We’re so happy that you’re looking to NSS to help you gain the skills for your new career in data, but you’re probably wondering, “which data bootcamp at NSS is right for me?” Fret not, as this is a common question from our potential students, so let’s compare and contrast the two bootcamps in several different areas, including schedule/cost/logistics, skills taught, who each bootcamp is intended for, and jobs prepared for.
But First, What’s The Difference Between Analytics and Data Science?
One of the simplest ways to describe the general differences between Analytics and Data Science fields is that data analysts are concerned with what has happened or is happening (the past and present) while data scientists focus on what will happen or what could happen (the future). Learn more about the general differences between data science and analytics in our blog post, which can be found here.
Schedule/Cost/Logistics
While all programs and bootcamps at NSS are offered in a synchronous, online learning environment, there are key differences between the programs that stand out when one compares items like length of bootcamp, cost of bootcamp, and other logistical considerations.
The Data Science Bootcamp is currently only being offered as a part-time course while the Data Analytics Bootcamp is offered as both a part-time and full-time courses. The Data Science Bootcamp costs $13,125, with a $5,000 deposit upon acceptance of admission. The Data Analytics Bootcamp is shorter in length and costs $7,875 with a $3,000 deposit upon acceptance of admission. Both classes offer financing and payment plan options.
|
Topic |
Data Science |
Data Analytics |
|
Full-time Length |
Not Available |
15 Weeks |
|
Part-time Length |
Nine months |
Six months |
|
Daily schedule (Part-time) |
Tuesday, Thursday, Saturday |
Tuesday, Thursday, Saturday |
| Full-time daily schedule |
Not applicable |
M-F: 9 AM - 4 PM CT |
|
Regular full tuition |
$13,125 |
$7,875 |
|
Opportunity Tuition available |
Yes |
Yes |
|
Payment plans available |
Yes |
Yes |
|
Student loans available |
Climb Credit, Meritize |
Climb Credit, Meritize |
|
Career development track and placement support |
Yes |
Yes |
Skills taught
Let’s start by acknowledging that the two programs overlap somewhat in terms of goals and skills taught. Both programs are designed to provide motivated adults who have no prior analytics training or experience with a pathway into a new career starting as a junior analysts or data scientist. Both programs provide immersive, accelerated learning models. But while both programs give graduates data skills, they target different types of data jobs for their graduates - or at least the sweet spots in terms of starting career paths for the two programs are different. Could graduates of both programs compete for the same initial jobs - yes, that could happen. But while there is a little overlap in the skills taught in the two programs there are also big differences in the focus of the skills coverage in the two programs.
|
Topic |
Data Science |
Data Analytics |
|
Overall learning focus |
Hands-on training in coding skills required to do data science. Students will learn to apply the Python, R, and SQL languages to data analytics problems.
Hands-on experience sourcing, cleaning, and aggregating data for data science projects. Training on machine learning and natural language processing (NLP) techniques, as well as cloud-based data management through services such as AWS.
|
Hands-on training in spreadsheets, Python, and SQL skills required to do data analytics. |
|
Programming languages and analytics tools |
Python, R, SQL, RStudio and Jupyter Notebooks
|
Python, SQL, Excel Spreadsheets, Jupyter Notebooks, Tableau and Power BI |
|
Industry Fundamentals |
Yes - You’ll learn the project life-cycle of a typical data science project. You’ll learn how to identify the business question, how to create and refine your hypothesis, build models, test and iterate the analysis, and communicate the results. |
Yes - You will study the stages of the analytics workflow, including gathering data, cleaning data, storing data for optimum use, analyzing data, and creating reports and dashboards. You will get practice with the tasks at each stage to understand how they work together in support of delivering actionable insights. |
|
Practical data skills/practices |
Yes - through classroom study and team-based development projects |
Yes - through classroom study and team-based development projects |
|
Databases |
Yes - You’ll master the use of SQL to query relational databases. You’ll also be introduced to the 4 main types of NoSQL systems and understand the tradeoffs of using each one |
Yes - You’ll learn to use SQL to query relational databases. |
|
Front-end Web Development |
Intermediate JavaScript, React framework |
Very light, introductory JavaScript, no frameworks |
|
Machine Learning |
Yes - NSS’s Data Science Bootcamp explores both Supervised and Unsupervised Machine Learning. |
No |
|
Communication, Dashboards, and Reports |
Yes - A key skill for data scientists is presenting the results of their projects to business decision makers and other stakeholders. You’ll learn common tools and techniques of data visualization and how to use them for effectively communicating the story of your data and your analysis. |
Yes - You will learn to communicate your findings via presentation and storytelling, building reports, and creating dashboards. You will learn to evaluate findings for their business value and to target your audience with empathy (understanding what they want). Data visualization tools include Tableau and Power BI. |
Students Intended For
What students are the best fit for each bootcamp?
