data science bootcamp

In-person data science bootcamps are returning to Data Science Dojo
Nathan Piccini
| January 20, 2023

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 bootcamps: 

“They’re back.” 

-DSD- 

(more…)

Unleash the power of Data Science: A comprehensive review of Data Science Dojo’s Bootcamp
Seif Sekalala
| January 6, 2023

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.

Data Science Bootcamp Review - Data Science Dojo
Data Science Bootcamp Review – Data Science Dojo

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, or R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at our 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!

 

register now

Carol Elefant
| October 27, 2017

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, with the exception of 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 trainings 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 a period of 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 thousands of 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 pre-requisites (though there was about 10 hours of pre-class prep).  And the class covered broad ground: in a span of the week I learned both the 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 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 principal 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: its group think.

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 crowd-source creation of data models. Many of the contests attract large numbers of participants – because the sponsors pony up substantial cash prizes as 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 or 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 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 50 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?

Data Science Dojo
| July 1, 2019

Data Science Dojo has been endorsed by the Singapore program, CITREP. Learn more about the program and its value in the market. 

Learning data science has now become easier and more accessible to Singaporeans.

CITREP initiative

Data Science Dojo (DSD) is now part of the SkillsFuture program in Singapore! The top-rated data science course was recently endorsed by CITREP+ (also known as just CITREP), part of the TechSkills Accelerator (TeSA) initiative. While citizens and permanent residents (PR) of Singapore may be fully aware of what SkillsFuture, CITREP+, and TeSA initiatives are, any non-Singaporean might be wondering what this means. Here’s a quick explanation.

 

TechSkills_Accelerator
TechSkills Accelerator Logo
infocom media development authority
Infocomm Media Development Authority Logo

The government in Singapore has made it it’s mission to provide upskilling opportunities to their citizens and PRs. SkillsFuture and CITREP are a national movement provided to Singaporeans to develop their technical skills. Any citizen or PR of Singapore may participate in the skill-up program, regardless of their current lifestyle. The emphasis of CITREP+ is to help the information and communication technologies (ICT) workforce keep up with training and technical skillsets that are continuously shifting in order to remain relevant and productive. CITREP realigned itself in 2016 to also include entry-level professionals the opportunity to build these technical skills.

“Skills mastery is more than having the right paper qualifications and being good at what you do currently; it is a mindset of continually striving towards greater excellence through knowledge, application and experience.” – SkillsFuture

Data Science Dojo will continue to offer their bootcamp in Singapore as they have in the past, open to all attendees. This program provides a new package option for Singaporeans who wish to apply for CITREP+ funding, potentially reducing financial barriers target=”_blank” to their data science training.

“It really opens the door for us to bring data science to everyone,” Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, replied. “We’re excited to build a relationship with Singaporeans and help advance their careers.”

What does this mean?

As per IMDA’s website, this program is for eligible Singaporeans, PRs, and companies looking to upskill and improve their ICT skillset.

For Singapore citizens and permanent residents, you’ll be able to attend data science trainings while possibly obtaining support. Companies may also receive assistance. Take a look at the Eligibility Criteria Chart to see if you qualify.

elegibility requirments text
Eligibility Criteria for CITREP (Source: IMDA)

Please note: Data Science Dojo does not know how much funding you will receive after completing the bootcamp. Funding depends on multiple factors shown in the chart above, and is ultimately decided by IMDA and the Singaporean Government.

What this means for Data Science Dojo

Its intensive, in-person, data science bootcamp will be offered in Singapore more frequently than in the past. The education company will be more accessible to Singapore citizens and PRs as the CITREP+ program offers visibility to those looking to upskill their talent(s).

About the Bootcamp

Data Science Dojo’s training is 5 days, in-person, and top-rated by CourseReport and Switchup. During the course, you will learn everything from predictive analytics and ensemble methods to recommender systems and the fundamentals of big data engineering.

Logo
Best Data Science Bootcamp from Switchup

The instructors have trained more than 4,000 individuals from nearly 1,000 different companies. The attendees come from diverse backgrounds, including software development, management consulting, medicine, education, project management, public service, target=”_blank” finance, non-profit, mining, oil and gas, and more.

Although there is no promise you will acquire a job after the training, but you get the opportunity to network and connect with alumni in the LinkedIn Alumni Group and with your bootcamp cohort at the Networking Dinner.

If you’re a citizen or permanent resident of Singapore and would like to learn more about SkillsFuture and CITREP+, check out their website at https://www.imda.gov.sg/imtalent/programmes/citrep-plus.

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