Despite having a PhD in astrophysics, Marcos Huerta, 43, based in Richmond, Virginia, found himself unemployed and struggling to find positions in his field. So he signed up for Pragmatic Institute's data science fellowship through The Data Incubator in fall 2018. Later that same year, he was hired as a data scientist at CarMax with a six-figure salary.

He told Insider the boot camp helped him quickly and successfully switch careers to tech.

"I was looking to broaden the positions and localities where I could find work," Huerta said. "Had the perfect noncoding job come up, I would have still considered it, but I wanted more options to find a stable job at a good company."

Huerta shared his story with Insider and advice for others looking to attend a boot camp.

After graduating grad school in 2007, Huerta worked in science policy for nine years, first through policy fellowships and then as an official in the Obama administration. He moved to Washington, DC, in 2008 and later worked for an office in the House of Representatives, eventually becoming an advisor at the US Department of Energy.

But the latter job was an appointee position that ended at the start of 2017, so he needed to come up with a new opportunity.

"While I found some short-term contract work, I was frustrated with my overall job search," Huerta said. "Data scientist was not really a job title when I left graduate school in 2007, but I started to think about it at the suggestion of my now father-in-law."

While he had some experience in data science from previous jobs, he lacked familiarity with new data-science tools. He taught himself the R language using free Johns Hopkins online classes and an R package called swirl.

Huerta then used R when applying to The Data Incubator since there are data analysis questions as part of the admissions process, but he was not initially selected for a fellowship. He applied again in the summer of 2018 and got in.

Huerta started the eight-week data science training course in September 2018 and completed it on November 2.

"CarMax called me and made me the offer on the very last day of the boot camp," he said. "We were having the end-of-boot camp celebration when I got the call."

Huerta was unemployed while attending the boot camp.

"We were told it was a full-time commitment and not to have other employment, but since I was between jobs, that was not an issue," he said.

Since Huerta was selected for a fellowship at The Data Institute, he didn't have to pay any tuition out of pocket for the training. His total cost for participating was around $150 for a computing resources fee.

Huerta said that the first week of the boot camp was "kind of overwhelming."

"The weekly mini-project was doing a lot of web scraping with Beautiful Soup a Python package and it was all new to me," he said. "Plus, I was also supposed to be thinking about job applications, my resume, my capstone project it was a lot."

The training's first weekly project turned out to be the hardest one, Huerta said. Once he completed that, the key challenges became time management and prioritization.

"The boot camp was a full day around eight hours a day from 9 a.m. to 5 p.m," Huerta said. "The amount of time I spent beyond that varied: The first week I probably put in an additional two to three hours a day. Subsequent weeks I could mostly get what I needed done from 9 to 5 or 9 to 6."

As the boot camp went on, the idea of learning something new each week became less intimidating, he said.

"The experience was the opposite. It became empowering as I realized, 'If I focus full time on a new data-science tool, in less than a week I can figure it out,'" he said. "Obviously, I didn't become an expert on what we were learning in just a week, but it still felt like I could learn the basics of anything quickly."

Huerta said when considering a boot camp or career change, try a free or inexpensive course first before diving into a big commitment.If you're still a student, he suggested taking machine-learning or data-science courses at your college.

Since doing a project will be a big component of any boot camp, he also said to give yourself a project first.

"I used several online sites and courses to teach myself Python and R before I even applied for the boot camp," Huerta said. "When I was teaching myself Python, I came up with a side project for myself to make my Raspberry Pi speak out the local weather and metro bus arrival times in the morning."

Finally, he said talk with people who have gone through the boot camp. Although he wasn't able to speak to an alum of the program, he said, he did try to cold email some people on LinkedIn, and a friend of a friend helped him connect with someone who had done a different boot camp.

"Alumni can give you their take about what they learned, the challenges, and the benefits," Huerta said.

More here:

How a Boot Camp Grad Went From Unemployed to 6 Figures in 2 Months - Business Insider

This is a guest blogpost by Emil Eifrem, co-founder and CEO at Neo4j. He writes on why he thinks graph technology is emerging as a powerful way to make AI a reality for the enterprise.

