of 21st century skills and Finnish curriculum.

Subject based learning goals

Data Preprocessing.

The principles of deployment and technologies related to that.

Understand basic logic of how programming languages work.

Professional branding and building your CV.

Preparing reports based on data.

Understand the stages of data science and skills of data scientists

Understanding databases, data types and data lifecycle.

Produce Python code including data types, functions, numpy, and Pandas.

Using Git & Version Control System

Advanced statistical methods: Sampling, hypothesis testing and. A/B testing.

Using SQL core queries

Data visualization methods.

Using libraries in programming.

Data mining and machine learning.

Centric descriptive statistics, correlation and causalisation.

Soft skills learning goals

Practicing to find, evaluate and share information

Practicing to use information independently and interactively

Learning to acquire, modify and produce information in different forms

Using technology as a part of explorative and creative process

Practicing to notice causal connections

Learning to build information on top of previously learned

Encouraging to build new information and visions

Practicing logical reasoning to understand and interpret information in different forms

Learning to combine information to find new innovations

Practicing to notice links between subjects learned

Learning about different languages

Practicing to work with others

Learning to listen other people’s opinions

Encouraging positive attitude towards working life

Learning to use foreign language in work context

Practicing to plan and execute studies, make observations and measurements

Using technology as a part of explorative process

Using technology for interaction and collaboration (also internationally)

Developing problem solving skills

Using technology for interaction and collaboration

Practicing logical reasoning, algorithms and programming through making

Using technological resources for finding and applying information

Understanding technological system operations through making

Using technology resources for problem solving

Building common knowledge of technological solutions and their meaning in everyday life

Learning to plan and design own written content and textual representations

Practicing time management

Connecting subjects learned at school to skills needed at working life

Practicing persistent working

Learning to notice causal connections

Understanding and practicing safe and responsible uses of technology

Understanding and interpreting of matrices and diagrams

Learning to plan and organize work processes

Practicing to improvise

Practicing creative thinking

Creating requirements for creative thinking

Learning to find the joy of learning and new challenges

Data Science Course