data science bootcamp

Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.

 

Unlock data science elements of statistics, Python, models, and more

These bootcamps are focused training and learning platforms for people. Nowadays, individuals tend to opt for bootcamps for quick results and faster learning of any particular niche. In this blog, we will explore the arena of data science bootcamps and lay down a guide for you to choose the best data science bootcamp.

 

 

What do Data Science Bootcamps Offer?

Data science bootcamps offer a range of benefits designed to equip participants with the necessary skills to enter or advance in the field of data science. Here’s an overview of what these bootcamps typically provide:

Curriculum and Skills Learned

These bootcamps are designed to focus on practical skills and a diverse range of topics. Here’s a list of key skills that are typically covered in a good data science bootcamp:

1. Programming Languages:
   • Python: Python is widely used for its simplicity and extensive libraries for data analysis and machine learning.
   • R: Often used for statistical analysis and data visualization.
2. Data Visualization:
   • Techniques and tools to create visual representations of data to communicate insights effectively. Tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are commonly taught.
3. Machine Learning:
   • Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.
4. Big Data Technologies:
   • Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.
5. Data Processing and Analysis:
   • Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.
6. Databases and SQL:
   • This involves managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.
7. Statistics:
   • Fundamental statistical concepts and methods, including hypothesis testing, probability, and descriptive statistics.
8. Data Engineering:
   • Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing.
9. Artificial Intelligence:
   • Concepts of AI include neural networks, natural language processing (NLP), and reinforcement learning.
10. Cloud Computing:
    • Utilizing cloud services for data storage and processing, often covering platforms such as AWS, Azure, and Google Cloud.
11. Soft Skills:
    • Problem-solving, critical thinking, and communication skills to effectively work within a team and present findings to stakeholders.

 

 

Moreover, these bootcamps also focus on hands-on projects that simulate real-world data challenges, providing participants a chance to integrate all the skills learned and assist in building a professional portfolio.

 

Learn more about key concepts of applied data science

 

Format and Flexibility

The bootcamp format is designed to offer a flexible learning environment. Today, there are bootcamps available in three learning modes: online, in-person, or hybrid. Each aims to provide flexibility to suit different schedules and learning preferences.

Career Support

Some bootcamps include job placement services like resume assistance, mock interviews, networking events, and partnerships with employers to aid in job placement. Participants often also receive one-on-one career coaching and support throughout the program.

 

 

Networking Opportunities

The popularity of bootcamps has attracted a diverse audience, including aspiring data scientists and professionals transitioning into data science roles. This provides participants with valuable networking opportunities and mentorship from industry professionals.

Admission and Prerequisites

Unlike formal degree programs, data science bootcamps are open to a wide range of participants, often requiring only basic knowledge of programming and mathematics. Some even offer prep courses to help participants get up to speed before the main program begins.

Real-World Relevance

The targeted approach of data science bootcamps ensures that the curriculum remains relevant to the advancements and changes of the real world. They are constantly updated to teach the latest data science tools and technologies that employers are looking for, ensuring participants learn industry-relevant skills.

 

Explore 6 ways to leverage LLMs as Data Scientists

 

Certifications

Certifications are another benefit of bootcamps. Upon completion, participants receive a certificate of completion or professional certification, which can enhance their resumes and career prospects.

Hence, data science bootcamps offer an intensive, practical, and flexible pathway to gaining the skills needed for a career in data science, with strong career support and networking opportunities built into the programs.

Factors to Consider when Choosing a Data Science Bootcamp

When choosing a data science bootcamp, several factors should be taken into account to ensure that the program aligns with your career goals, learning style, and budget.

 

 

Here are the key considerations to ensure you choose the best data science bootcamp for your learning and progress.

1. Outline Your Career Goals

A clear idea of what you want to achieve is crucial before you search for a data science bootcamp. You must determine your career objectives to ensure the bootcamp matches your professional interests. It also includes having the knowledge of specific skills required for your desired career path.

2. Research Job Requirements

As you identify your career goals, also spend some time researching the common technical and workplace skills needed for data science roles, such as Python, SQL, databases, machine learning, and data visualization. Looking at job postings is a good place to start your research and determine the in-demand skills and qualifications.

3. Assess Your Current Skills

While you map out your goals, it is also important to understand your current learning. Evaluate your existing knowledge and skills in data science to determine your readiness for a bootcamp. If you need to build foundational skills, consider beginner-friendly bootcamps or preparatory courses.

4. Research Programs

Once you have spent some time on the three steps above, you are ready to search for data science bootcamps. Some key factors for initial sorting include program duration, cost of the bootcamp, and the curriculum content. Consider what class structure and duration work best for your schedule and budget, and offer relevant course content.

