DATA SCIENCE FOR BUSINESS
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Bibliography
- Delivery method
- Teaching methods
- Contacts/Info
The student knows about the topics presented in the courses of Intelligent Systems and Data Mining.
A knowledge of at least one programming language is helpful. Also, it is suggested to bring a laptop.
The learning will be measured with an exam consisting on the discussion of a project work.
The aim of this course is to complete the presentation of data science-related topics bent towards business applications. Starting from the topics presented in the Intelligence systems and Data Mining courses, this course will provide methods and techniques geared towards the implementation of projects suitable for production environments.
This course relates the knowledge of theoretical aspects of data science with the most relevant technologies for manipulating, managing and visualizing data.
Students will be taught about data analysis through datasets available online. Throughout the course activities, predictive experiments (machine learning and deep learning) will be shown in order to fulfill the requirements of the use cases.
The learning objectives and expected results of this course are:
To define an architecture that allows for the development of a data science project, with respect to data volume, velocity and availability, along with computing power and implementation and maintainability requirements.
Select adequate methods for solving the proposed problems, with machine learning and deep learning technologies.
Analyze, visualize and meaningfully interpret the obtained results, given the proposed solution methods.
Implement simple projects in order to gain hands-on experience on methods and techniques in data analysis.
1. How a data science project works
1.a. Tools and cloud
1.b. How a project works
1.c. Projects’ examples and use cases
2. Data transformation and load
2.a. Data manipulation
2.b. ETL concepts
2.c. Data quality
3. Data analysis
3.a. Feature selection and class unbalancing
3.b. Data analysis workflow
3.c. How to contextualize and perform effective analyses
4. Machine learning and deep learning
4.a. Classification
4.b. Clustering
4.c. Feed forward networks
4.d. Autoencoders and Word2Vec
5. Presentation
5.a. How to effectively present data
5.b. Data representation with Business Intelligence tools
The course aims to complete the training of students in the field of data science with an approach linked to the corporate world. Developing the concepts learned in the Data Mining and Intelligent Systems courses, the course will have an approach oriented to the realization of projects and solutions applicable in a production context.
The course combines theoretical knowledge of data science with the use of the main technologies for manipulating, organizing and visualizing data.
Participants will be guided in the analysis and contextualization of data within the datasets available online. During the course activities, the construction of predictive experiments (machine learning and deep learning) will be achieved with the aim of responding to certain needs related to the proposed use cases.
In summary, the teaching objectives and the expected learning outcomes are as follows:
- Knowing how to define an architecture that allows the development of a data science project with respect to the requirements of volume, speed, data availability, calculation and analysis capacity and implementation and maintainability.
- Knowing how to select ways of solving problems proposed with technologies related to the world of machine learning and deep learning.
- Analyze, visualize and give meaning to the data obtained with respect to the solutions identified and based on the resolution mode chosen.
- Carry out simple projects to have direct experience with the techniques and methodologies of data analysis.
Prerequisites
Basic contents of the Intelligent Systems course delivered in the first year of the master's degree course.
Basic contents of the Data Mining course delivered in the first year of the master's degree course.
The course is recommended for anyone who has knowledge of at least one programming or scripting language.
It is recommended that you get a laptop (Windows, Mac or Linux).
Contents
1. How a Data Science project works and what happens in the world of work
a. Tools, Cloud
b. How an architecture and a project work
c. Project examples and use cases
2. Data transformation and load
a. How to manipulate the data
b. Concepts of ETL
c. Data quality
3. Data analysis
a. Feature selection and class unbalancing
b. Data analysis flow
c. Contextualize and create effective analyzes
4. Machine learning and deep learning
a. Classification
b. Clusterization
c. Feed Forward Networks
d. Convolutional Neural Network
e. Autoencoders and Word2Vec
5. Presentation
a. How to effectively represent data
b. Data representation with BI tools
No text or book is required for this exam.
Here some suggestion for those who wants to deepen the topics:
- Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook/
- Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow (quello anno scorso): https://www.amazon.it/Hands-Machine-Learning-Scikit-learn-Tensorflow-dp-...
The course will include frontal lessons to introduce the theoretical concepts and hands-on sessions for practices and exercise.
None
Professors
Borrowers
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Degree course in: COMPUTER SCIENCE