Data Classes Oslo

This 1-week data analytics course provides a deep-dive into using Python for data analysis. Students will get comfortable with the basics of Python programming and start working with critical data analysis libraries like Numpy, Pandas, and Matplotlib to perform data analysis and create data visualizations. This 35 hour intensive is meant to quickly get beginners in Python up to speed on performing data analysis and visualization in Python.

This machine learning course is meant for learners looking to master the concepts needed for machine learning. Students will learn how machine learning can be used for decision-making, the different machine learning techniques available, and how to tackle machine learning projects. The course covers various techniques in machine learning, including regression, classification, k-means, neural networks, and more.

This Python for Finance Immersive course gives students a deep understanding of the use of Python for data extraction, analysis, and visualization. Throughout the 30-hour program, the students will work with NumPy, Pandas, Matplotlib and other scientific libraries as they learn how to pull and process data. Students will graduate with hands-on experience working with Jupyter Notebook, having completed small projects using Python to analyze data. This program is open to beginners, with no requirement for experience in advanced mathematics or coding.

In this part-time bootcamp, students learn necessary programming skills to manipulate databases and perform various levels of analysis on the data. This program prepares students for entry-level data science and Python engineering positions.

This data analytics course is a 40-hour in-person or online program for individuals with no prior experience with data analytics. Utilizing a hands-on approach, students learn by doing in order to build vital and dynamic skills that are applicable in a wide range of industries. The course curriculum centers around the core processes all data analysts must know, including data collection, cleaning, analyzing, and data visualization best practices. Students also come away with hands-on experience using some of the most common tools in the industry, including Microsoft Excel, SQL, and Tableau. By the end of the course, students will have a better understanding of how to gain insights from data and feel more prepared for a possible career in data analytics.