5. Consider Structure and Location

With in-person, online, and hybrid formats, there are multiple options for you to choose from. Each format has its benefits, such as flexibility for online courses or hands-on experience in in-person classes. Consider your schedule and budget as you opt for a structure and format for your data science bootcamp.

6. Take Note of Relevant Topics

Some bootcamps offer specialized tracks or elective courses that align with specific career goals, such as machine learning or data engineering. Ensure that the bootcamp of your choice covers these specific topics. Moreover, you can confidently consider bootcamps that cover core topics like Python, machine learning, and statistics.

7. Know the Cost

Explore the financial requirements of the bootcamp you choose in detail. There can be some financial aid options available that you can benefit from. Other options to look for include scholarships, deferred tuition, income share agreements, or employer reimbursement programs to help offset the cost.

8. Research Institution Reputation

While course content and other factors are important, it is also crucial to choose from well-reputed options. Bootcamps from reputable institutions are a good place to look for such options. You can also read reviews from students and alumni to get a better idea of the options you are considering.

The quality of the bootcamp can also be measured through factors like instructor qualifications and industry partnerships. Moreover, also consider factors like career support services and the institution’s commitment to student success.

9. Analyze and Apply

This is the final step towards enrolling in a data science bootcamp. Weight the benefits of each option on your list against any potential drawbacks. After careful analysis, choose a bootcamp that meets your criteria. Complete their application form, and open up a world of learning and experimenting with data science.

 

 

From the above process and guidelines, it can be easily said that choosing the right data science bootcamp requires thorough research and consideration of various factors. By following a proper guideline, you can make an informed decision that aligns with your professional aspirations.

Comparing Different Options

The discussion around data science bootcamps also caters to multiple comparisons. The leading differences are drawn and analyzed to compare degree programs and bootcamps, and differentiate between in-person and online bootcamps.

Degree Programs vs Bootcamps

Both data science bootcamps and degree programs have distinct advantages and drawbacks. Bootcamps are ideal for those who want to quickly gain practical skills and enter the job market, while degree programs offer a more comprehensive and in-depth education.

Here’s a detailed comparison between both options for you.

Aspect	Data Science Degree Program	Data Science Bootcamp
Cost	Average in-state tuition: $53,100	Typically costs between $7,500 and $27,500
Duration	Bachelor’s: 4 years; Master’s: 1-2 years	3 to 6 months
Skills Learned	Balance of theoretical and practical skills, including algorithms, statistics, and computer science fundamentals	Focus on practical, applied skills such as Python, SQL, machine learning, and data visualization
Structure	Usually in-person; some universities offer online or hybrid options	Online, in-person, or hybrid models available
Certification Type	Bachelor’s or Master’s degree	Certificate of completion or professional certification
Career Support	Varies; includes career services departments, internships, and co-op programs	Extensive career services such as resume assistance, mock interviews, networking events, and job placement guarantees
Networking Opportunities	Campus events, alumni networks, industry partnerships	Strong connections with industry professionals and companies, diverse participant background
Flexibility	Less flexible; requires a full-time commitment	Offers flexible learning options including part-time and self-paced formats
Long-Term Value	Provides a comprehensive education with a solid foundation for long-term career growth	Rapid skill acquisition for quick entry into the job market, but may lack depth

While each option has its pros and cons, your choice should align with your career goals, current skill level, learning style, and financial situation.

 

Here’s a list of 10 best data science bootcamps

 

In-Person vs Online vs Hybrid Bootcamps

If you have decided to opt for a data science bootcamp to hone your skills and understanding, there are three different variations for you to choose from. Below is an overall comparison of all three approaches as you choose the most appropriate one for your learning.

 

Aspect	In-Person Bootcamps	Online Bootcamps	Hybrid Bootcamps
Learning Environment	A structured, hands-on environment with direct instructor interaction	Flexible, can be completed from anywhere with internet access	Combines structured in-person sessions with the flexibility of online learning
Networking Opportunities	High, with opportunities for face-to-face networking and team-building	Lower compared to in-person, but can still include virtual networking events	Offers both in-person and virtual networking opportunities
Flexibility	Less flexible, requires attendance at a physical location	Highly flexible, can be done at one’s own pace and schedule	Moderately flexible, includes both scheduled in-person and flexible online sessions
Cost	Can be higher due to additional facility costs	Generally lower, no facility costs	Varies, but may involve some additional costs for in-person components
Accessibility	Limited by geographical location, may require relocation or commute	Accessible to anyone with an internet connection and no geographical constraints	Accessible with some geographical constraints for the in-person part
Interaction with Instructors	High, with immediate feedback and support	Can vary; some programs offer live support, others are more self-directed	High during in-person sessions, moderate online
Learning Style Suitability	Best for those who thrive in a structured, interactive learning environment	Ideal for self-paced learners and those with busy schedules	Suitable for learners who need a balance of structure and flexibility
Technical Requirements	Typically includes access to on-site resources and equipment	Requires a personal computer and reliable internet connection	Requires both access to a personal computer and traveling to a physical location

 

Each type of bootcamp has its unique advantages and drawbacks. It is up to you to choose the one that aligns best with your learning practices.

