Springboard Data Analytics Bootcamp Review: Is it worth in 2024

Are you ready to embark on a rewarding and exciting journey into the world of data science? If you’re contemplating which data analytics career track to take, you’re not alone. The demand for skilled data scientists, data analysts, and business analysts is skyrocketing as organizations strive to leverage data-driven insights to stay ahead in today’s competitive landscape. 

In this blog post, I’ll share my experience with the Springboard Data Analytics Bootcamp and shed light on whether it’s worth considering in 2023.

The Springboard Data Analytics program is designed to equip students with the skills and knowledge necessary to harness the power of big data and address real-world challenges. Through a carefully curated syllabus, students dive into various aspects of data analysis, gaining a comprehensive understanding of data-driven decision-making.

Unveiling the Path to Transformation: My Background and Aspirations

Like many others, I found myself at a crossroads in my career. Armed with a college degree, I was stuck in a mundane job at a factory, yearning for something more fulfilling and promising.

The tech industry intrigued me, and I believed data science could be the key to unlocking a fulfilling and prosperous future. I knew I needed a technology-based program that could guarantee a job and propel me into a successful tech career.

After extensive research and comparing various data science career tracks, I settled on the Springboard Data Analytics Bootcamp for several reasons. Firstly, around six months suited my timeline, allowing me to quickly transition into a new career. 

Secondly, the course offered robust career services, including career coach access. This vital resource could provide valuable guidance throughout the job search process. Lastly, the promise of lifetime access to course materials was an added incentive, ensuring continuous learning and growth after completion.

The Springboard Data Analytics Bootcamp: A Journey of Learning and Discovery

As I embarked on my data science journey with Springboard, I was welcomed by a well-structured and engaging curriculum delved into essential concepts, tools, and techniques. The program began by framing structured thinking using the HDEIP and SMART frameworks, powerful problem-solving approaches in real-world scenarios.

Analyzing Business Problems:

One of the early modules in the bootcamp focused on data analysis using Microsoft Excel. Although the tutorial videos provided a solid introduction, I yearned for more in-depth coverage and practice.

Having previously taken a Coursera Excel course, I felt that the Excel component of the program could have been better developed to prepare students more comprehensively for data analysis tasks in the workforce.

Connecting Data using SQL:

SQL is a fundamental skill for data professionals, and Springboard provided instructional material through DataCamp courses. While these courses covered essential SQL concepts such as SELECT, FROM, WHERE, JOINS, and GROUP BY, I hoped for more extensive projects to hone my SQL proficiency further.

Analyzing the Data

Analyzing data is necessary for a better understanding of data. It is done in 3 ways:

1. Financial Analysis
2. Economic Data Analysis
3. Statistical Data Analysis

Once the data is in front of you, you need to translate that into a way that makes business sense of it.

Springboard Data Analytics Bootcamp will make sure you learn financial concepts, apply problem-solving and analytical skills, and work on real-life case studies.

You will also learn economic principles and how it impacts business and decision-making process with the help of data.

Statistics is necessary to add an edge in the decision-making process of any business. In this boot camp, you will learn two different types of statistics – descriptive and inferential.

You will learn all three types of data analytics in this Springboard Data Analytics Bootcamp with real-life case studies.

Visualizing Data with Python:

Aspiring data scientists must master programming languages, and Springboard emphasizes Python for data visualization and analysis. Although DataCamp was again the primary resource for learning Python, practical exercises and projects are crucial for solidifying programming skills.

The second large project, where Python was employed for data analysis and visualization, provided valuable applied practice. However, I needed to independently relearn and apply Python concepts, as the DataCamp experience didn’t fully align with real-world data science projects.

Capstone Projects: Real-World Applications and Mentorship

One of the highlights of the Springboard Data Analytics Bootcamp was the capstone projects. We were free to choose our datasets from sources like Kaggle and formulate the underlying questions we aimed to address. Assigned individual mentors, who were experienced industry professionals, guided us in making project-related decisions and provided invaluable feedback during weekly discussions.

The first capstone project allowed me to leverage Excel and Tableau to delve into data insights. In contrast, the second project was more advanced, employing Python and Tableau for data analysis and visualization. Presenting my work to the graders was challenging but ultimately rewarding, reflecting the culmination of my learning experience.