According to Gartner, by 2025 graph technology will be used in 80% of data and analytics innovations, up from 10% in 2021. The worlds largest IT research and advisory firm is also reporting that an amazing 50% of all inquiries it receives around AI and machine learning are about graph database technology, up from 5% in 2019.

Its a rise the firm attributes to the fact that graph relates to everything when it included graphs in its top 10 data and analytics technology trends for 2021.

Whats clear from these figures is that graph databases are an essential tool for developers, but also increasingly for data scientists. Google, shifted its machine learning over to graph several years ago, and now the enterprise is following.

From concept to concrete

I predict that within 5 years, machine learning applications that dont incorporate graph techniques will be the vanishingly small exception. Graphs unlock otherwise unattainable predictions based on relationships, the underpinnings of AI and machine learning. And thats why the enterprise is going all in on graphs and why Gartners phone keeps ringing!

Graph data science is essentially data science supercharged by a graph framework, which connects multiple layers of network structures. The graph-extended models predict behaviour better.

Graph databases are also the perfect way to bridge the conceptual and the very concrete. When we create machine learning systems, we want to represent the real world, often in great detail and in statistical and mathematical forms. But the real world is also connected to concepts that can be complex. Thats why graphs and AI go together so well, because youre analysing reams of data through deep, contextual queries.

Connections in data are exploding

Uptake on graphs is set to continue because data management is increasingly about connected use cases. After all, many of the best AI-graph commercial use cases didnt exist 20 years ago. You couldnt spotlight fraud rings using synthetic identities on mobile devices because none of those things existed. And yet theyre everywhere today.

Manufacturing companies would have a supply chain that was only two or three levels deep, which could be stored in a relational database. Fast forward to today, and any company that ships goods operates in a global, fine-grained supply chain mesh spanning continent to continent. In 2021, youre no longer talking about two or three hopsyoure talking about a supply chain representation that is 20-30 levels deep. In response, many of the worlds biggest and best businesses have discovered graphs as a great way to get visibility n levels deep into the supply chain to spot inefficiencies, single points of failure, and fragility. Only graph technology can digitise and operationalise it for that degree of connectedness at scale.

As global digitisation increasingly expands, the volume of connected data is expanding right along with it. Were also facing more and more complex problems, from climate change to financial corruption, and its going to continue. The good news is we now have graph technology to access more help from machines to face the challenging situations ahead.

Welcome to the world of real, practical enterprise AI at last.

More here:

Someone's calling about AI. Graph technology is ready to answer - ComputerWeekly.com

Peter Valverde pictured at his Cal Berkeley dorm hopes to find a summer internship in San Diego when he returns home this summer. (Courtesy photo)

In addition to celebrating holidays, December is the perfect time for students on their winter break to explore summer internships or job prospects. Alvarado Estates is home to students attending local colleges and universities, as well as institutions in other parts of California and across the country. No matter where they reside during the school year, most of them will be in the neighborhood for the summer months. As well as having fun with friends and sleeping in, most students will make time for personal and professional development, too.

Peter Valverde and his siblings grew up in Alvarado Estates. He is now a sophomore at the University of California, Berkeley. He plans to search for an internship when classes end next week.

I will be coming home for the summer vacation, but hopefully it wont be too much of a vacation, he said, adding he wants to look into data science internships, which would compliment his academic interests and career goals. I found that my data science tutoring job has helped me connect with people in the community and has motivated me toward finding and pursuing data science opportunities.

Peters older sister, Danielle Valverde, has already secured an internship. She is finishing a degree in Communication Studies at the University of San Diego. A family friend alerted her to an opportunity that sounded like a great match with her undergraduate degree and the Masters Degree she wants to earn in Human Resources and Leadership. After submitting a resume of her previous job experiences and educational accomplishments, she had a phone interview that resulted in an internship with the Office of Development and Foundations Executive Director and the Development Coordinator at Southwestern College.

Most colleges and universities have a program in place to assist students who want to bolster their academic studies with experiential learning. Schools know that an internship can help a student bridge their studies and the real world by gaining the skills to launch into a meaningful career.

Andre Frater helps students find out about opportunities on and off campus right here at San Diego State University Career Services. The programs website offers enrolled SDSU students the chance to set up an in-person or virtual one-on-one career advising appointment to define, develop and realize their career potential. Students learn tips to produce a resume, write cover letters and prepare for an interview. How to Make a Good First Impression on a Virtual Interview, is one of many videos produced by recruiting sites and available as direct links from the SDSU Career Services website.

