Eskwelabs Data Science Bootcamp

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Eskwelabs Data Science Bootcamp

Eskwelabs Data Science Bootcamp

Data Science Bootcamp
Eskwelabs is an online data upskilling school

Eskwelabs is an online data upskilling school in the Philippines. Their mission is to drive social mobility in the future of work through data skills education and to do that by bringing together the best aspects of online and community-based learning. Their courses offer in-demand digital and data skills, preparation for jobs at different skill levels, and a peer community to promote lifelong learning.

Eskwelabs also provides a free learning course—called “Aral-Aral for Data Science”—for aspiring Data Science Fellowship students. This free learning course is open to all learners who want to learn the core skills of Python programming and statistics to prepare for the Bootcamp.

Age groups 
Professional education
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.

The Eskwelabs Data Science Bootcamp course proceeds logically from the basics to more complicated content. Aral Aral Course takes into account the different baseline knowledge of students and prepares them for the Bootcamp. The videos in the Aral Aral self-study course are clear and inspiring. In the Bootcamp, studying is very intensive and require active participation since the students are required to return some work after every lesson and receive feedback.
Eskwelabs Bootcamp delivers plenty of new knowledge through lessons and requires rehearsing and utilizing learned in open-ended problem-solving in tasks. The close link to work-life is a great way to show the importance of the topic and encourage to use the knowledge to tackle new challenges.
The Bootcamp follows linear, predetermined user progression, where progress is be scheduled accurately with learning goals clearly in the focus. The syllabus is comprehensive and well thought off. The Aral Aral course makes sure the students have a required pre-knowledge level. Learning progress is mainly individual. As the Bootcamp provides a broad base knowledge in the topic, it gives the learner the abilities to progress further and deepened their knowledge in various areas after the course.
Social interaction through digital channels is an integral part of the learning experience. The course does not allow passing through the content without interacting with other users - there are many tasks that require group work and also ways to contact other students when needed. This is a very good way for preparing for actual work situations and ways how to make innovations in this field. The course also emphasizes some collaborative working skills.

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

The Bootcamp gives a good base knowledge about the learned topic, and the Aral Aral course and entrance test make sure everyone has the required starting level of knowledge.
The social interaction and group work are really great aspects of the Bootcamp. The projects that the students work on after each sprint is a good way to practice everything that they have learned.
Bootcamp provides an intense, engaging learning experience that has well-thought goals and schedule.
The course has great support channels and working mentoring model.

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

Understand basic logic of how programming language works, and be able to produce basic Python code including data types, numpy, and Pandas
Understand basic statistical concepts like probability, descriptive statistics, and inferential statistics
Understanding different data types in Python
Uderstanding the Basic algorithms in Python
Data Wrangling with Pandas
Using advanced Excel formulas such as IF-formulas and Text processing
Core roles and responsibilities of a business analyst, and the tools they have at their disposal.
How to create a narrative from your data
Building a slide deck
Public speaking
Data ethics; Privacy, Anonymity, Public externalities, Public externalities, Algorithmic fairness
Social impact of data science practices
Deep learning and network theory
Identifying use cases for machine learning and conceptualizing them
Creating machine learning models, deploying and maintaining them
Understanding the centric descriptive statistics: Measures of central tendency and dispersion, Statistical distributions and shapes, Quantiles, Percentiles, Skewness, Kurtosis Correlation and Covariance
Being able to do Differential Calculus required for data analysis.
Understanding the selection of Treatment/Control Groupsm, Sampling Strategies and Sampling Biases
Diagnostic Analytics such as experimental design or A/B Testing
Providing Predictive Analytics
Prescriptive Analytics; Developing actionable recommendations from data in different formats
Descriptive Analytics of data to deliver insights from it to your audience
Understand the stages of data science and skills of data scientists
Creating beautiful, intuitive, and informative graphs in approriate tools
Matching the graph to the message
Surfacing insights from graphs
Understanding supervised and unsupervised machine learning process

Soft skills learning goals

Practicing strategic thinking
Learning to face failures and disappointments
Practicing to recognize and express feelings
Encouraging the growth of positive self-image
Practicing to look things from different perspectives
Practicing to plan and execute studies, make observations and measurements
Practicing to notice causal connections
Learning to build information on top of previously learned
Encouraging to build new information and visions
Learning to combine information to find new innovations
Practicing to notice links between subjects learned
Learning to understand people, surroundings and phenomenons around us
Practicing to work with others
Practicing to argument clearly own opinions and reasonings
Learning to listen other people’s opinions
Learning decision-making, influencing and accountability
Practicing communication through different channels
Learning to understand the meaning of rules, contracts and trust
Practicing to express own thoughts and feelings
Practicing to give, get and reflect feedback
Enabling the growth of positive self-image
Encouraging positive attitude towards working life
Practicing time management
Learning to plan and organize work processes
Practicing decision making
Practicing versatile ways of working
Connecting subjects learned at school to skills needed at working life
Realizing the connection between subjects learned in free time and their impact to skills needed at worklife
Learning to face respectfully people and follow the good manners
Learning to use foreign language in work context
Practicing logical reasoning to understand and interpret information in different forms
Understanding concepts of music and familiarizing with different notations
Using technology as a part of explorative and creative process
Understanding and interpreting of matrices and diagrams
Learning to acquire, modify and produce information in different forms
Practising to understand visual concepts and shapes and observe their qualities
Practicing to use information independently and interactively
Practicing to find, evaluate and share information
Familiarizing with the influences of media and understanding its affordances
Building common knowledge of technological solutions and their meaning in everyday life
Using technology resources for problem solving
Understanding technological system operations through making
Using technological resources for finding and applying information
Practicing logical reasoning, algorithms and programming through making
Using technology for interaction and collaboration
Using technology as a part of explorative process
Practicing categorization and classification
Learning to notice causal connections
Practicing persistent working
Practicing to find ways of working that are best for oneself
Practicing to take responsibility of one's own learning
Practicing to set one's own learning goals
Practicing to evaluate one's own learning
Learning to find the joy of learning and new challenges
Creating requirements for creative thinking
Practicing creative thinking
Developing problem solving skills
Learning to recognise and evaluate arguments and their reasonings
Practicing to create questions and make justifiable arguments based on observations
Understanding and practicing safe and responsible uses of technology

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