Flexibility and Self-Paced Learning

In a world where many professionals have busy schedules, the flexibility of the Springboard Data Analytics Bootcamp can be a significant advantage. In a self-paced program, students can adjust their learning pace according to their personal commitments and preferences. This feature allows individuals to maintain their jobs or handle other responsibilities while upskilling.

Job Placement and Career Services

While the quality of education is essential, the ultimate goal for most bootcamp participants is to secure a job in data analytics. Springboard recognizes this and provides robust career services to assist students in their job search.

Career coaches offer personalized resume-building advice, interview preparation, and networking strategies. Additionally, Springboard has partnerships with various companies, which may help graduates connect with potential employers.

I had a college degree, but I was currently working in a factory. I hated my job, and it was going nowhere.

I was looking for a technology-based program that would get my foot in the door at some tech job that would actually be a career.

At the time I shopped around for a program that was roughly 6 months and not relatively too expensive (at the time), and I think I chose Springboard for its job search resources.

Right now I am searching for a job, but it has only been one month.

Here I am sharing my journey in the Springboard Data Analytics program to give you insights into how I prepared and successfully completed it.

Why should you choose Springboard Bootcamp for Data Analytics?

Springboards bootcamp is one of the most popular Data Analytics courses on the Internet. It’s easy to see why it’s so popular. With its interactive lectures, job guarantee, and engaging content, Springboard ensures that you’ll be engaged for hours on end.

The self-paced, part-time bootcamps offered by Springboard will prepare you for the workforce in six to nine months while you sit at home. Prep courses are also linked to courses in data science and software engineering.

• Comprehensive curriculum: The Springboard Data Analytics Career Track covers all the essential skills for data analytics, from the basics of data cleaning and preparation to advanced machine learning techniques.
• Experienced instructors: The Springboard Data Analytics Career Track is taught by experienced instructors who are experts in the field.
• Hands-on projects: The Springboard Data Analytics Career Track includes hands-on projects that allow you to apply your skills and build your portfolio.
• Career coaching and mentorship: The Springboard Data Analytics Career Track provides career coaching and mentorship to help you land a job in data analytics.

The Springboard team has put together an excellent course that provides real-world examples of data analytics in action – from how to calculate ROI, to customer segmentation analysis.

Springboard also offers a lifetime access pass which means you can come back at any time!

If you’re looking for a new career or just want some extra skills in your arsenal, then I would highly recommend checking out the Springboard Data Analytics course.

Highly Recommended: Udacity Data Analyst review

Syllabus of Springboard Data Analytics

Springboard Data Analytics program will help you learn how to use the power of big data to solve real-world problems. The Springboard Data Analytics Bootcamp syllabus covers:

1. Framing structured thinking

The first module lays the foundation for structured problem-solving by applying two powerful frameworks: HDEIP and SMART. The HDEIP framework stands for Hypothesis, Data, Experiment, Insights, and Product. In contrast, the SMART framework encompasses Specific, Measurable, Achievable, Relevant, and Time-bound characteristics for goal setting.

Structured thinking is a fundamental skill that permeates various industries, not just data analytics. By employing these frameworks, individuals can break down complex problems into manageable components, leading to well-defined solutions.

The practicality of framing structured thinking is not limited to the business place; it extends to numerous scenarios, including personal and professional decision-making. Mastering this skill can significantly enhance critical thinking and strategic planning abilities.

The structures used HDEIP and SMART frameworks, which are ways of framing problems. 

These seemed obvious to me, and I don’t know if they are used specifically in the business place and worth learning. 

We practiced writing some statements and following these frameworks in business scenarios. 

2. Analyzing business problems

In this module, the focus shifts towards analyzing business problems, and indeed, a significant aspect of this module involves utilizing Microsoft Excel. While the tutorial videos and project provide an introductory experience, some learners seek additional practice and in-depth coverage of Excel.

Microsoft Excel remains a pivotal tool in data analysis due to its versatility and widespread use in various industries. Proficiency in Excel empowers data analysts to manipulate and interpret data efficiently, paving the way for data-driven insights and business recommendations.