 

 

What is the Future of Data Science Bootcamps?

The future of data science bootcamps looks promising, driven by several key factors that cater to the growing demand for data science skills in various industries.

One major factor is the increasing demand for skilled data scientists as companies across various industries harness the power of data to drive decision-making. The U.S. Bureau of Labor Statistics estimates the data science job outlook to be 35% between 2022–32, far above the average for all jobs of 2%.

 

 

Moreover, as the data science field evolves, boot camps are likely to continue adapting their curriculum to incorporate emerging technologies and methodologies, such as artificial intelligence, machine learning, and big data analytics. It will continue to make them a favorable choice in this fast-paced digital world.

Hence, data science bootcamps are well-positioned to meet the increasing demand for data science skills. Their advantages in focused learning, practical experience, and flexibility make them an attractive option for a diverse audience. However, you should carefully evaluate bootcamp options to choose a program that meets your career goals.

Want to know more about data science, LLM, and bootcamps? Join our Discord community for regular updates!

 

This blog elaborates on a Data Science Dojo vs Thinkful debate when you are looking for an appropriate data science bootcamp.

Choosing to invest in a data science bootcamp can be a daunting task. Whether it’s weighing pros and cons or cross-checking reviews, it can be brain-wracking to make the perfect choice.

To assist you in making a well-informed decision and simplify your research process, we have created this comparison blog of Data Science Dojo vs Thinkful to let their features and statistics speak for themselves.

So, without any delay, let’s delve deeper into the comparison: Data Science Dojo vs Thinkful Bootcamp.

Data Science Dojo 

As an ideal choice for beginners with no prerequisites, Data Science Dojo’s Bootcamp is a great choice. It is a 16-week online bootcamp that covers the fundamentals of data science. It adopts a business-first approach in its curriculum, combining theoretical knowledge with practical hands-on projects. With a team of instructors who possess extensive industry experience, students have the opportunity to receive personalized support during dedicated office hours.

The boot camp covers various topics, including data exploration and visualization, decision tree learning, predictive modeling for real-world scenarios, and linear models for regression. Moreover, students can use multiple payment plans and may earn a verified data science certificate from the University of New Mexico.

 

Thinkful

Thinkful’s data science bootcamp provides the option for part-time enrollment, requiring around six months to finish. Students advance through modules at their own pace, dedicating approximately 15 to 20 hours per week to coursework.

The curriculum features important courses such as analytics and experimentation, as well as a supervised learning experience in machine learning where students construct their initial models. It has a partnership with Southern New Hampshire University (SNHU), allowing graduates to earn credit toward a Bachelor’s or Master of Science degree at SNHU.

Data Science Dojo vs Thinkful features 

Here is a table that compares the features of Data Science Dojo and Thinkful:

Which data science bootcamp is best for you?

Embarking on a bootcamp journey is a major step for your career. Before committing to any program, it’s crucial to evaluate your future goals and assess how each prospective bootcamp aligns with them.

To choose the right data science bootcamp, ask yourself a series of important questions. How soon do you want to enter the workforce? What level of earning potential are you aiming for? Which skills are essential for your desired career path?

By answering these questions, you’ll gain valuable clarity during your search and be better equipped to make an informed decision. Ultimately, the best bootcamp for you will depend on your individual needs and goals.

 

Feeling uncertain about which bootcamp is the perfect fit for you? Talk with an advisor today!

The job market for data scientists is booming. In fact, the demand for data experts is expected to grow by 36% between 2021 and 2031, significantly higher than the average for all occupations. This is great news for anyone who is interested in a career in data science.

According to the U.S. Bureau of Labor Statistics, the job outlook for data science is estimated to be 36% between 2021–31, significantly higher than the average for all occupations, which is 5%. This makes it an opportune time to pursue a career in data science.

In this blog, we will explore the 10 best data science bootcamps you can choose from as you kickstart your journey in data analytics.

 

 

What are Data Science Bootcamps? 

Data science boot camps are intensive, short-term programs that teach students the skills they need to become data scientists. These programs typically cover topics such as data wrangling, statistical inference, machine learning, and Python programming. 

• Short-term: Bootcamps typically last for 3-6 months, which is much shorter than traditional college degrees. 
• Flexible: Bootcamps can be completed online or in person, and they often offer part-time and full-time options. 
• Practical experience: Bootcamps typically include a capstone project, which gives students the opportunity to apply the skills they have learned. 
• Industry-focused: Bootcamps are taught by industry experts, and they often have partnerships with companies that are hiring data scientists. 

