Data literacy is a crucial but rare skill to have for any modern-day business. Our bootcamp curriculum teaches working professionals how to extract actionable insights from data enabling you to solve real-world problems in the shortest duration possible. A series of live in-person instructor-led tutorials will teach you the fundamentals of data science and equip you with skills in R programming and Azure machine learning tools for data science. Our carefully crafted curriculum provides the right mix of theoretical concepts, hands-on practical exercises, and business interpretation of results.
Who should attend?
Working professionals who want to add data science to their current positions, or who want to learn more about this new field.
You should have an interest in data science and data engineering as well as knowledge of at least one programming language. However, many of our attendees come to us with little to no programming experience. Our pre-bootcamp materials will get you where you need to be to hit the ground running.
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
Data Science Fundamentals
Storytelling with Data
Communicating actionable insights. Various possible interpretations of plots. Storytelling with data. Bias in data acquistion, transformation, cleaning, modeling and interpretation
Experienced data professionals will tell you that storytelling is one of the most important skills for communicating insights. We will practice the skill of storytelling while presenting analysis.
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 and 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.
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.