Raja’s delivery is impressive, to say the least, and it makes the long days seem short. This is the best training I’ve received in my professional career. Read more “Mitchell Browning”
Application Developer at TransCanada
…Most important, we discussed several examples of PRACTICAL applications, and I have several great ideas of how to apply these learnings at my company. I feel confident that I can put some of these concepts to use right away. Read more “Michelle (Garrett) Scarbrough”
Michelle (Garrett) Scarbrough
BCA Core Estimating Data Scientist at Boeing
I thought 10 hour days would go slow, but they actually went fast (and am glad they set the tone on day one that we would be there the entire time). Read more “Mike Dwyer”
Sr SQL Developer at Société Générale
I have never learned so much practical knowledge in a week. I can go back to work and begin adding value with these new skills immediately. Read more “Matt Digel”
Product Manager - Analytic Capabilities & Algorithms at Nike
It was a great 5 day workshop with getting some hands on experience and understanding the roots of data science. It made me work towards how data can be applied to solve real world problems. Read more “Lesha Bhansali”
Program Manager at Microsoft
The instructors made the bootcamp the most enjoyable and informative class I’ve ever taken. Read more “Jirui Qin”
Quant Research at Federal Reserve Bank of New York
This was drastically different than a lot of my education, and it’s exactly why I signed up for the course. Read more “Sofia Auer”
Data Scientist at National Research Council Canada
This is the greatest gift I could’ve given myself to jump forward in a field I’m passionate about.
Business Operations Manager at Cisco
I feel like I am walking out with a solid understanding of data science as well as the resources to go further. The bootcamp transformed me from not knowing where to start to being able to build my own predictive models! Read more “Tiffany Li”
Senior Data Scientist at Criteo
I would highly recommend this course to anyone seeking a very solid foundation in data science. It is intense and fast paced, but worthwhile and enjoyable. Read more “Jenna Butler”
Senior Software Engineer at Microsoft
This kind of information is not available in online tutorial or courses because it needs a deeper engagement to understand the intricacies involved. Read more “Manish Kumar Gupta”
Manish Kumar Gupta
Senior Software Engineer at Microsoft
The bootcamp is very well organized and you leave with plenty of material to continue learning data science on your own. Read more “Eunice Yang”
Sampling. Quantity, quality, and variety of data. Privacy, access control, legal, ethical and security issues in data acquisition.
Beginners in data science often put too much emphasis on machine learning algorithms while ignoring the fact that garbage data will only produce garbage insights. Data quality is one of the most overlooked issues in data science. We discuss challenges and best practices in data acquisition, processing, transformation, cleaning and loading.
Various data visualization and exploration techniques and packages. Interpreting boxplots, histograms, density plots, scatterplots and more. Segmentation and Simpson's paradox.
Through a series of hands-on exercises and a lot of interactive discussions, we will learn how to dissect and explore data. We take different datasets and discuss the best way to explore and visualize data. We form hypothesis and discuss the validity of our hypothesis by using various data exploration and visualization techniques.
Modeling a Real World Predictive Analytics Problem
Face detection. Adversarial machine learning. Spam detection. Translating a real world problem to a machine learning problem
Taking a real world business problem and translating it into a machine learning problem takes a lot of practice. We will take some common applications of predictive analytics around us and discuss the process of turning that into a predictive analytics problem.
Supervised learning is about learning from historical data. We will understand some of the key assumptions in predictive modeling. We will discuss in what scenarios the distribution of future data will not remain the same as the historical data.
Decision Tree Classification
Decision tree learning. Impurity measures: Entropy and Gini index. Varying decision tree complexity by varying model parameters.
We will start learning building predictive models by understanding decision tree classification in depth. We will start with an understanding of how we split nodes in a decision tree, impurity measures like entropy and Gini index. We will also understand the idea of varying the complexity of a decision tree by change decision tree parameters such as maximum depth, number of observations on the leaf node, complexity parameter etc.
Interactive discussion. R. Python
Building an evaluating a classification model
Train/test split. Training, prediction and evaluation. Varying model hyperparameters such as maximum depth, number of observations on leaf node, minimum number of observations for splitting etc.
We will build a classification model using decision tree learning. We will learn how to create train/test datasets, train the model, evaluate the model and vary model hyperparameters.
Model Evaluation and Selection
Evaluation Metrics for Classification Models
Confusion matrix, false/true positives and false/true negatives. Accuracy, pecision, recall, F1-score. ROC curve and area under the curve.
Once we have understood how to build a predictive model, we will discuss the importance of defining the correct evaluation metrics. We will discuss real-world anecdotes to discuss under what circumstances one metric might be a better metric than the other.
Model Evaluation and Selection
Generalization and Overfitting
Generalization. Overfitting. Bias and variance. Repeatability. Bootstrap sampling
Building a model that generalizes well requires a solid understanding of the fundamentals. We will understand what do we mean by generalization and overfitting. We will also discuss the ideas of bias and variance and how the complexity of a model can impact the bias and variance of our model.