10 Best Data Science Bootcamps

Without further ado, here is our selection of the most reputable data science boot camps.  

1. Data Science Dojo Data Science Bootcamp

• Delivery Format: Online and In-person
• Tuition: $2,659 to $4,500
• Duration: 16 weeks

Data Science Dojo Bootcamp is an excellent choice for aspiring data scientists. With 1:1 mentorship and live instructor-led sessions, it offers a supportive learning environment. The program is beginner-friendly, requiring no prior experience.

Easy installments with 0% interest options make it the top affordable choice. Rated as an impressive 4.96, Data Science Dojo Bootcamp stands out among its peers. Students learn key data science topics, work on real-world projects, and connect with potential employers.

Moreover, it prioritizes a business-first approach that combines theoretical knowledge with practical, hands-on projects. With a team of instructors who possess extensive industry experience, students have the opportunity to receive personalized support during dedicated office hours.

2. Springboard Data Science Bootcamp

• Delivery Format: Online
• Tuition: $14,950
• Duration: 12 months long

Springboard’s Data Science Bootcamp is a great option for students who want to learn data science skills and land a job in the field. The program is offered online, so students can learn at their own pace and from anywhere in the world.

The tuition is high, but Springboard offers a job guarantee, which means that if you don’t land a job in data science within six months of completing the program, you’ll get your money back.

3. Flatiron School Data Science Bootcamp

• Delivery Format: Online or On-campus (currently online only)
• Tuition: $15,950 (full-time) or $19,950 (flexible)
• Duration: 15 weeks long

Next on the list, we have Flatiron School’s Data Science Bootcamp. The program is 15 weeks long for the full-time program and can take anywhere from 20 to 60 weeks to complete for the flexible program. Students have access to a variety of resources, including online forums, a community, and one-on-one mentorship.

4. Coding Dojo Data Science Bootcamp Online Part-Time

• Delivery Format: Online
• Tuition: $11,745 to $13,745
• Duration: 16 to 20 weeks

Coding Dojo’s online bootcamp is open to students with any background and does not require a four-year degree or Python programming experience. Students can choose to focus on either data science and machine learning in Python or data science and visualization.

It offers flexible learning options, real-world projects, and a strong alumni network. However, it does not guarantee a job, requires some prior knowledge, and is time-consuming.

5. CodingNomads Data Science and Machine Learning Course

• Delivery Format: Online
• Tuition: Membership: $9/month, Premium Membership: $29/month, Mentorship: $899/month
• Duration: Self-paced

CodingNomads offers a data science and machine learning course that is affordable, flexible, and comprehensive. The course is available in three different formats: membership, premium membership, and mentorship. The membership format is self-paced and allows students to work through the modules at their own pace.

The premium membership format includes access to live Q&A sessions. The mentorship format includes one-on-one instruction from an experienced data scientist. CodingNomads also offers scholarships to local residents and military students.

6. Udacity School of Data Science

• Delivery Format: Online
• Tuition: $399/month
• Duration: Depends on the program

Udacity offers multiple data science bootcamps, including data science for business leaders, data project managers, and more. It offers frequent start dates throughout the year for its data science programs. These programs are self-paced and involve real-world projects and technical mentor support.

Students can also receive LinkedIn profiles and GitHub portfolio reviews from Udacity’s career services. However, it is important to note that there is no job guarantee, so students should be prepared to put in the work to find a job after completing the program.

7. LearningFuze Data Science Bootcamp

• Delivery Format: Online and in-person
• Tuition: $5,995 per module
• Duration: Multiple formats

LearningFuze offers a data science boot camp through a strategic partnership with Concordia University Irvine.

Offering students the choice of live online or in-person instruction, the program gives students ample opportunities to interact one-on-one with their instructors. LearningFuze also offers partial tuition refunds to students who are unable to find a job within six months of graduation.

The program’s curriculum includes modules in machine learning and deep learning and artificial intelligence. However, it is essential to note that there are no scholarships available, and the program does not accept the GI Bill.

8. Thinkful Data Science Bootcamp

• Delivery Format: Online
• Tuition: $16,950
• Duration: 6 months

Thinkful offers a data science boot camp which is best known for its mentorship program. It caters to both part-time and full-time students. Part-time offers flexibility with 20-30 hours per week, taking 6 months to finish. Full-time is accelerated at 50 hours per week, completing in 5 months.

Payment plans, tuition refunds, and scholarships are available for all students. The program has no prerequisites, so both fresh graduates and experienced professionals can take this program.

9. Brain Station Data Science Course Online

• Delivery Format: Online
• Tuition: $9,500 (part time); $16,000 (full time)
• Duration: 10 weeks

BrainStation offers an immersive and hands-on data science boot camp that is both comprehensive and affordable. Industry experts teach the program and includes real-world projects and assignments. BrainStation has a strong job placement rate, with over 90% of graduates finding jobs within six months of completing the program.