Enhancing the coverage of Excel topics and incorporating hands-on projects that mirror real-world business scenarios could further bolster students’ confidence and competence in utilizing Excel as a data analytics tool.

I’m assuming this is referring to the Excel unit.  This was done mainly through tutorial videos and a project. 

With the importance of Excel to data analysis, I thought there should be more practice and more in-depth coverage of Excel. 

While the project reviewed common Excel skills, it definitely didn’t prepare me for a career using it.  Having taken a Coursera Excel course, I felt they left a lot out. 

3. Connecting data using SQL

Structured Query Language (SQL) is the bedrock of data manipulation in relational databases. In the Springboard Data Analytics Bootcamp, the teaching of SQL primarily revolves around DataCamp courses, encompassing essential concepts like SELECT, FROM, WHERE, JOINS, GROUP BY, and subqueries.

While the small project provides an opportunity to practice basic SQL queries, some learners express the desire for more comprehensive projects to deepen their SQL proficiency.

SQL proficiency is indispensable in data analytics, as it enables data professionals to extract valuable insights from large datasets.

By incorporating hands-on projects that require students to navigate complex data sets and perform advanced SQL queries, the bootcamp can empower learners to master SQL effectively and tackle more intricate data analysis tasks in real-world scenarios.

Most of the teaching of SQL was done via DataCamp courses that included select, from, where, joins, group by, subqueries, etc. 

There was also a small project that tested these skills, but there were no comprehensive projects.  I finished this topic feeling like I could write a basic SQL query but not go much further. 

4. Visualizing data with Python

As the field of data analytics increasingly embraces programming languages, the Springboard Data Analytics Bootcamp introduces Python as a critical tool for data visualization and analysis. The importance of hands-on practice with Python cannot be overstated, as mastering a programming language demands consistent application and exposure to real-world data scenarios.

While DataCamp is the primary resource for learning Python, some learners expressed needing additional practical exercises and projects. The bootcamp addresses this by incorporating a second large project, where students use Python to analyze and visualize data, allowing for applied practice.

Python’s versatility and libraries such as Pandas and Matplotlib make it a popular data analysis and visualization choice. Mastery of Python empowers data analysts to conduct complex data manipulations, perform statistical analyses, and present insights through compelling visualizations.

By emphasizing more hands-on Python projects, learners can effectively build their confidence in utilizing Python for data-driven decision-making.

This was also done using DataCamp.  I think learning a programming language requires a lot of practice, which I did not get with only doing DataCamp. 

In our second large project, we had to use Python to analyse and visualize our data, so we got some applied practice there. 

I basically felt like I had to relearn and teach myself python when doing my second project because DataCamp and applied python to a project are two totally separate experiences. 

Project Experience

Projects were of our own choosing. We chose the dataset (Kaggle or something) and the underlying question we wanted to tackle. We had individual mentors that would help us decide these things.

I would say it was as challenging as you wanted to make it, although I’m not sure what other people did as projects or how hard other mentors pushed people.

The first project utilized Excel and Tableau, while the other used python and Tableau.

At the end of the project, we presented to the graders our 10-15 minute presentation, which I think probably passed everyone, although it was intimidating.

I hope you are finding this Springboard Data Analytics Review interesting!

Cost & Duration Of Springboard’s Data Analytics Course

Cost: $1̶1̶5̶0̶0̶ $8500

The price of the Springboard Data Analytics Bootcamp, approximately $8500, initially struck me as relatively high. However, it’s essential to consider the value the program offers. The career services, including access to a dedicated student advisor and career coach, proved instrumental in my job search journey.

Springboard’s job guarantee policy provided additional reassurance that the investment was not in vain. Students who fulfill the minimum job-seeking requirements and do not find a job within six months of completing the course are eligible for a full refund.

I paid roughly $6000 for the 3-year loan, which was near the price of the course. I think the price is similar to other courses when I was first researching this.

I felt like it was overpriced for the amount of material, but I do appreciate the job resources (and job coach).

I felt like navigating my way through finding a new job and getting a certificate “heavy” enough to get a new job, I needed to pay that much money. (In addition to the coursework, job finding content was also added.)

