Data Science Course

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Data Science Course

Data Science Course

Digital Skola
Data Science
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
Bahasa Indonesia
Desktop Windows
Desktop Mac
Offline play 
Internet required
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.

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).
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.
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.
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

The Finnish Educational Quality Certificate

Our Quality Evaluation Method is an academically sound approach to evaluating a product’s pedagogical design from the viewpoint of educational psychology.

The method has been developed with university researchers and all evaluators are carefully selected Finnish teachers with a master's degree in education.

More about the evaluation