However, the program is expensive and can be demanding. Students should carefully consider their financial situation and time commitment before enrolling in the program.

10. BloomTech Data Science Bootcamp

• Delivery Format: Online
• Tuition: $19,950
• Duration: 6 months

BloomTech offers a data science bootcamp that covers a wide range of topics, including statistics, predictive modeling, data engineering, machine learning, and Python programming. BloomTech also offers a 4-week fellowship at a real company, which gives students the opportunity to gain work experience.

BloomTech has a strong job placement rate, with over 90% of graduates finding jobs within six months of completing the program. The program is expensive and requires a significant time commitment, but it is also very rewarding.

 

Here’s a guide to choosing the best data science bootcamp

 

What to expect in the best data science bootcamps?

A data science bootcamp is a short-term, intensive program that teaches you the fundamentals of data science. While the curriculum may be comprehensive, it cannot cover the entire field of data science.

Therefore, it is important to have realistic expectations about what you can learn in a bootcamp. Here are some of the things you can expect to learn in a data science bootcamp:

• Data science concepts: This includes topics such as statistics, machine learning, and data visualization.
• Hands-on projects: You will have the opportunity to work on real-world data science projects. This will give you the chance to apply what you have learned in the classroom.
• A portfolio: You will build a portfolio of your work, which you can use to demonstrate your skills to potential employers.
• Mentorship: You will have access to mentors who can help you with your studies and career development.
• Career services: Bootcamps typically offer career services, such as resume writing assistance and interview preparation.

Wrapping up

All and all, data science bootcamps can be a great way to learn the fundamentals of data science and gain the skills you need to launch a career in this field. If you are considering a boot camp, be sure to do your research and choose a program that is right for you.

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“Our data science bootcamp online offers the same comprehensive curriculum as our in-person program. Learn from industry experts and earn a certificate from the comfort of your own home. Enroll now!”

Data Science is one of the most in-demand skills in today’s job market, and for good reason. With the rise of big data and the increasing importance of data-driven decision-making, companies are looking for professionals who can help them make sense of all the information they collect. 

But what if you don’t live near one of our Data Science Dojo training centers, or you don’t have the time to attend classes in-person? No worries! Our data science bootcamp online offers the same comprehensive curriculum as our in-person program, so you can learn from industry experts and earn a certificate from the comfort of your own home. 

A glimpse into the Data Science Bootcamp Online by Data Science Dojo

Our online boot camp is designed to give you a solid foundation in data science, including programming languages like Python and R, statistical analysis, machine learning, and more. You’ll learn from real-world examples and work on projects that will help you apply what you’ve learned to your own job. 

1. Learn at your own pace

One of the great things about our online boot camp is that you can learn at your own pace. We understand that everyone has different learning styles and schedules, so we’ve designed our program to be flexible and accommodating. You can attend live online classes, watch recorded lectures, and work through the material on your own schedule. 

2. Mentorship and support for participants

Another great thing about our online bootcamp is the support you’ll receive from our instructors and community of fellow students. Our instructors are industry experts who have years of experience in data science, and they’re always available to answer your questions and help you with your projects. You’ll also have access to a community of other students who are also learning data science, so you can share tips and resources, and help each other out. 

3. Interactive course material

Our Data Science Dojo bootcamp is designed to provide a comprehensive and engaging learning experience for students of all levels. One of the unique aspects of our program is the diverse set of exercises that we offer.

These exercises are designed to be challenging, yet accessible to everyone, regardless of your prior experience with data science. This means that whether you’re a complete beginner or an experienced professional, you’ll be able to learn and grow as a data scientist. 

4. Participate in data science competitions

To keep you motivated during the bootcamp, we also include a Kaggle competition. Kaggle is a platform for data science competitions, and participating in one is a wonderful way to apply what you’ve learned, compete against other students, and see how you stack up against the competition. 

5. Instructor-led training

Another unique aspect of our bootcamp is the instructor-led training. Our instructors are industry experts with years of experience in data science, and they’ll be leading the classes and providing guidance and support throughout the program. They’ll be available to answer questions, provide feedback, and help you with your projects. 

6. Ask your queries during dedicated office hours

In addition to the instructor-led training, we also provide dedicated office hours. These are scheduled times when you can drop in and ask our instructors or TA’s any questions you may have or get help with specific exercises. This is a great opportunity to get personalized attention and support, and to make sure you’re on track with the program. 

7. Build a strong alumni network

Our bootcamp also provides a strong alumni network. Once you complete the program, you’ll be part of our alumni network, which is a community of other graduates who are also working in data science. This is a great way to stay connected and to continue learning and growing as a data scientist. 