Model Evaluation and Selection
Tuning of Model Hyperparameters
Model complexity. Bias and variance. K-fold cross validation. Leave one out cross validation. Time series cross validation.
How do we build a model that generalizes well and is not overfit? The answer is by adjusting the complexity of machine learning model to the right level. This process known as hyperparameter tuning is one of the most important skills you will learn at the bootcamp. Using the decision tree learning parameters as an example we will observe how a model is impacted by creating a deeper or a shallow tree. We will do practical hyperparameter tuning exercises using cross validation.
Azure ML, R, Python
Bagging, Boosting and Random Forest
Binomial Distribution, The importance of randomization and generalization in modeling, Sampling with replacement, Sampling without replacement, Bootstrapped sampling, Bagging, Boosting, Random Forests, AdaBoost
After building a predictive model and understanding the pitfalls of wrong choice of evaluation metrics, we move to somewhat advanced learning techniques. We discuss the importance of ensemble techniques in machine learning and how they help us get machine learning models that are more generalized. The module goes in-depth into sampling with/without replacing, bootstrapped sampling, bagging, random forest and boosting. We discuss how ensemble methods utilize many different random subsets of data and combines the strength of many models to learn from many varied examples. The hands-on exercise will exercise your thinking in terms of how to choose an appropriate number of trees and the sampling techniques appropriate for the given problem.
Structured versus semi-structured versus unstructured data, Structuring raw text, Tokenization, Stemming and lemmatization, Stop word removal, Treating punctuation, casing, and numbers in text, Creating a terms dictionary, Drawbacks of simple word frequency counts, Term frequency – inverse document frequency, Document similarity measure
Not always will you work with fully structured data. Many applications of data science require analysis of unstructured data such as text. We will teach you the basics of converting text into structured data, and how to model documents to find their similarities and recommend similar documents. We cover the important steps in pre-processing text in order to create textual features and prepare text for modeling or analysis. This includes stemming and lemmatization, treating punctuation and other textual components, stop word removal, and more. We also demonstrate how to model documents using term frequency-inverse document frequency and finding similar documents. The hands-on exercise looks at an example of analyzing text and introduces additional problems to solve in pre-processing text/documents.
Real-world problems that unsupervised learning algorithms solve, The K-means clustering algorithm, Euclidean distance measure, Defining k, The Elbow Method, Strengths and limitations of k-means clustering
As one of the oldest branches of machine learning, unsupervised learning at its core is about revealing the hidden structure of any dataset. Not always are you going to be working with labeled data or records tagged with a label outcome. For example, collecting data on customer’s purchasing habits does not come with a label outcome of ‘high value customer’ or ‘low value customer’; that label needs to be created. We teach the underpinnings of the k-means clustering algorithm to solve this problem of finding the common attributes that separate out one cluster group from another. We can then use this to categorize our data based on clusters, or customers of similar attributes such as high value customers who all have similar spending habits. You will also learn how to approach an unsupervised learning challenge through a hands-on exercise and how to define your cluster groups.
Collaborative versus content recommenders, Data structure of collaborative versus content, Text recommenders, Search and recommenders, Pearson’s correlation, Cosine similarity, N nearest neighbors, Mean absolute error for recommenders, Root mean square error for recommenders, Discounted cumulative gain
In many ways, recommenders are the first and greatest problem of modern machine learning, and they are the engines which drive modern commerce. You will learn about the two types of recommenders, collaborative and content, and how to blend them to get the best of both worlds. We different types of recommenders such as text recommendation, search ranking. You’ll also learn the similarity measure for ranking recommended items and the prediction methods. We teach you how to evaluate of recommender and the metrics appropriate for this. You will then build and deploy a recommendation engine in Azure Machine Learning.
A/B and multivariate tests, A/B metrics, Hypothesis testing in A/B tests, Type one and two errors, Confidence intervals, Conducting a t-test, Pitfalls in online experimentation
Online experimentation is perhaps the most misused of data science techniques. There are many errors that can creep into an experiment and test if not set up and conducted properly. We will walk through the best practices for designing and evaluating A/B and multi-variate tests. We discuss how to choose the appropriate metrics, how to detect and avoid errors, and how to properly interpret test results. Learn diverse examples of A/B and multivariate tests, hypothesis testing in A/B tests, type one and two errors, confidence intervals, t-tests, and more. Take part in an interactive game in class to illustrate different A/B and multivariate tests.
Linear regression and basic math notation, Cost function, Gradient descent, Batch gradient descent, Stochastic gradient descent, Regularizing regression models, Mean absolute error, Root mean square error
Regression and classification are the two sides of the supervised learning coin. You will learn how to adapt the techniques you have learned in predictive analytics and classification to the challenge of predicting numbers such as prices, revenues, click rates, and so on. We give you an overview of how regression models learn, teach you how to evaluate them, and demonstrate the use of regularization to prevent overfitting. Learn regression methods such as gradient descent, batch gradient descent, stochastic gradient descent, and the differences between these. Learn about the cost function in gradient descent and how it is used to find the optimal model fit. We end with a hands-on exercise to solidify how regression works and use it to predict house prices.
