Basic Information

Duration / Date(s)
maximum one year study time
Extent
30 ECTS
Next application period:
  • nonstop throughout the year
Price / Tuition fee
2.950 € + VAT, if applicable

Upskill your competence as a data analytics professional! Study 30 ECTS in English online and obtain a Diploma in Data Analytics on the courses completed.

In this 30 credits diploma, you will gain profound knowledge and understanding of data analytics and learn to use and apply this knowledge in practice using Python programming language. In addition, you will learn how to use common tools such as Microsoft Power BI and Tableau.

The fully online studies allow you to become a student regardless of where you live. This Diploma corresponds to level 7 on the EQF (European Qualifications Framework) of reference.

30 credits study module contents

30 credits study module contains following topics:
(1 ECTS credit = 27 hours of workload to a participant)

Introduction to Python for Data Science 4 ECTS

This course is designed to introduce the students to the basics of the Python programming environment, including fundamental Python programming techniques used in data science. The course aims to teach students various data visualization, manipulation, and cleaning techniques using the popular Python data science libraries by exploring different types of data. This course provides a unique opportunity for the student to get hands-on experience with popular Python libraries such as NumPy, Pandas, and Matplotlib. By the end of this course, the student will understand the data science workflow, the basics of Python programming, and learns how to take tabular data, as well as clean, manipulate and visualize it, and run basic analyses.

Exploratory Data Analysis with Python, 6 ECTS

Exploratory Data Analysis (EDA) is a combination of multiple techniques that extract valuable insights and meaningful information from the data. The main aim of EDA is to investigate datasets to reveal the underlying structures, challenges, and opportunities of data without attempting to apply any machine learning model. This course will introduce the student to the practical knowledge and the main pillars of EDA, including data exploration, data preparation, data visualization, data relationships, and data clustering using Python programming language. Apart from the intuitions, the student will get familiar with how EDA steps are performed by various Python libraries such as NumPy, Pandas, and Matplotlib.

SQL for Data Science, 4 ECTS

SQL is the standard query language to work and deal with relational databases. Relational databases manage data in tables (or relations), making them efficient and flexible to store and access structured information. Entity-Relationship (ER) modeling helps us collect and visualize the requirements of the database to create an optimized database. This course will introduce students to SQL, its capabilities and functionalities, create an ER diagram, and use it to create a relational database. Besides, the students will get introduced to the MySQL database management system to manage, control, and query the data stored in the relational databases using SQL language. Apart from the intuitions, the student will get familiar with lots of SQL commands required for an aspiring Data Scientist and Data Analyst.

Microsoft Power BI or Tableau, 6 ECTS

This course helps the student learn the fundamentals of data visualization and practice communicating with data. The student learns how to build visualizations, apply design principles, organize data, design dashboards, and effective storytelling with data to empower more meaningful business decisions and effective business intelligence solutions. 

Tableau is a visual analytics empowering people and organizations to make the most of their data using meaningful visualizations.

Fundamentals of AI, 2 ECTS

AI has numerous applications in today's world. AI has enabled us to build machines that can think rationally and sometimes as a human. AI can be seen everywhere, from self-driving cars to a simple chatbot or even YouTube video recommender. This course is a gentle introduction to AI's basic concepts and methodologies from both theoretical and practical perspectives. The course covers essential intuitions behind different AI methods (e.g., machine learning and deep learning) as well as the business-side topics, like the implications of AI and its effect on today's industry. The student will get familiar with modern AI aspects as his/her very first steps on the journey to AI. The student will learn to think outside the box using AI and present AI-based solutions using appropriate methods discussed in the course.

Fundamentals of Machine Learning, 2 ECTS

Machine Learning has found its way into many of the services we use daily, e.g., Google Search, YouTube, Netflix, and Spotify. It is an application of AI that deals with the challenge of computers performing tasks without being explicitly programmed. This course will introduce the student to the basic principles and concepts of machine learning. Apart from the intuitions, the student will get familiar with the most popular machine learning algorithms, their applications, and their intuitions. After passing this course, the student will be prepared to enter the fantastic world of machine learning towards amazing job positions in the industry.

Machine Learning with Python, 6 ECTS

This course dives into practical machine learning using an approachable and well-known programming language, Python. It provides a unique opportunity for the student to get hands-on experience with popular Python libraries for machine learning such as Numpy, Matplotlib, Pandas, Seaborn, and Scikit-learn. After passing this course, the student will be able to implement his/her own machine learning models (supervised and unsupervised) from scratch, get them to work, and evaluate their performance. Furthermore, common practices and tricks used by data scientists and machine learning experts are also described throughout the course to prepare the student for future job opportunities.

Requirements

An appropriate foreign higher education degree in Bachelor of Engineering (Information Technology, or near study field).

Career prospects

The Diploma enhances your professional growth in Data Analytics. If you continue from Diploma to Master's Degree in Information Technology, your Diploma studies will be recognized as part of the degree studies, and only thing left is the Master’s thesis. Two years relevant work experience (in addition to 240 ECTS degree in Bachelor of Engineering, Information Technology or near study field) will be required for Master’s degree studies. The Master’s degree qualifies for team and project leadership in an IT organization and advancement towards management positions in private or public sector, e.g., IT manager, team leader, senior software designer.

Start dates

Nonstop throughout the year.

Registration

Link to the registration will be provided upon request

Contacts

Manager of Education Export Aija Ahokas
aija.ahokas [at] metropolia.fi