You also get a full refund if you follow minimum job-seeking requirements and don’t find a job within 6 months of finishing the course.

Part of this job guarantee involves passing a mock job interview and technical interview that aren’t very hard but you must pass.

Duration: 6 Months

The six-month duration of the bootcamp was manageable, even for someone with a full-time job. However, as I completed the program, I did feel that more practice and hands-on projects would have better prepared me for the dynamic data science field.

The course took 6 months, and for someone with a full-time job, it was a fair amount of material and projects to go through in that time, but after finishing I felt like I was under prepared and wished I had more practice on the aforementioned course topics.

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Pros and Cons of Springboard Data Analytics Program

Springboard: Pros and Cons

Reflecting on my Springboard experience, there are several aspects I genuinely appreciated, as well as some areas for improvement:

Pros:

Career Coaching Support: The access to a dedicated student advisor and career coach for six months after the program was invaluable. My coach was genuinely invested in my success and provided guidance on navigating the job market and acing interviews.

Flexibility and Independence: Springboard’s structure allowed for personalized exploration through my chosen capstone projects. This autonomy empowered me to delve into areas that aligned with my interests.

Real-World Relevance: The program incorporated real-world examples of data analytics applications, helping me grasp the practical implications of the concepts taught.

I liked the fact that after the program, you get access to a career coach for 6 months. I felt like they were really invested in you not only finishing the project but also getting a new job.  I also like the idea of doing two large projects completely independently instead of doing prepackaged projects.

Cons:

Mentor Expertise: While I appreciated my mentor’s friendly approach, I felt their expertise was limited primarily to Excel. I yearned for mentors with broader knowledge across various data science topics.

Additional Practical Exercises: The program could have included more practical exercises and hands-on projects to reinforce learning and build confidence in key tools and techniques.

We were assigned mentors (industry professionals), in which we discussed the course once a week. I found my mentor to be friendly, but not really a professional in anything besides Excel. I also felt like I didn’t have enough practice in any of the topics.

Not only that, but I also wish I’d had more guidance for the projects, although I understand why they wanted us to do them independently.

Maybe that was just my mentor. I don’t mind the fact that the material was outsourced from other websites, but I wonder why it was that expensive when they’re essentially just giving us links for instruction.

Also read: Bloomtech Data Science review

Conclusion: Is it worth it?

Deciding whether the Springboard Data Analytics Bootcamp is the right fit requires careful consideration of individual goals and learning preferences.

For individuals seeking a structured program and guidance in their career transition, Springboard can be a valuable option. The career coaching support and job guarantee, coupled with Springboard’s lifetime access to course materials, provide a level of security and direction for job seekers.

However, prospective students should know that the bootcamp may benefit from enhancements in certain areas, such as additional practical exercises and diverse mentor expertise.

Moreover, supplementing the program with other learning resources can help students maximize their learning experience and make the most of the investment.

Springboard offers a solid foundation for data analytics aspirants. Still, weighing its features against individual needs and aspirations is essential.

Pursuing a career in data science demands a commitment to continuous learning and self-improvement, and the Springboard Data Analytics Bootcamp can serve as a stepping stone toward a fulfilling and prosperous data science career in 2023 and beyond. 

With the right blend of determination, technical and communication skills, and mentorship, you can unlock the potential of data science and shape your future in this rapidly evolving industry. Remember, the journey to success is about embracing opportunities, making informed decisions, and becoming the data scientist you aspire to be.

I can’t say I recommend it compared to other courses, and I don’t feel like I got my money’s worth.

However, I do recommend it in the sense that if you want to change your career drastically without going back to college, you’re probably going to have to learn something in a structured manner and get career coaching to promote yourself (considering you may be sort of under prepared, or you have no degree to prove you learned a lot of things).

I hope this Springboard Data Analytics review post has helped you to make an informed decision about which data analytics course is best for your needs.

Notes: I also learned about basic statistics, mostly from Khan Academy. I felt very unprepared in this area. Although I did learn the basics, I felt if I took a basic statistics final test, I might not pass. 

We also learned about basic financial measures (like EBIT) and economic constants (like elasticity), but I thought the program barely touched these topics.