Data Scientist

Learn all processes and best practices of software engineering, making and evaluating diagrams for defining and planning software, how to design and develop software following good practices and quality development standards.

The students will learn the best of management for more fluid completion of activities. They will have access to business concepts, Trello, and much more.

Meet the basics of Data Science. Data analysis using proper and practical methodology for a better understanding of the module contents.

Understand the programming language used to query, add, manage, and delete your information, applications, and services in a database.

Learn how to rise in job market with Introduction to Python. See all about Environment Setup, Data Cleaning and Handling, Charts, Visualizations and Dashboard.

Learn the basics of Github, how and where to use it. Learn how to use this platform that allows convenient hosting of source code and files in the cloud.

Area of statistics that applies techniques to describe and summarize a large set of data.

Learn the calculation of probabilities with unique and exclusive thematic of our course.

Statistical inference uses the statistical method on samples data and draws conclusions about the population of interest, describing it or testing hypotheses.

In supervised Machine Learning on our course, students will learn all about the input data, the dataset, and how it is presented.

Learn the set of algorithms related to Machine Learning. With its applications being more and more present in our daily lives.

Also known as interdependency or exploratory models, these can be used for much of human knowledge, where the analyst or data scientist can search databases.

Deploy is the part that prepares the model to be applied on a daily life. It is the time of greatest adaptation for users to make use of this algorithm.

Time Series are sequences of points ordered in chronological order. Traditionally, the time series has equidistant data in time.

These technologies are clustered into seven practical groups of text mining. Analysis and processing of semi structured or unstructured text.

Collect, organize, and manage a broad range of data using tools like Databricks, Cloudera, and Apache Spark. Make your apps and data available in the cloud with lower costs, easy and immediate scalability.

Learn how to work within the General Data Protection Act. Regulations, applications that ensure the security of your data.

Learn and use the principles of Graph Theory and its applications in Social Network Analysis.

Developing a final project. Based on the content taught during the course, you will develop a project that will be used by companies in the technology industry.