8. Master your skills with live code environments

One of the most important aspects of our bootcamp is the live code environments within a browser. This allows participants to practice coding anytime and anywhere, which is crucial for mastering this skill. This means you can learn and practice on the go, or at any time that is convenient for you. 

Once you finish the bootcamp, you’ll still have access to post-bootcamp tutorials and publicly available datasets. This will allow you to continue learning, practicing and building your portfolio. Alongside that, you’ll have access to blogs and learning material that will help you stay up to date with the latest industry trends and best practices. 

Start your data science learning journey today!

Overall, our Data Science Dojo bootcamp is designed to provide a comprehensive, flexible and engaging learning experience. With a diverse set of exercises, a Kaggle competition, instructor-led training, dedicated office hours, strong alumni network, live code environments within a browser, post-bootcamp tutorials, publicly available datasets and blogs and learning material, we are confident that our program will help you master data science and take the first step towards a successful career in this field. 

At the end of the program, you’ll receive a certificate of completion, which will demonstrate to potential employers that you have the skills and knowledge they’re looking for in a data scientist. 

So, if you’re looking to master data science, but you don’t have the time or opportunity to attend classes in-person, our online data science bootcamp is the perfect solution. Learn from industry experts and earn a certificate from the comfort of your own home. Enroll now and take the first step towards a successful career in data science 

Bellevue, Washington (January 11, 2023) – The following statement was released today by Data Science Dojo, through its Marketing Manager Nathan Piccini, in response to questions about future in-person data science bootcamp: 

“They’re back.” 

-DSD- 

Nothing can compare to Michael Jordan’s announcement in 1995 that he was returning to the NBA, but for Data Science Dojo (DSD), this comes close.  

In 2020, we had to move our in-person Data Science Bootcamp curriculum to an online format. Doing this allowed us to continue teaching and helping working professionals grow their skill sets and careers. We will continue to provide all our courses in part-time, online formats, but we’re bringing back an old friend.  

We are excited to announce that we will be hosting our first in-person data science bootcamp (since 2020) this March in Seattle! If you joined Data Science Dojo’s community during or after the COVID pandemic, you may have some questions about how it works, whether can really learn data science in 5 days, why DSD is comparing itself to MJ…I can’t explain the part about MJ other than that I thought it would be fun, but I can explain how in-person bootcamps work at DSD.  

How it works  

In-person bootcamps at Data Science Dojo are a little different than what you’ve seen on the market. Typically, in-person data science bootcamps are full-time, multiple weeks (I’ve seen as many as 24), and cost you an arm and a leg.

Our in-person bootcamp cuts through the fluff so that you’re applying concepts and techniques back at work in only five days, rather than weeks, without sacrificing any limbs.  

• 5 days  
• 10 hours per day 
• Industry expert instructors 
• Hands-on, practical exercises 
• Post-bootcamp supplemental learning  

 

 

Similar to our online format, we provide pre-bootcamp coursework to help our students prepare. These tutorials include topics like R & Python programming, data mining, and Azure ML (Machine Learning). These are important for our students to complete to be successful during the bootcamp.  

 

Learn Data Science with a “Think-Business-First” Approach: Hands-on Activities and Real-World Applications in our Bootcamp Class

When the bootcamp starts, you’re in class! You’ll have live instructors and TAs working with you to help you learn these complex topics. During class, we use a mix of conceptual learning and hands-on activities to drive a “think-business-first” approach to data science and instill a foundation for critical thinking.

Our goal is that our students can immediately start applying what they learn in the real world, and we have a plethora of use cases, extra practice material, and live coding notebooks to ramp up our students’ abilities.  

After each class period, you will have homework to reinforce your learning and prepare you for the next day. You will also work on an in-class Kaggle competition to compete with your peers for prizes, but more importantly, bragging rights.  

At the end of the 5th day, you’ll graduate from the program and become a Data Science Dojo alum. You’ll receive a verified certificate in association with the University of New Mexico, be invited to join DSD’s alumni group and take your lessons back to work to start solving problems with a new data science skillset.

Just because the bootcamp ends, doesn’t mean your education does. We provide post-bootcamp tutorials for our alumni to continue their data science education.  These include topics on NLP (Natural Language Processing), neural networks, and other more advanced techniques we don’t have time to cover during the bootcamp.  

Get more information on our in-person data science bootcamp

This is a lot to learn in one blog post, and I’ve done my best to try to make it as simple as possible. If you’re interested in solving problems with data and want to attend a fast-paced, in-person program, I encourage you to schedule a call with one of Data Science Dojo’s advisors.

With our expert instructors, hands-on practical exercises, and post-bootcamp tutorials, you’ll be on your way to becoming a data science pro in no time. Don’t miss this opportunity to take your career to the next level! 