Andre encourages students to use online platforms like Handshake to find internships. Handshake is a relatively new online recruiting site for higher education students and recent alumni. The app helps to streamline the process by connecting students with open positions, such as internships and entry-level jobs all over the country.

The Handshake website states that there are, more than nine million active student users, more than 1,400 college and university partners, and more than 600k active employers, including 100% of the Fortune 500 companies. You can learn more about the app by going to joinhandshake.com. The website instructions seem simple: Download the Handshake app and sign-up with your college and student account, create a personal profile and get personalized job recommendations, connect with employers to learn about company culture and open roles, and apply and get the job!

In addition to Handshake, try Indeed.com, LinkedIn, Facebook or networking with friends and neighbors to secure that invaluable summer experience.

If you have the ability to provide an internship opportunity for students, sign up as an employer on Handshake or reach out to Andre Frater and the Career Resources Team at SDSU by calling 619-594-6509.

Karen Austin writes on behalf of the Alvarado Estates Association.

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'Tis the season to plan a summer internship - In addition to celebrating holidays December is the perfect time for students on their winter break to...

Method of teaching has been a subject of discussion and debate for a long time. The effectiveness of training and skilling has been questioned and deliberated time and again globally, irrespective of the field, stream, sector, or specialisation.

The education sector, of late, has been witnessing a move away from traditional teaching techniques. Rote learning is slowly becoming expendable, especially in practical fields like analytics and data science.

The pace at which these fields are evolving coupled with the rapidly increasing demand globally across all industry verticals has created a significant gap in the right talent supply with the skillset to apply themselves for a given business context and create impact.

While the academic institutes, MOOCs, and the likes are doing a tremendous job in creating awareness and equipping the talent with theoretical concepts and knowledge, there is a gap that is widening around enabling the talent with the right experience to be impactful on-the-job quickly. The rising attrition of experienced talent is adding to the pressure on the system.

There is no doubt that theoretical learning is foundational for analysts and data scientists, but the work entails individuals to critically understand business problems and create innovative solutions. This demands them to be continuous prolific learners, creative thinkers, and quick problem solvers. The way to achieve these desired qualities is through learning by experience.

The learning-by-doing method allows learners to be engaged and actively participate in the learning process by working and reflecting on the projects done. This form of learning is proving to be the most effective in becoming successful in the analytics and data science landscape. We will discuss how one can and should upskill oneself through experiential learning in analytics and data science.

Before delving into the essence of experiential learning, there are two fundamental concepts that every aspiring analyst and data scientist must ingrain themselves with to become successful in the field.

1. Do not believe data without reasoning

Data is the basis for your trendsetting, analysis, prediction, and business solutions. If the data is faulty, the entire project will fail. One must question the existence of the data and reason with the data to ensure its validity and quality before moving on to any other step. For instance, last year, Italy had the highest number of COVID-19 deaths at one point. But a part of this situation owed itself to every death in an Italian COVID-19 hospital being counted as a COVID-19 death, regardless of the real reason. If one were to base ones predictions and trends on just the former statement, the results would be faulty.

2. Do not arrive at conclusions without critically examining the data

Complementing data reasoning this step entails examining the data and its correlation to causation. Go a step further into ensuring that the claims made by the data are backed by facts and information. For instance, citizens in the UK shop more during winter than summer. At face value, this proves seasonal consumer preferences, but in reality, winter coincides with Christmas and New Year sales, pushing customers to go on shopping sprees. Basing your analysis on the first statement would lead to an incorrect business solution.

The most fundamental aspect at which all three streams of analytics, descriptive, predictive, or prescriptive are built on, is the clarity around correlation v/s causation. Several of the analytics and data science applications fail to address business problems due to a lack of critical examination leading to the faulty judgment of interchanging correlation with causation and vice versa.

The methods of teaching and learning are undergoing a significant change in the modern era. The traditional classroom approach, based on the foundations of listening to lectures and reading out of textbooks, is not proving to be successful in readying professionals for todays workplaces. An increasing number of researchers with empirical pieces of evidence is proving the advantage of experiential learning on learners over conventional methods.

