Data are becoming the new raw material of business
The Economist

The 3 Things That Make Technical Training Worthwhile

seminar-594125_960_720Managers understand that having employees who understand the latest tools and technologies is vital to keeping a company competitive. But training employees on those tools and technologies can be a costly endeavor (US corporations spent $87.6 billion on training expenditures in 2018) and too often training simply doesn’t achieve the objective of giving employees the skills they need.

At The Data Incubator, we work with hundreds of clients who hire PhD data scientists from our Fellowship program or enroll their employees in our big data corporate training. We’ve found in our work with these companies across industries that technical training often lacks three important things: hands-on practice, accountability, and breathing room. Continue reading


The Data Incubator Is Now Part Of Pragmatic Institute!

Big Changes for The Data Incubator

At The Data Incubator, we aim to be at the forefront of data science training. We stay on top of the news (and make it when we can). We ensure our students have access to all the latest tips, tricks and toys. And when we see an opportunity to improve our training opportunities, we jump at it.

That’s why we’re pleased to announce that The Data Incubator is now a part of Pragmatic Institute!.

 

So what does that mean?

The Data Incubator (TDI), the leading provider of data science education, has joined with Pragmatic Marketing, the authority on product management and marketing training, to become a single source for comprehensive business training for companies — Pragmatic Institute.

Don’t worry, TDI will continue to provide private corporate training, our fellowship program, placement services and online training for data scientists. But with Pragmatic Institute, we will also be offering a new live, in person three-day course to teach business leaders how to integrate data science into their business models. And we’ll get to expand our hands-on training for data scientists across the country — and beyond!

And in the near future, data scientists will be able to add the soon-to-be-coveted Pragmatic Data Certification to their credentials as we continue to roll out new courses and training opportunities.

 

What’s changing?

With this new partnership, we’ve got a lot of exciting things happening. As part of Pragmatic Institute, we’ll be able to provide more training on a broader spectrum of topics to more current and future data scientists, starting with The Business of Data Science.

The Business of Data Science is a 3-day course focused on the principles of data science, identifying the optimal (and most profitable) ways to use data science in you business, and integrating it into your business. If you want to learn how to make better business decisions using data, want to better lead your data science team or simply want to understand data science better, then this course is for you.

This new course will debut in late April, 2019. But it isn’t the only new course we’re offering. Make sure you subscribe today to stay in-the-know on all the great data science courses we’ll be rolling out this year.

 

What’s staying the same?

For the most part, everything you’ve come to know and love about TDI is staying the same. There’s just going to be more of it.

We’ll still be offering our incredibly popular, incredibly competitive Fellowship program, which will continue to provide the hands-on, real-world data science training top companies are looking for. And we’ll continue our placement services to pair the right data scientist with the right position in healthcare, finance, tech and more. Both of these will continue to function under the TDI name.

Our online courses will still provide the same great information from the same great TDI data science instructors. And we’ll still offer onsite training for your employees so your entire team is on the same page.

So most everything will still be the same. But now, as part of the Pragmatic Institute brand, we’ll be able to provide more of it on a larger scale, so we can help meet the growing demand for qualified, dedicated data scientists the world needs.

 

Visit our website to learn more about our offerings:

 


Data Science Bootcamps – How To Avoid College Debt and Still Be Successful

By Corinne Spears and Ryan Craig

There is no doubt that 2018 was an innovative year. 3D metal printing became more mainstream, altering the way many think about the future of manufacturing. AR went from a vague promise to tangible developments in software and hardware. In medicine, researchers created a cancer-detecting liquid and developed AI that detects Alzheimer’s six years before human physicians.

One thing that did not change in 2018 was higher education. Colleges and universities continue to look like they did in the 60s, only with higher tuition and worse student outcomes. If the music business worked like higher education, we’d still be listening to music on 8-track tapes: the Walkman wouldn’t be invented yet, let alone streaming on noise cancelling, wireless headphones.
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How The Internet of Things is Revolutionizing Data

With the evolution of sensor, mobile, and wireless technologies, there’s no doubt that the Internet of Things (IoT) is slowly rising to the apex of modern technology. The true use of IoT, however, lies in the world of analytics – rather than hardware innovations. For businesses, IoT analytics are of crucial importance. Extracting rich insights from IoT-related consumer products helps businesses alter their strategies or better understand the consumer experience.

 

What’s so great about IoT analytics?