Get a behind-the-scenes look at Data Science Dojo’s intensive data science Bootcamp. Learn about the course curriculum, instructor quality, and overall experience in our comprehensive review.

“The more I learn, the more I realize what I don’t know”

(A quote by Raja Iqbal, CEO of DS-Dojo)

In our current era, the terms “AI”, “ML”, “analytics”–etc., are indeed THE “buzzwords” du jour. And yes, these interdisciplinary subjects/topics are **very** important, given our ever-increasing computing capabilities, big-data systems, etc. 

The problem, however, is that **very few** folks know how to teach these concepts! But to be fair, teaching in general–even for the easiest subjects–is hard. In any case, **this**–the ability to effectively teach the concepts of data-science–is the genius of DS-Dojo. Raja and his team make these concepts considerably easy to grasp and practice, giving students both a “big picture-,” as well as a minutiae-level understanding of many of the necessary details. 

Learn more about the Data Science Bootcamp course offered by Data Science Dojo

Still, a leery prospective student might wonder if the program is worth their time, effort, and financial resources. In the sections below, I attempt to address this concern, elaborating on some of the unique value propositions of DS-Dojo’s pedagogical methods.

The More Things Change…

Data Science enthusiasts today might not realize it, but many of the techniques–in their basic or other forms–have been around for decades. Thus, before diving into the details of data-science processes, students are reminded that long before the terms “big data,” AI/ML, and others became popularized, various industries had all utilized techniques similar to many of today’s data-science models. These include (among others): insurance, search engines, online shopping portals, and social networks. 

This exposure helps Data-Science Dojo students consider the numerous creative ways of gathering and using big data from various sources–i.e. directly from human activities or information, or from digital footprints or byproducts of our use of online technologies.

 

The Big Picture of the Data Science Bootcamp

As for the main curriculum contents, first, DS-Dojo students learn the basics of data exploration, processing/cleaning, and engineering. Students are also taught how to tell stories with data. After all, without predictive or prescriptive–and other–insights, big data is useless.

The bootcamp also stresses the importance of domain knowledge, and relatedly, an awareness of what precise data points should be sought and analyzed. DS-Dojo also trains students to critically assess: why, and how should we classify data. Students also learn the typical data-collection, processing, and analysis pipeline, i.e.:

1. Influx
2. Collection
3. Preprocessing
4. Transformation
5. Data-mining
6. And finally, interpretation and evaluation.

However, any aspiring (good) data scientist should disabuse themselves of the notion that the process doesn’t present challenges. Au contraire, there are numerous challenges; e.g. (among others):

1. Scalability
2. Dimensionality
3. Complex and heterogeneous data
4. Data quality
5. Data ownership and distribution, 
6. Privacy, 
7. Reaction time.

 

Deep dives

Following the above coverage of the craft’s introductory processes and challenges, DS-Dojo students are then led earnestly into the deeper ends of data-science characteristics and features. For instance, vis-a-vis predictive analytics, how should a data-scientist decide when to use unsupervised learning, versus supervised learning? Among other considerations, practitioners can decide using the criteria listed below.

 

Unsupervised Learning…Vs. … >>	<< …Vs. …Supervised Learning
>> Target values unknown	>> Targets known
>> Training data unlabeled	>> Data labeled
>> Goal: discover information hidden in the data	>> Goal: Find a way to map attributes to target value(s)
>> Clustering	>> Classification and regression

 

Read more about the supervised and unsupervised learning

 

Overall, the main domains covered by DS-Dojo’s data-science bootcamp curriculum are:

• An introduction/overview of the field, including the above-described “big picture,” as well as visualization, and an emphasis on story-telling–or, stated differently, the retrieval of actual/real insights from data;
• Overview of classification processes and tools
•  Applications of classification
• Unsupervised learning; 
• Regression;
• Special topics–e.g., text-analysis
• And “last but [certainly] not least,” big-data engineering and distribution systems. 

 

Method-/Tool-Abstraction

In addition to the above-described advantageous traits, data-science enthusiasts, aspirants, and practitioners who join this program will be pleasantly surprised with the bootcamp’s de-emphasis on specific tools/approaches.  In other words, instead of using doctrinaire approaches that favor only Python, R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at their disposal.

“Whoo-Hoo! Yes, I’m a Data Scientist!”

By the end of the bootcamp, students might be tempted to adopt the above stance–i.e., as stated above (as this section’s title/subheading). But as a proud alumnus of the program, I would cautiously respond: “Maybe!” And if you have indeed mastered the concepts and tools, congratulations!

But strive to remember that the most passionate data science practitioners possess a rather paradoxical trait: humility, and an openness to lifelong learning. As Raja Iqbal, CEO of DS-Dojo pointed out in one of the earlier lectures: The more I learn, the more I realize what I don’t know. Happy data-crunching!

