You'll learn the basics of using Python gathering and presenting data to find real insights from data you have, SQL for querying, as well how to design and run your own experiments to gather new data.
Time to build your first models. You'll start in machine learning with supervised models, covering everything from the classics to cutting edge techniques.
The second largest branch of machine learning, here we'll cover various forms of unsupervised models, useful for things like feature reduction and clustering.
Data science is a wide field. Specializations will help you stand out. Pick your path and learn some more advanced skills.
Choose your specialization because Data Science is a highly contextual discipline. No other bootcamp writes curriculum to enable specialization like we do.
Natural Language Processing, usually just simply called NLP, is the key to applying the tools of data science to human language. In this specialization you’ll learn some of the various ways data scientists can extract rigorous analytic insights from text and language. You’ll learn techniques like sense2vec and ngrams for translating text into tabular data and Latent Semantic Analysis for finding topics in text.
The tools you’ll learn can be used to build things like sentiment classifiers or spam detectors, or even automatically create summaries of large pieces of text. Want to be able to algorithmically identify who wrote that novel? Want to automatically summarize each message in your inbox? Quickly tell if the feedback your customers left means they are satisfied? This is the specialization for you.
One of the most exciting advances in data science in the past several years, deep learning is becoming a powerful and essential tool in the data science toolkit. A continuation of the concepts and structures of simple neural networks that have been around for decades but enabled by increased data collection and computing power, deep learning has found incredible power and accuracy across a wide variety of fields.
This growth in deep learning has been enabled by new packages that have made it easier than ever to build custom neural networks. In this specialization you’ll learn two of the most widely used deep learning packages, Keras and TensorFlow. You’ll also cover various neural network structures such as feed forward, convolutional, and recurrent. By the end of the specialization you’ll be ready to deploy these packages in whatever environment you choose, building powerful neural networks to create some of the most accurate models possible today.
Time is an essential part of so many things that data scientists try to model. It’s rare that things happen all at once or where the time that passes doesn’t affect the process. The problem is, most data science models tend to treat time just like any other variable. The truth is, there are plenty of situations where time deserves its own special considerations. The models in this specialization focus on handling the peculiarities of time and how events evolve.
Specifically, this specialization introduces the ARIMA framework for modeling time series data. This allows data scientists to look at the world as an evolving process, allowing the past to inform the future and generate projections of how things will change going forward. This framework is used throughout data science, though it has a particularly relevance to finance.
Our world is filled with networks: Networks of communication as people share information with others, social networks of people’s relationships and associations, and many more. Many relationships between multiple actors can fall into a specific branch of data science called network analysis, a branch of graph theory. It is no surprise that this specialized form of tracing out relationships and networks comes with its own sets of tools. This specialization introduces you to those tools.
Many of us are familiar with social networks, the ideas of friends or followers. That alone can be relatively simple. Then there are friends of friends, people who you follow you but you don’t know (an asymmetric network) and much more. If you’re interested in these kinds of relationships and formalizing the language and analysis around them, network analysis is for you. You’ll learn the tools to visualize and analyze the various forms of networks you could run into in the world of data science.
Over recent years, biology has seen an immense amount of growth in the amount of data that has been collected. That has led to biology seeing some of the strongest growth in demand for the skills of data science. However, biology is a specialized context, it doesn’t perfectly copy the tools of data scientists in tech. This specialization introduces some of the ways biostatistics can vary from the data science toolkit.
Specifically, this section focuses on introducing survival analysis. The world is full of things that are dying or disappearing at some known but random rate, and also potentially giving birth or arriving at some other known but random rate. Survival analysis gives data scientists the tools to look at a population and account for those arrivals and departures at the population level. It is a powerful tool in biostats, though it also has a wider relevance.
Economics and the social sciences have an immense need for the skills of data science, but they approach the problems with their own set of priorities. Specifically, these fields often emphasize things like interpretability over simple algorithm performance. This leads to these fields often emphasizing a specific set of models and tools for performing analysis.
This specialization introduces some of those tools. Specifically, we’ll cover robust regression, which allows for linear regression type modeling but with various adjustments to mitigate the effect of outliers. We also cover panel data, a unique form of data that follows multiple subjects for multiple periods of observation. These tools can begin a toolset to really dive into the data driven problems of economics and the social sciences.
Your personal mentor is not only your partner-in-code, but a seasoned industry-expert who is as dedicated to your success as you are.
We invest as much in you as you do in us. We put a whole team of support behind you, including: a mentor, program manager, career coach, and career services manager.
Even after graduation, we’re still invested in you. Your very own career coach will spend the next six months helping you navigate the job market, apply to positions, and ace your interviews.
It's on your own time - but you have to work to make it work. That means finding the time before and after work to commit to the program.
Learn the right things in the right order. Our curriculum is battle-tested and refined to keep pace with the state of the art in technology and the demands of the job market.
You bring the ambition, we'll bring the support. With over 40 hours of Q&A sessions and two dedicated personal mentor sessions a week, there's no doubt you'll be working in the right direction.
Build deeply technical and impressive projects to build your portfolio and prove your tech abilities to employers.
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Fill out our quick online application by answering a few questions.
This interview will make sure your learning preferences and schedule align with the course.
Our tech screening will test your ability to learn and solve problems.
4-week prep phase to master Data Science fundamentals, plan your career, and submit a capstone research proposal.
You’ll step into the full program after passing a technical assessment and having your proposal approved.
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