Advanced Data Science Techniques and Tools
Discover the world of data science with a focus on intelligent autonomous systems, machine learning, and data analysis. Explore the applications of Python, Rapidminer, and other tools in this field. Learn about the skills required, such as critical thinking and attention to detail, for a successful career in data analysis. Dive into the fundamentals of data science and data quality assurance, along with the various types of data analysts. Uncover the importance of clear communication and math skills in data analysis.
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Presentation Transcript
(.) (IAS) (MISL) Intelligent Autonomous Systems (IAS) group, Multi-agent Intelligent Simulation Laboratory (MISL) E-mail: olarik.s@msu.ac.th
Links http://olarik.it.msu.ac.th https://github.com/mrolarik https://www.python.org/downloads https://orange.biolab.si/
Fundamentals of Machine Learning and Analyzing Data with Python A Practical Approach :
field that uses scientific methods, Data Science processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data https://www.stoodnt.com/blog/top-universities-for-ms-in-data-science-in-usa/
data assurance data quality Types of data analyst finance higher education marketing sales
Data analysts are often called to present their findings, or translate the data into an understandable document. You will need to write and speak clearly, easily communicating complex ideas. Critical Thinking: Data analysts must look at the numbers, trends, and data and JOB SKILLS AND REQUIREMENTS come to new conclusions based on the findings. Attention to Detail: Data is precise. Data analysts have to make sure they are vigilant in their analysis to come to correct conclusions. Math Skills: Data analysts need math skills to estimate numerical data.
Rapidminer Orange Tools Programming Python Scikit-learn Tensorflow Keras Pytorch Matlab
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> Python >>> Import sklearn Error numpy
Installing numpy > pip install numpy
Install scipy > pip install scipy
> Python >>> import sklearn
