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Data Science Courses

Data Science, Data Analysis and Machine Learning are disciplines that create value from large amounts of data using programming languages. The applications are numerous and the demand for knowledge about these techniques is enormous. SpiralTrain provides classroom and advanced Data Science training in Python, R and Java. Visit our LinkedIn, Facebook or Instagram page for an impression of SpiralTrain. Click on the links below for more information about the courses and the schedule.

Data Science Course List

Course Julia Computing
Course Julia Computing
Code: DSC800
First start: 12-12-2022
3 days € 1850
Course Machine Learning with TensorFlow
Course Machine Learning with TensorFlow
Code: DSC750
First start: 19-12-2022
3 days € 1850
Data Analysis course with Power BI
Course Data Analysis with Power BI
Code: DSC400
First start: 15-12-2022
2 days € 1199
Data Analysis Course with Tableau
Course Data Analysis with Tableau
Code: DSC300
First start: 09-01-2023
2 days € 1199
Data Analysis with R
Course Data Analysis with R
Code: DSC200
First start: 12-12-2022
4 days € 2250
Course-Data-Analysis-with-Python
Course Data Analysis with Python
Code: DSC100
First start: 19-12-2022
4 days € 2250
Course Machine Learning with Python
Course Machine Learning with Python
Code: DSC700
First start: 13-12-2022
4 days € 2450
Machine-Learning-with-R
Course Machine Learning with R
Code: DSC780
First start: 12-12-2022
4 days € 2450
Course Hadoop for Big Data
Course Hadoop for Big Data
Code: DSC900
First start: 25-01-2023
3 days € 1950
Course-Data-Analysis-with-PySpark
Course PySpark for Big Data
Code: DSC950
First start: 13-12-2022
4 days € 2450

Big Data and Data Analysis

Data Science is a field that has gained momentum in recent years. It is about finding complex patterns in large data sets that you cannot find with traditional analysis tools. You build a model to detect, analyze and validate those patterns. The Data Science process consists of a cycle of steps. Data is generated and stored in databases at any time of the day. In order to obtain the correct model, you must determine which data is relevant. Rearranging, filtering and transforming data is done using a programming language.

Python and R

With languages ​​such as Python and R, the data is transformed into models that can be used when making predictions. After the data has been prepared and transformed, the Data Analyst can start working with statistics. By making causal connections, a Data Analyst can then make predictions. A commonly used statistical method to measure causal relationships is regression analysis.

Machine Learning

In addition, predictions can also be made using Machine Learning where predictions are made by algorithms that are self-learning. Finally, the data is visualized. This is an important step because connections and dependencies are often only visible when they are visualized.