 

 

Written by Seif Sekalala

Is there a relation between data science and law? Here’s what a lawyer learned from a 50-hour data science Bootcamp at Data Science Dojo.

With an increased focus on the growing role of data science and data analytics in the future of law, I decided that it was high time to learn what all the fuss is about.

How do data science and law work together?

Initially, I considered taking a course on data analytics geared for lawyers, but shockingly, I couldn’t find much, except a couple of classes that focused on e-discovery where predictive coding is a hot topic.  One new player in the legal data space,  LexPredict also offers  a bunch of training for lawyers, but the company seemed geared towards big law and in any event, didn’t list dates or prices for its classes

Unable to find data engineering classes for lawyers, I decided to get at the subject from another angle: start with the data science tech and work my way back to the law. That approach gave me a plethora of options, from low-cost classes at Udemy and Coursera to 12-week bootcamps costing $10k or more.

However, because I didn’t have the luxury of giving up my day job, I knew that I’d need a compact course since any program that dragged out over weeks or months increased the chances that I’d drop out once my caseload and client emergencies presented a conflict.

Likewise, given that I’d have to take time out of my practice for a class that would cause some financial loss, I didn’t want to shell out several thousand dollars for a class.

Based on my criteria, Data Science Dojo’s data science bootcamp fit the bill: it’s a reasonably priced 5-day, 50-hour onsite program that didn’t have any prerequisites (though there were about 10 hours of pre-class prep).

The class covered broad ground: in the week I learned coding tools like basic R, MS Azure, Hadoop, and Hive along with concepts like data mining and visualization, predictive modeling, Ensemble methods like bagging and boosting, random forests, the importance of cross-validation, the difference between training and test data, AB Testing basics, building a recommendation system and handling real-time and streaming data (we hacked a quick IoT solution using Azure tools, though truth be told, I was pretty much lost by then).

Below are some of my takeaways on big data, especially as it relates to the legal profession and what it’s like for a lawyer to learn a new skill at an advanced age.

Lesson 1

The mechanics of building a predictive model aren’t particularly difficult; understanding what features to include and how to approach the problem is – and that’s where domain knowledge is important.

One of the underlying themes of the class is that data science (itself a buzzword) is merely a collection of skills, intuition, and domain knowledge that matter as much as coding a predictive model.  Yet oddly, when data science is discussed in the legal profession, we downplay the importance of legal expertise and its value in creating effective models.

Predictive models are iterative and constant questioning is a good thing.

Although most lawyers will argue a legal principle ad nauseam, when it comes to data, we’re surprisingly passive.  For the past two years, Clio has released a Trends Report that produced interesting, albeit counter-intuitive results. Yet the results are reported as is, with no questions as to the methodologies used, what the data means, or how it was gathered.  That’s not true data science: it’s groupthink.

Big legal data Isn’t all that big

Our instructor shared with us the Five V’s — Volume, Velocity, Variety, Veracity, and Value – which are used to evaluate whether data rises to the level of big data. For volume, we’re talking about massive amounts of data – not terabytes, but exabytes and beyond – too large to be stored and processed on traditional machines.

For example, on Facebook, 10 billion messages are exchanged each day. It’s hard to imagine many sources of legal data that approach that volume. Our instructor’s point was that we shouldn’t make a data problem into a big data problem unless necessary. So, I wonder whether lawyers are using the term “big data” for small data or treating ordinary data problems as big data problems.

Kaggle competitions are way cool

I didn’t know much about Kaggle before my class. Although our involvement in Kaggle was limited to an in-class competition over who could build the most accurate model to predict survival on the Titanic, more broadly, Kaggle serves as a platform where companies can crowdsource the creation of data models.

Many of the contests attract large numbers of participants – because the sponsors pony up substantial cash prizes as an incentive. Lawyers are often criticized for not crowd-sourcing orb-sharing information like other professions — but I’ve not seen a single platform that offers any financial reward to lawyers for creating content that might be used as the equivalent of case notes.

If any of the companies adding blog content to supplement caselaw – as Fastcase in collaboration with Lexblog are doing now – offered a thousand-dollar award every week for best content, I think we’d see an explosion of high-quality crowd-sourced materials

All practicing lawyers, not just millennials, need to understand new technology

Most of the conversation about the importance of learning about big data, AI, or other new tools comes in the context of advice as to what millennials need to learn. But I think it’s even more important for us mid-career and older lawyers to keep pace with the future if we want to have control over how the last decade or two of our careers play out.

After 40 hours of bootcamp, I’ve had to catch up on client work – and I’m not sure how soon it will be before I can apply all the fancy new tricks and knowledge that I’ve learned.  For now, I’m satisfied that at least I’ve taken the first step.  When will you do the same?

 

 

Written by Carol Elefant