Study Plan
This course enables students to know and understand the fundamental concepts of Data Science. Participants learn the steps associated with the execution and development of a data science project and the importance of data collection, and the impact this has on decision making. Additionally, they will understand the three essential steps in data management: collection, analysis and interpretation.
Data quality and life cycle.
- Definition of data science
- Data quality
Throughout the course, students focus on learning and understanding the fundamental
concepts of data science, one of the most important disciplines in the world we live in
today.
The course also examines the steps associated with the design and execution of a data
science project, role binding, general applications and, ultimately, the subject of data
quality and its derivatives.
Data Preparation and Pre-processing
- Objectives and initial reflection
- What is data collection?
- Data science workflow process
- Data Management
- Data governance
- Data wrangling
- Data wrangling stages
- Data wrangling in Python
- Data cleaning
Data is the essential ingredient in the work of a data scientist. Without data, there is no analysis, there are no models... Without data there is no vision of anything. That’s why it is essential to start building your knowledge of this discipline starting from here. You are going to learn about the importance of data collection so you understand that the impact of the decisions made during this process can be decisive in the rest of the tasks that a data scientist carries out every day. You will also examine several use cases to see the initial scenarios that will open the door to the world of data analytics and modeling.
Innovation Catalyst
