Over the past year and a half, we’ve amassed hundreds of data science bootcamp reviews and gathered thousands of data points to bring you our list of recommendations and rankings.
Data science bootcamps are immerse training programs that help students from all usually technical backgrounds transition into a data-oriented career. Since 2012, these private education courses have been training data scientists/engineers with strong industry job placement records. Many mid-career professionals are learning data skills and landing jobs in tech by going through the bootcamp model. However, due to the huge variety of data-related jobs and the specific skillsets needed for different positions, navigating through the complex career transition is challenging.
This article will help you kickstart your research. Additionally we present the differences between different data-oriented careers and hopefully you can find the right program to match the job that you are looking for. Both online and offline schools are considered in this article.
The Best Data Science Courses
Here is a list of date science programs who have also made it onto our shortlist, but do not currently have a lot of alumni reviews or publicly available info.
Bit Bootcamp is based in New York. Both the 12-week Algorithm Training course and 4-week Big Data/Hadoop Training course are immersive and use diverse methods -- live instruction, class collaboration and technical fundamentals -- to cover much more than just the basics of data science. By the end of the course, students will not only have a portfolio of real-world projects but will also have access to a network of employers and career preparation. Prospective students are expected to have familiarity with SQL and programming like Java, C# and C++, as well as solid math and problem solving skills.
The Data Incubator is an intensive fellowship that seeks to transform scientists and engineers into data scientists and quants. The fellowship is seven weeks long and includes training in technical skills, like software engineering, statistics, data visualization, databases and parallelization, and soft skills like communication techniques and networking . It also provides mentorship opportunities, employer-paid scholarships and access to innovative employers. Fellows can either attend the program full time in person in New York City, San Francisco or Washington, D.C., or part time online.
The Eric & Wendy Schmidt Data Science for Social Good Fellowship is a University of Chicago program that runs for 12 weeks in the summer. The program trains aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. In the program, fellows work to help governments and nonprofits solve real-world problems dealing with education, health, transportation, economic development and more. Prospective fellows are expected to be graduate students or at least seniors in college and have a passion for solving problems with social impact.
An intensive 8-week introduction to data science, the Microsoft Research Data Science Summer School is a course for college students in the New York City area. Course work includes both data science and group research projects, and classes are taught by leading scientists at Microsoft Research. In an effort to increase diversity, the school encourages women, minorities and individuals with disabilities to apply. Each student receives a $5,000 stipend and a laptop.
The intensive, 7-week fellowship touts itself as the bridge to a career in data engineering. It takes place in New York City and Silicon Valley and is open to anyone with a strong background in math, computer science and software engineering fundamentals. They also seek fellows who come from positions in the industry or directly from degree programs (bachelor’s, master’s and PhD). In the program, students will be able to participate in project-based learning and work with top industry mentors.
Comparison of data analyst, data scientist, and data engineering roles
A data analyst role can be found at pretty much any large corporation or government organization. From accounting, to risk analysis, to a/b testing, to working on government data, there are a lot of data analyst roles out there. A data analyst is basically a junior data scientist. It’s a good place to start if you don’t have a very technical background and have only taken one or two statistics classes.
You won’t be required to have mathematical background or a PhD/research experience, but you will be required to be diligent, strong at communication, and able to perform computer work. Once you get more experience as a data analyst, you can take more advanced courses, earn a master’s degree or consider a data-science bootcamp to jump into a more research-based, analytical role.
Data analysts need to have some basic understanding of the following subjects: Excel spreadsheet, basic statistics (college-level intro classes), basic statistical tools (like STATA/SPSS), producing graphs and diagrams and presenting simple summary results.
A strong attention to detail is necessary as processing data requires much diligence, patience and an eye for error. You'll be manipulating small to large datasets, and it's very easy to make mistakes.
Day-to-day You'll be given specific instruction from a more senior member and will likely be using Excel to process data and produce summary results. Things like bar charts, pie charts, trend lines, simple regression analysis, box plots, etc., will be common day-to-day tasks. As a side note: You may also be querying databases for data using SQL and scripting languages. For this reason, it is not a bad idea to learn some basic SQL programming. It's important to remember that you won't be doing complicated analysis independently or building your own statistical models or any type of predictive analysis. These higher level tasks are usually conducted by data scientists or senior researchers.
Some companies treat the titles of “data scientist” and “data analyst” as the same thing and they are often used interchangeably. However, in general, there are some distinctions between the two. A data scientist’s work usually needs more complicated analysis and a stronger understanding of the fundamentals of statistics. A strong background in college and graduate level statistics coursework is needed for a career as a data scientist. Usually job listings will require a master’s degree in quantitative finance, statistics, or some relevant field.
While a data analyst simply may be doing work in excel to present summary statistics of small datasets, a data scientist will be managing larger data sets from different sources. They'll likely be comfortable with Python and R programming and using advanced statistical models and tools like STATA and SPSS. SQL and basic scripting languages are a must-know for data scientists
Data scientists are often employed by technology and financial sectors, where huge volumes of data are being processed every day. As new data comes in and new problems come up, these data scientists are employed to find ways to optimize a company’s marketing campaign, optimize a hedge fund’s trading algorithm, or come up with new ways to predict or model consumer behavior. The end goal is to make full use of the company’s data to help generate profits and make the products better.
