Data Science and Machine Learning Bootcamp

Be ready to enter tech’s highest-paying fields in just 16 weeks!

Learn Python, SQL, Looker (Google Data Studio), Streamlit, and Statistics and become a professional Data Analyst, ready to contribute to any data-driven team at any company. Create predictive models using Python, Pandas, Numpy, and other powerful libraries. Explore algorithms like Close Neighbors, Decision Trees, and more. Work with supervised and unsupervised learning models. Practice solving real-life cases, and learn to tackle the type of challenges you’ll encounter in your career. Master the use of industry-standard tools. Strategize and develop your own A.I. projects, following the complete lifecycle from development to deployment and maintenance, ensuring privacy and security.

Duration: 16 Weeks
Classes: 3 classes weekly
Class size: 12 people max
Certificate: Our certificates are recognized by multiple organizations.
Requisites: No previous experience needed
Language: You can decide between English and Spanish to consume the syllabus and content. Live classes are taught in the language of your campus.

SYLLABUS: Start with Python, Data Science, Machine Learning, Deep Learning, and the maintenance of a production environment in A.I.

INTRODUCTION TO PYTHON

• Starting from a Prework (2 weeks before), get introduced into the world of coding and learn Python and Algorithms' basics; This module will help you prepare to code and develop the different A.I. and Machine Learning model training and evaluations for the next step.

• Projects: Collect, store and measure with Numpy, Pandas, Mstplotlib, etc.

Fundamentals: STATISTICS AND LINEAR ALGEBRA WITH PYTHON

• Use python to write code that reviews and applies the most used Linear Algebra concepts, Matrix and Vectors, F(x), and the relation functions have between variables. We will also be coding the most used concepts in probabilities understanding core trends, standard deviation, mean median and mode. Finally, we will calculate the probability of an event happening: This is a very important skill used to build prediction models that we will be using further along.
• Projects: We will provide you with a list of real life problems and solutions for the different concepts, you will use python to solve them.

COLLECT AND STORE DATA

• Probably longest step in most professional projects is collecting and cleaning the data. In this module you use Python and connect to a SQL Database, learn the SQL syntax to query and manipulate that date, upload and download static files (CSV, JSON, etc.). Learn how to connect with API's or scrape websites that don't have available API's. Store the data in an organized way.
• Projects: Loading structured and unstructured data from different sources (e.g. SQL, Web Scraping, text files) and connect with the Twitter API.

MANIPULATE DATA WITH PYTHON

• Once you data has been stored, its necessary to understand it. We will start with simple visualization techniques for better understanding of some samples and developing the different strategies to clean and optimize your dataset: Feature Engineering, Outliers, Missing Data, Feature encoding, Feature Scaling. Learn how to develop reports using Looker (Google DataStudio) with advanced graphs, filters and other dashboard elements and KPI's, one of the most popular data warehousing tools in the market.
• Projects: Explore the Airbnb dataset and publish a report using Google Cloud and Looker Studio.

PRACTICAL MACHINE LEARNING

• One by one we will be reviewing the most used algorithms and predictive models: Supervised and not Supervised. Every model will be used to fix a problem using Python: Lineal Regresion, Logistic Regresion, Decision Trees, Random Forest, Bayes, Support Vector Machine, NLP, KNN, Time Series, NN and others.
• Projects: Predict medical insurance costs, classify patients based on diabetes. Sentiment Analysis on Google Play Store reviews. Email Spam Detector. Group houses based on geolocation or average income. Classify images and more.

A.I. IN PRODUCTION

• One of the most important aspects of developing Artificial Intelligence solutions is understanding how to prepare, deploy, and manage the solution in a Production Environment. We walk you through developing an AI Strategy, Machine Learning Lifecycle of development and deployment, Machine Learning Operations (MLOps), as well as Privacy, Security, and Ethics. Your final project will be to deploy your machine learning model into production.
• Projects: Building data architectures, deploying AI models into production both on-premiss and on cloud, monitoring and maintenance of the models.

CHOOSE A PROBLEM AND FIX IT WITH MACHINE LEARNING

• Work in a team to choose a real life problem, start collecting the data needed to predict, make an exploratory analysis using several ways of visualizing data in order to understand it. Clean your dataset and optimized for your prediction needs, choose two models and start predicting. Evaluate how effective your models were by using the different techniques we saw in class. Deploy in production using Streamlit o Looker (Google DataStudio).
• Projects: Building data architectures, deploying AI models into production both on-premiss and on cloud, monitoring and maintenance of the models

LIFELONG SUPPORT

• Keep practicing with the academy's help - we will be coaching you for life. We will help you apply for jobs and continue to support you once you find a job in A.I.
• Projects: Get paid to keep learning and raise your income gradually as you become a Machine Learning Engineer. Lead a successful career in AI and Machine Learning development, management, and leadership.