The aim of this training is to support the professional development of specialists working in, or entering, the field of data analytics by equipping them with the skills needed to use data in decision-making and in the development of business processes and services. The course enables participants to strengthen their ability to collect, analyse and clearly present data to support data-driven decision-making within organisations.
The training is based on the Google Data Analytics Professional Certificate available on the Coursera e-learning platform, which comprises nine English-language online courses. Within this curriculum, eight courses are completed. Further information is available here: https://www.coursera.org/professional-certificates/google-data-analytics.
ARISA’s e-learning environment is used for the data protection module. Learners are also provided with 26 academic hours of group sessions with mentors. All learning is delivered online, including meetings with mentors.
A participant who has completed the training:
- understands the role of a data analyst in supporting business decisions and can identify the data required for effective decision-making;
- acquires and prepares data, assesses data quality, and carries out statistical and exploratory analyses;
- creates summaries and reports that support business functions and presents analytical results clearly and in a structured manner;
- supports decision-making processes by drawing conclusions from existing data and forecasting future scenarios;
- collaborates with a range of stakeholders within the organisation, understands business needs, and interprets data within the appropriate context;
- uses data analysis tools and applies them appropriately according to the task;
- follows ethical and data protection principles in data analysis and assesses security risks.
Target audience and prior knowledge (Prerequisites):
The course is intended for individuals who wish to begin or advance a career in data analytics and contribute to data-driven decision-making within organisations. It is suitable for those who:
- have good computer skills, including the ability to manage files, use a variety of software, and navigate digital environments;
- have a strong interest in data analytics and, preferably, some prior experience in the field;
- possess at least a B2 level of English;
- are able to commit to a four-month course.
Teaching aids: Participants need a computer (Windows or macOS) with web camera and microphone; permission to install and configure applications, as well as an up-to-date web browser (Google Chrome recommended).
Program:
02.12.26 17:00-18:30
1. Webinar with the mentor – Introduction of the program (2 academic hours)
Modul 1 – Data Collection, Preparation & Analysis Basics
E-learning and independent study – 36 academic hours
16.12.26 17:00-18:30
2. Webinar with the mentor – Introduction to data (2 academic hours)
E-learning and independent study – 25 academic hours
06.01.27 17:00-18:30
3. Webinar with the mentor – Data Preparation and Protection (2 academic hours)
E-learning and independent study – 21,5 academic hours
13.01.27 17:00-18:30
4. Webinar with the mentor – Data Quality and Cleaning (2 academic hours)
E-learning and independent study – 33,5 academic hours
27.01.27 17:00-18:30
5. Webinar with the mentor – The Fundamentals of Data Analysis (2 academic hours)
Modul 2 – Applied Data Analysis & Visualization
E-learning and independent study – 24 academic hours
10.02.27 17:00-18:30
6. Webinar with the mentor – Data visualization (2 academic hours)
E-learning and independent study – 36 academic hours
25.02.27 17:00-18:30
7. Webinar with the mentor – Data analysis using Python (2 academic hours)
E-learning and independent study – 15 academic hours
03.03.27 17:00-18:30
8. Webinar with the mentor – Case Study and Using AI for Data Analysis (2 academic hours)
Modul 3 – Graduation Project
Independent study – 40 academic hours
The group selects a research problem to work on and identifies or creates an example dataset to address that problem.
The graduation project should include five components:
– a clear problem statement;
– an analysis plan;
– a description of the data;
– the data analysis itself;
– a presentation of the results.
10.03.27 17:00-18:30
9. Webinar with the mentor – Graduation project preview and consultations (2 academic hours)
17.03.27 17:00-18:30
10. Webinar with the mentor – Graduation project preview and consultations (2 academic hours)
24.03.27 14:00-18:30
11. Webinar with the mentor –Graduation project presentations
Each group has 10 minutes to present their work to the mentor and co-students (6 academic hours)
Learning methods and volume of the course:
The course is conducted in English.
You can participate in the webinars by joining through the online platform Zoom. Between the online webinars, the main communication platform for interacting with the mentor and fellow students is the course’s learning environment Slack.
The course has three modules.
The total volume of the training is 257 academic hours (within period of 16 weeks) including:
Mentor meetings: 26 academic hours
E-learning and independent study: 231 academic hours
e-learning = learning that takes place partially or completely with the help of digital technological means.
Methods used during the course:
- E-course lectures
- E-course video lectures
- Workshops with mentor
- Practical exercises
Evaluation method:
- Completion of all mandatory Coursera e-learning courses.
- Participation in at least 75% of the online webinars and workshops.
- Completion of the final project.
Graduation criteria: Learners who have achieved the learning outcomes and successfully passed the assessment will be awarded a certificate in accordance with the applicable continuing education standard in Estonia.
If a participant fails to meet at least one of the following conditions, they will receive a certificate of participation instead:
- not completing all mandatory Coursera e-learning courses;
- not attending at least 75% of the workshops;
- not completing the graduation project.
The price includes: Access to the e-learning platform and other study materials.
See the full curriculum of the program here.
Curriculum group: 0613 Software and Application Development and Analysis.
Lecturer’s LinkedIn