Note that in different industries, often times they require specialized knowledge. For example, in the medical industry they require knowledge of biostatistics and bio-statistical models, which can be different to financial statistics and financial modeling.
A data engineer is very different to a data scientist. Think of a data engineer as more of a computer scientist who specializes in building systems to manage data. They focus on creating robust data systems that can aggregate, process, clean, transform, and store large amounts of data. Typically in large corporations a data engineer builds a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into databases or datasources. Data engineers are typically software engineers by trade. Instead of data analysis, data engineers are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place.
Data engineers essentially lay the groundwork for a data analyst or data scientist to easily retrieve the needed data for their evaluations and experiments.
Skills and tools: Data engineers need to have strong knowledge of core computer science principles and software development experience. In addition, they need to have expertise in new technologies that help manage large datasets. These technologies and concepts include MapReduce, NoSQL databases, MongoDB, SQL, Hadoop, Storm, and other various Dev Ops tools like Chef. You will need the ability to learn whatever technology the company is using to manage their data systems, and there are a wide variety of them, although the core underlying principles are very similar.
The McKinsey Global Institute has predicted that by 2018 the U.S. could face a shortage of between 140,000 to 190,000 people with deep analytical skills, and a shortage of 1.5 million managers and analysts who know how to leverage data analysis to make effective decisions.
With that in mind, there a going to be a lot of job opportunities opening up for people with the right skill set. As a results a lot of new education programs have popped up to fill that need and gap. However, being a highly-specialized profession, transitioning into a data science career will require not just basic training, but lots of specialized coursework and experience.
According to DataJobs, national salary ranges for the following data job are as follows:
Data analyst (entry level): $50,000-$75,000
Data analyst (experienced): $65,000-$110,000
Data scientist: $85,000-$170,000
Database administrator (entry level): $50,000-$70,000
Database administrator (experienced): $70,000-$120,000
Data engineer (junior/generalist): $70,000-$115,000
Data engineer (domain expert): $100,000-$165,000
Planning to build your tech career in 2017? You may want to consider a few high-demand fields that are positioned to take off this year. We’re profiling careers to watch, along with everything you need to get started.
High-Demand Job: Data Science
- Market Growth: By 2018, data science jobs in the U.S. will exceed 490,000, with fewer than 200,000 available data scientists to fill these positions (McKinsey & Co.)
- Average Salary: Between $116,000 and $163,500 in 2017 (Forbes)
- Job openings: Shortage of up to 1.5 million by 2018 (McKinsey & Co.)
If you are comparing tech careers, you’ve probably heard some of the hype surrounding Data Science jobs. Glassdoor ranked Data Scientist as the best job in America in 2016, and Harvard Business Review went so far as to name Data Scientist “The Sexiest Job of the 21st Century”.
Sure, the hype might be a bit over-the-top, but there’s no question that data science job growth isn’t slowing down anytime soon. Companies are now working with more data than ever, and need employees with the training necessary to make sense of the numbers. Thanks to demand, Data Scientists enjoy high earning potential, a wealth of career opportunities, and the large number of job openings.
It’s sometimes hard to know the best way to learn data science because the term varies widely. Many companies are seeking different skillsets, expertise, and experience levels. For example, you could be working for a B2C company that is looking to better understand their customer base, or you might be working for a company that offers data as the product. When you begin your data science training, it’s important to have a clear idea of how you would like to use your skills.
Regardless of your path, you will likely need to demonstrate the following skills to land a job:
Programming Languages: A statistical programming language like R or Python, and a database querying language like SQL.
Basic Statistics: At least a basic understanding of statistics is crucial. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc.
Machine Learning: This is especially important if you plan to work at a large company, or a company that offers data as a product. You can use R or Python libraries for many machine learning techniques, but it’s still an important concept to understand at a high level.
How To Become A Data Scientist
1. Learn The Basics. Before you dive into a bootcamp, make sure you’ve mastered the basics. Check out one of these free online courses to get started:
2. Research Data Science Bootcamps. Once you’ve learned the basics, a Data Science bootcamp can help you fill any gaps in your knowledge and get you ready for an entry-level data science job.
3. Choose A Career Path.A Data Science Team consist of multiple roles with slightly different skillsets, that all work together to tackle a problem. Common roles are:
Data Scientist The data scientist uses a range of tools to take a project from start to finish. As a Data Scientist, you’ll need to master the ability to manage and analyze raw data, and share insights in a compelling way. Skills include predictive modeling, Python, SQL, R, and distributed computing. Medium Data Scientist Salary: $113,436.
Data Engineer: A great career transition for someone with a background in software engineering. The Data Engineer’s secret weapon is fluency in both statistical programming languages and languages used in web development. Skills include database systems, database modeling, and languages like SQL, R, Matlab, and Python. Medium Data Engineer Salary: $95,526.
Data Analyst: The Data Analyst is the key liaison between the data team and the rest of the company. During your Data Analyst training, you’ll need both technical data science chops as well as business and communications experience. Skills include programs like R. Python, and SQL, statistics and business communications. Medium Data Analyst Salary: $60,476
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