Setting the foundation for todays classrooms, Edgar Dales Cone of Experience, or his Learning Pyramid (1940), illustrates how the depth of a persons understanding depends on the medium leveraged and the senses involved in the learning process. Dales research identifies that direct, purposeful, or on-field experiences are the most effective method, resulting in 90 per cent retention of the information. In contrast, it revealed the least effective learning method through presented information like verbal and visual symbols.

As Dale explains, people learn best when they are present in action and learn from their experience. In the world of data science, opening up the learners sensory channels to interact with the information at hand is bound to produce better results. Moreover, analytics and data science are practical fields, entailing practitioners to work on models, deal with data, and make engineering decisions. For instance, a data scientist cannot learn a hackathon solution without brainstorming the possibilities or building an intelligent model right from the textbook.

Experiential learning methodologies and their effectiveness can be illustrated through the essential skills under the hard-skill and soft-skill umbrella in the analytics and data science space. While hard skills provide a foundation for all solutions, soft skills help in creating innovative ideas and communicating them. A nurtured combination of the two is what sets apart a data scientist from their peers.

The need for practitioners to be skilled in the textbook technical concepts to ensure that the best possible analytical approach and models are built, while is necessary, is not sufficient. They need to be seasoned in applying the concepts in real-life problem situations.

The way to develop application-oriented hard skills is to focus on three essential components.

1. Applied knowledge of algorithms

While one may have mastered algorithms, it is essential to know how and when to apply them. There may be instances when one comes across a problem where conventional algorithms dont work. One will need to be fluent in writing a new/heuristic algorithm or creative in tweaking the old ones. Applied knowledge is learned from experience, so one must practice applying oneself in the right way.

2. Translation for business context

Data scientists often work with non-tech-based business professionals to find solutions to business problems or to create incremental business impact. It is paramount for them to understand the business context and translate those to data analytics problems, followed by building the right solution to map the context for timely implementation. This process also requires translating back the solution to business stakeholders in a language that they can comprehend. This is critical not only for a successful implementation of analytical solutions but to also set the stage for continuous improvement for incremental impact. Contextualisation leads to the adoption and growth of data-driven culture within organisations. The skills acquired by one through the experiential learning approach can help with the above endeavour.

3. Programming skills in Python or R

Python or R can handle applications from data mining and ML algorithms to running embedded applications under one unified language. Data scientists need to be skilled in one or both programming languages to be successful in the field. The application-oriented case study-based approaches enabled through experiential learning methodologies enable one towards industry readiness with this skill.

LinkedIns Future of Skills report from 2019 that studied behavioural insights based on millions of data points from member engagement identified soft skills to have increased value in enterprises. This, they reported, is given the expanding application of new technology that is broadening the job expectations for data scientists. The data science industry focuses majorly on hard skills, but it is time we lay enough importance on developing soft skills as well. There are three soft skills that are most important for a data scientist to nurture.

1. Critical thinking & problem-solving

Critical thinking and problem-solving skills assist data scientists in clarifying vague and broad problems. If the dataset has errors or is not understood correctly, the solution will be unsuccessful. Under the experiential learning framework, one can build these skills by participating in hackathons, building models for experimentation, or engaging with data.

2. Effective communication

Once one has solved the problem, it is important to communicate it to the stakeholders effectively. Data scientists inability to communicate with stakeholders is a pressing concern within the industry. If the receiver does not understand the solution, it will not be implemented. Individuals can hone this skill by putting themselves out there, explaining solutions to non-technical people, receiving feedback on it, and working on enhancing the skill with more practice.

3. Agility & flexibility

Agility and flexibility are two skills that are increasingly becoming more important. The agile approach to working empowers data scientists to prioritise and create roadmaps based on business needs and adapt to different goals. Agile individuals are always learning and growing from new practical experiences.

In summary, experiential learning is learning by doing with application orientation and contextualisation. The framework is poised to get wide adoption in the field of analytics and data science globally across enterprises, functions, and academia. The aspirants and practitioners in the field should benefit from the framework to be continuous and prolific learners to upskill themselves in the most effective way and be future-ready.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the formhere.

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Council Post: Experiential LearningAn Essence To Address The Skill Gap In The Field Of Analytics And Data Science - Analytics India Magazine