Python is one of the languages that has become the lingua franca of data science (the other being R). In this module, you'll learn to program in Python and start using an ecosystem of useful and powerful Python-based tools for doing data science and building an online portfolio.
- iPython Notebook
- Git and Github
Estimated Time: 10+ Hours
It is estimated that data scientists in industry spend the most time on data wrangling i.e. cleaning the raw data and getting it into a format amenable for analysis, usually with the help of semi-automated tools. In this module, you'll learn the most common tools and workflows in Python that make this normally onerous task a snap.
- Deep Dive into Pandas for Data Wrangling
- Data in files: Work with a variety of sources from unstructured/semi-structured text files (.txt) to delimited/structured/nested format files like excel, csv, json, xml etc.
- Data in Databases: Get an overview of relational and NoSQL databases and practice data manipulation with SQL.
Estimated Time: 17+ Hours
If there's one thing that most data scientists would have loved to know before they entered the field, it's that data science is not just about the math, the algorithms and the analysis, it's also about telling a good story. In real life, data scientists don't work in a vacuum - there's always a client, internal or external, waiting on the results of their work. A data story is a powerful way to present insights to your clients, combining visualizations and text into a narrative. But storytelling is an art, and needs creativity. This section will try to get your creative juices flowing by suggesting some interesting questions you can ask of your dataset, and a few plotting techniques you can use to reveal insights.
You’ll practice the concepts learned by creating a data story.
Estimated Time: 10+ Hours
Statistics is the mathematical foundation of data science. Within statistics, inferential statistics is a set of techniques that helps us identify significant trends and characteristics of a data set. Not only is it useful to explore the data and tell a good story, but also opens the way for deeper analysis and actual predictive modeling. In this module, we cover several important inferential statistics techniques in detail.
- Theory and application of inferential statistics
- Parameter estimation
- Hypothesis testing
- Statistical significance
- Correlation and regression
- A/B Testing
Estimated Time: 13+ Hours
Machine Learning combines aspects of computer science and statistics to extract useful insights and predictions from data. Machine Learning is what lets us make useful predictions and recommendations, or automatically find groups and categories in complex data sets. In this module, we'll cover the major kinds of machine learning algorithms (supervised and unsupervised), with several techniques within each of them. You'll learn when these algorithms are useful, the assumptions they incorporate, the tradeoffs they involve and the various metrics you can use to evaluate how well your algorithm performs.
- Supervised and unsupervised learning
- Fundamentals: Regression, Naive Bayes, SVM, Decision trees, Clustering
- Advanced: Recommender systems, Anomaly detection, Time series analysis
- Validation and evaluation of machine learning
- Feature engineering
- Best practices for applying machine learning
Estimated Time: 53+ Hours
Have you seen the stunning interactive visualizations on news websites such as New York Times or FiveThirtyEight? Have you wondered how those are created? These advanced interactive visualizations not only look great and show your skills, but are also excellent tools for exploring complex, high-dimensional data sets.
Estimated Time: 5 Hours
You now know how to work with data sets that easily fit in the memory of your laptop. But what happens when that's not the case? A data scientist often has to know how to scale these analyses and algorithms to really huge data sets. This is where "Big Data" technologies like Hadoop and Spark come in. Hadoop is an open-source implementation of map-reduce, one of the first major algorithmic innovations in big data, and arguably the algorithm that allowed Google to become the behemoth it is today. Spark is Hadoop's newer, younger cousin -- a technology that addresses some glaring flaws and inefficiencies in Hadoop, and allows many complex machine learning and other analytical techniques to be implemented at scale in highly efficient ways.
- Intro to Big Data
Estimated Time: 10 Hours
In this program, you'll complete two Capstone Projects for your portfolio. You'll work on the first project as you go through the main part of the curriculum, and on the second project as you're focused on your job search.
The Capstone Project is a key part of our curriculum that every student must complete. The project is designed to provide you with the experience of working in a realistic data science scenario. Working with your mentor, you'll pick a data set and a problem of interest. From the start to the finish, your project will be targeted to a specific client (real or imaginary). Using the data science techniques you've learned, you'll not only come up with a reasonable solution to the problem, but learn to present it to them as a compelling story.
