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Who should attend
The Data Science Bootcamp is for professionals seeking to learn how to use data across industries more efficiently. It’s also for those wanting to quickly gain essential data science skills to kick-start their data science careers. Finally, it’s ideal for those with a Bachelor’s Degree and typically between zero and five years of work experience.
About the course
The Data Science Bootcamp will give you the tools and resources to enhance your skills and accelerate your career through the study of data science.
By 2020, there will be an estimated 1 million new digital and tech jobs in Europe. Traditional higher education is unlikely to produce enough graduates to fill even a third of them. Upon completing the Bootcamp, you’ll have learned to acquire, clean, structure, store, manipulate, analyze, and visualize data from diverse sources to answer complex business questions. You’ll know how to find patterns, use machine learning and other analytical tools to put into practice across the business world.
What will you learn in this program?
- Programming: R, Python
- Data visualization: ggplot2, seaborn, matplotlib
- Statistics: inferential statistics, probability distributions, regression analysis
- Machine Learning: classification, clustering, and recommendation algorithms
- Communication skills: these are essential to adequately explain and visualize all that was learned before
- Data Labs: A mix of teaching, mentoring, and working on real data sets
- A Capstone Project to present for the Demo Day at the end of the Bootcamp
R and python programming
This module is designed to give you an in-depth understanding of both the R & Python programming languages from their syntaxes to coding tips and techniques for script optimization. You will understand the different programming structures in both languages as this module provides the core coding foundation for you to excel in the rest of the Bootcamp. You will learn to code from scratch in intensive practice-only sessions using exercises and individual programming assignments.
Math and statistics for data science
This module provides the mathematical foundation for the machine learning module as it covers the mathematical and statistical concepts that support data science and machine learning projects. The sessions in this module provide the theoretical knowledge in quantitative methods and statistical models that will complement their R & Python machine learning workshops. You will learn the statistical models to extract insights from data and the statistical tests to support your findings.
Machine learning in R and python
This module is designed to enhance an in-depth understanding of the practical knowledge in implementing the quantitative and statistical models that are part of the machine learning landscape in R and Python. The machine learning classes will provide you with the hands-on training based on analyzing multiple data sets to take your data science and machine learning output to the next level.
Data acquisition and visualization
This module will enable you to complement your data analysis skills with the ability to acquire the data from different sources (from text files to Hadoop files and SQL databases) using both R and Python scripts. Additionally, you will learn how to produce powerful and compelling visualizations using both R and Python packages. Picking the right graph can be the difference between being an agent of change or an irrelevant analysis. You’ll learn how to represent data to highlight your work.
Communication and data storytelling
In today’s competitive world, having the technical proficiency to achieve success in the data science industry is not enough to mobilize an organization towards change. Crafting a strong narrative and effectively communicating your analysis is critical to get there. This module aims to complement your technical knowledge with the storytelling and communication skills required to maximize the impact of your data analysis.
Iván Martín is the Chief Technology Officer of BINFLUENCER, a data driven company leveraging the power of machine learning techniques to detect the most influential people in any sized market globally. He is an Ex-McKinsey Data Scientist, software engineer specialized in Artificial Intelligence a...
Experience CEO @CANALYTICALS • Co-Founder We develop predictive algorithms through Big Data and Machine Learning May '16 - Present (3 years 3 months) CTO @McKinsey & Company Jan '14 - Jan '17 (3 years) Amazing Things Pablo's Made PhD in Mathematics and Statistics, with expertise in Big ...
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