Applicants for the Data Analytics Bootcamp range from those interested in adding analytical skills to their current work, including analysts who want to enrich their skillset, to those simply intrigued by answering questions with data.
The Data Science Bootcamp does require prior experience or education in statistical reasoning or quantitative research. The Data Science Bootcamp can also be for analytically inclined software engineers, grad students or post-docs in a quantitative field, and BI/Data Analysts who want to “upskill”. You can gain the required statistics skills with our part-time Statistics for Data Science course.
Both programs can provide an on-ramp to careers in data for motivated adults who have the aptitude for this work. For those interested in exploring these fields, our Analytics Jumpstart will give you an introduction to coding with Python.
We’ve outlined the admissions process for the Analytics + Data Science bootcamps in our blog post, which can be found here.
|
Topic |
Data Science |
Data Analytics |
|
Experience or Training Required |
Prior experience or education in statistical reasoning, quantitative research, and/or software engineering for a job as a Data Scientist, Analytics Consultant, Data Engineer or related position. |
None |
|
Steps for admission |
|
|
|
Preparation between admission and first day of class |
Pre-work is sent out 3-4 weeks before class starts, and it consists of two-and-a-half DataCamp courses plus some statistics materials, which take about 20 hours to complete. |
Pre-work is sent out 2 weeks before class starts, and it consists of two DataCamp courses, which take about 10 hours to complete.
|
|
Hours required outside of class per week for homework |
The program design does not require homework to any meaningful degree. Many students spend anywhere from five to fifteen hours a week outside of class reviewing material or practicing coding. |
Full Time: Students will occasionally need to work outside of the class schedule, specifically during the capstone module |
|
Optional Introductory Course Related To Bootcamp |
Yes - Analytics Jumpstart or Statistics for Data Science |
Yes - Analytics Jumpstart |
Jobs prepared for
As we’ve stated earlier, both bootcamps are designed to prepare students for careers in data that are very much in high demand. They target different types of roles and (to some degree) different potential employers. Both types of roles are well compensated. Both types of roles provide high degrees of potential for future career growth and learning.
The Data Analytics Bootcamp is designed to prepare students for careers in analytics and business intelligence, including jobs such as data analyst, business analyst, research analyst, and marketing analyst. The Data Science Bootcamp is designed to prepare students for jobs such as data scientist, analytics consultant, data engineer or related position.
NSS has documented in our community impact reports that over the last couple of years the median starting salary from our bootcamps has been $65,260 for Data Analytics and $86,600 for Data Science.
Throughout each bootcamp, students also prepare for the job search by working with our skilled Career Development team. Students of both bootcamps meet working data scientists and analysts from several industries, attend workshops on resume preparation/marketing themselves, interview preparation, negotiating, and more. Students are also introduced to prospective employers at their class Demo Day. The career development team will continue to support a student’s job search until they get their first job in data.
Have more questions about which program is right for you? Attend our monthly Analytics + Data Science Info Session or connect with us on our Contact page.