Estimated Time: 50 Hours
We provide career material at strategic points both in the curriculum as well as via calls with our expert career support coach. We'll help you create a tailored job search strategy based on your background and goals, teach you how to evaluate companies and roles, show you how to effectively get and ace interviews, and negotiate on salary.
- Anatomy of a tech company
- The job search strategies that top candidates use
- How to build your network and effectively use it to land interviews
- Create a high-quality resume, LinkedIn profile and cover letter
- Interview coaching and practice, including mock interviews for both technical and non-technical topics
- Negotiation success tips
Estimated Time: 35 Hours
Awesome, we just sent the syllabus to your email.
Get a detailed course syllabus in e-mail:
Is this program right for me?
Who is this workshop for?
The Data Science Career Track is for
people with a prior background in statistics and programming.
How much prior experience is needed?
Most students in this course will:
(1) have completed a college-level statistics class, or have equivalent knowledge, AND
(2) know programming well enough to be comfortable picking up a new language using resources on the web.
Other than that, all professional and academic backgrounds are welcome. We've taught software developers, grad students, analysts, and BI / finance professionals.
Schedule & Price
The next class starts on:
April 24th 2017
Deadline for applications is April 10th, 2017. That's in:
15 Days 23 HoursStart My Application
Tuition and payment options
|Upfront for 6 months||Month to month|
|Benefit||Most affordable, save 20% by paying upfront||Pay as you go, only for the months you need|
|Paid at the time of enrollment||$4,800||$1,000|
|Total cost||$4,800||Variable (capped at $6,000)|
We estimate the total effort required at ~200 hours. If you devote 8-10 hours per week, you should expect to complete in 5-6 months.
To read the terms of the job guarantee, head to the FAQ below.
Apply for Data Science Career Track today
Ready to take your career to new level? Apply now!
Spots are limited, and we'll accept qualified applicants on a first-come-first-served basis. Apply early to secure your spot.Start My Application
The application will take 5-10 mins
Full refund within 7 days if you are not happy with the course.
Full refund if you don’t get a job within 6 months of completion.
Complete the course at your own pace. You pay for the course month-by-month.
- You must be 21 years or older.
- You must be proficient in spoken and written English.
- You must be eligible to legally work in the United States for at least 1 year following graduation from the program.
- You must be willing to apply to and accept jobs in one of our 11 supported metropolitan areas in the US.
- You must satisfactorily complete all requirements for graduation
- Monthly Plan: You pay $1,000 per month while you are enrolled in the program, capped at $6,000. After 6 months in the program, we will stop billing you. If you take 6 months to graduate, your total payment is $6,000. If you graduate sooner, you pay less!
- Upfront payment: You pay $4,800 upfront for 6 months. This is a 20% discount on the monthly plan.
- 200-hour curriculum of technical and career materials curated by industry experts.
- Weekly 1-1 video calls with your personally matched mentor.
- Access to an exclusive community and Office Hours with mentors, career coaches and peers.
- Dedicated community managers to answer questions and give feedback on projects within a day.
- Personalized feedback to help you polish your resume, portfolio and social profiles
- 1-on-1 session with a data science career coach to personalize your pitch and job-search strategy
- Lots of interview practice via 1-on-1 mock interviews (behavioral and technical)
- Exclusive access to an employer network
- 100% money-back guarantee if you can't find a job in 6 months
- All taxes and fees.
You should expect to spend at least 7-10 hours per week on the course. Depending on your desired pace, we’ll work with you to create a course plan at the beginning of the course.
Note that many of the videos, etc. in the curriculum are not created in-house by us. Instead, we find and curate the best resources available on the subject. Think of us as a college professor who creates a syllabus by combining the best textbook chapters, articles, papers, and projects (instead of teaching from only her own textbook).
We believe the curated curriculum is our unique strength. By standing on the shoulders of giants, we’re able to update the curriculum frequently, and always teach the latest tools and technologies.
- Your mentor: Data science expert for weekly 1:1 guidance and accountability
- Community Teaching Assistants: 24x7 help when you’re stuck, and detailed feedback on each project
- Career coaches: Schedule for resume reviews, mock interviews, etc.
- Springboard student advisors: Develop lessons plans, or get help with any aspect of the program