The first challenge of big data isn’t one of analysis, but rather of volume and velocity. How do you process terabytes of data in a reliable, relatively rapid way? We teach you the basics of MapReduce and Hadoop Distributed File System, the technologies which underly Hadoop, the most popular distributed computing platform. We also introduce you to Hive, Mahout and Spark, the next wave of distributed analysis platforms. Learn how distributed computing works to be able to scale machine learning training on terabytes of data. The hands-on lab will take you through the process step-by-step on setting up a Hadoop cluster to handle processing big data.
Extract, transform, and load pipelines, Data ingestion, Event brokers, Stream storage, Azure Event Hub, Stream Processing, Event processors, Access rights and access policies, Querying streaming data and analysis
Often the data that we are working with is not sitting in a database or files, it is being continuously streamed from a source. Network systems, sensor devices, 24-hour monitoring devices, and the like, are constantly streaming and recording data. Learn how to handle the end-to-end process of handling these data, from extracting the data, to processing it, to filtering out important data and analyzing the data on the fly, near real-time. We take you through building your own end-to-end ETL (extract, transform, load) pipeline in the cloud. You will stream data from a source such as Twitter, or credit card transactions, or a smartphone to an event ingestor. This processes the data and writes it out to cloud storage. You will then be able to read the data into Azure for analysis and processing.
A user-interface into a model makes it easier to see how it would work in the real world, where a new customer enters the systems and data is collected on their age, gender, and so on. We teach you direct and simple processes for setting up real-time prediction endpoints in the cloud, allowing you to access your trained model from anywhere in the world. We walk you through constructing your own endpoints and show a few practical demos of how this can be used to expose a predictive model to anyone you’d like to use it and see how it takes new data and makes a prediction.
Introduction to Big Data, Data Science and Predictive Analytics
Big Data, ETL Pipelines, Data Mining, Predictive Analytics
We introduce you to the wide world of Big Data, throwing back the curtain on the diversity and ubiquity of data science in the modern world. We also give you a bird's eye view of the subfields of predictive analytics and the pieces of a big data pipeline.
Dataset types, Data preprocessing, Similarity, Data exploration
All great learning opportunities are built on a solid foundation. This session is jam-packed with all the background information, technical terminology, and basic knowledge that you will need to hit the ground running on the first day of the bootcamp.
R basics, R data types, R language features, R visualization
Here we introduce the basics of the R programming language. R is a free, open-source statistical programming platform. It is designed to make many of the most common data processing tasks as simple as possible. With this knowledge, you'll be able to engage fully with the hands-on exercises in the class.
Introduction to Azure Machine Learning
Azure ML basics, Azure ML preprocessing, Azure ML visualization
Azure Machine Learning Studio is a fully featured graphical data science tool in the cloud. You will learn how to upload, analyze, visualize, manipulate, and clean data using the clean and intuitive interface of Azure ML
Feature Engineering, Model Training, Model Evaluation, Model Tuning
You will apply your learning, knowledge and skills of data science throughout each day of the bootcamp. We coach you throughout the week to put those new skills to the test with a real problem. Kaggle's Titanic survival prediction competition is the perfect testing ground to cut your teeth on. You'll compete against your fellow students, with the top 2-3 contenders receiving a special prize.
Naive Bayes is one of the most popular and widely used classfication algorithms, particularly in text analysis. It is also a simple, fast, and small algorithm suitable for use on datasets of any size. We teach you how Naive Bayes works, why it works, and when it is likely to break down.
Logistic Regression is one of the oldest and best understood classification algorithms. While not suitable for every application, it is fast to run and cheap to store. We will teach you how logistic regression fits a dataset to make predictions, as well as when and why to use it.
With the massive increase in velocity and volume of data, even the largest and fastest SQL database lags under the load of millions of requests per second. We teach you how NoSQL databases solve this problem, sacrificing a small amount of consistency for a massive increase in durability.
The world of data science and data engineering is larger than we have time to cover in the bootcamp. We want you to be as equipped to tackle this world as possible, so we have written a 350+ page textbook filled with step by step tutorials introducing you to many different tools. You will get a copy of this book at the bootcamp, allowing you to learn this additional information at your own pace.
Numerous data science topics from Time Series Forecasting, to Churn Prediction, to Resume Preparation, and more.
Your learning does not stop after the bootcamp. You’ll be able to tune into a live webinar and keep practicing your skills with a walk-through example or exercise on a new topic every two weeks. Master your art and strengthen your skills with regular practice. The webinars will also be recorded to view at a more convenient time.