The first thing to realize about IoT data analytics is that it involves the use of sensors for data collection. Sensors are really cheap these days and are sophisticated enough to support a variety of use cases. Data analysis and data pattern recognition are important analysis tools that are used after the sensors have done their part in providing the raw data. Now, the ultimate goal behind IoT is not just fancy data. The goal is the usage of said data to understand people.

 
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Telling Truth from Hype When Hunting for Data Science Work

There’s a lot of talk these days about “fake news”, and for good reason. But the growing uneasiness about relying on information from the web has crept beyond politics into other areas, including the search for employment. Scams and fake offers are an unfortunate reality of online job searches across industries.

The data science world in particular has been swirling with skepticism, not just over whether job offers are legitimate, but about the job title itself—that is, over the tendency for some tech professionals to falsely label themselves as data scientists in the first place.

If you’d like to grab some tips for telling the scams, cons, and poppycock from real and worthwhile job opportunities in data science, read on.
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The Future of Management: Human Resource Analytics

Data science has marked its presence as the beautiful soul of the digital world and has crept across the multiple phases of the organizations driving their business growth. These days, small to large organizations are leveraging this technology to understand their consumers and business needs. This growing technology is making its way into the HR department so that organizations can use their data to improve engagement, performance and decision making.

A survey report by Global Recruiting Trends 2017 reveals that the 83 percent of Human Resource professionals think that talent is the biggest priority of an an organisation. The future of recruitment is 34% dependent upon the innovative interviewing tools.

 

Benefits Analysis:

One of the very effective uses of data science can be observed in the insights collected from the employee’s data. It uncovers the answer to typical queries like employees benefits, their opinion, costs optimization and analysis. Here, data science can predict whether the deal is good or not for the candidates and organization will face loss or profit. They can also put data science to manage the plans like gym memberships, health insurance, travel, food, funds, assessment, training, events, seminars, counselling etc. By analyzing the benefits and outcome of such events, organizations can make better decisions and achieve their objectives.

Predictive analysis deals with statistics and mathematical data. It ensures the effective future plan on the basis of the collected data. PA visualizes the employee’s data in the form of the graphs, states, and many more interesting methods providing a clear pictorial view. Thus, HR can easily deal with the critical scenarios like pay gap, bonus, resign, new batch recruitment etc. To deeply understand the concept and internal working of such methodologies, go for Data Scientist Course and be the part of this technological trend.

Talent Analytics:

Recruitment of the right candidate is the first priority on the HR lists. No matter whether your company is small or large, you need skilled workers to drive companies growth. Since companies are recognizing more, data science is helping them to find good candidates in an effective way.

The data science helps them make a congenial strategy for recruiting candidate by reducing unfair or other bias. The data collected from the analytics help recruiters to gather candidate’s performance data and make predictions about which candidate fits best for the particular job profile.

It allows organizations to channelize best fit talents, manage training campaigns, monitor turnover and create strategic plans for retention and recruitment. As seeking the best employee for the job is the toughest part of the HR recruitment journey. Around 38 per cent of the organizations are leveraging smart tools for the interview.

Work Analytics:

To understand the main requirements of the organization, data science is the best option. It deals with shape, experience, variety, knowledge and other attributes to boost up the companies efficiency. It assists them to increase their throughput and achieve future success. Some companies may employ a Quality Management System Software to help with work analytics.

Since recruitment plays a crucial role in companies growth until the organisation doesn’t have the right candidate for the right position. Here, data science can be very useful.

Data science not only allows to get accurate employees’ experience data, but it also observes behavioural patterns. It recognizes observing employee pregnancy, work affection etc.

The Ugandan governement has already implemented data science to monitor various public undertaking projects across the region in 2016. They use analytics to examine the quality of the services provided to the public. So they can monitor who is doing their job correctly and best.

Understanding employees performance will make management to decide promotions. Because choosing the best manager is not an easy task as it is not only decided on the basis of top performance. Here predictive analysis decides which selection is best for which job. The data collected from the manager and whole team defines which employee is more likely to achieve manager post and which one is not.

Final Words to Take Home:

A report by IBM and MIT concluded that the companies that are using HR analytics in their culture have more positive outcomes in their business. These organizations achieve 8 per cent increase in the sale and a 24 per cent increase in their overall revenue. Thus, you must have understood the need and impact of HR analytics in an organization. Don’t get too late, pace up your speed with the technology, then only you can achieve desired results.

Author:
Sanjay Kumar is B2B digital marketing doyen, with close to 5 years of experience in web marketing, project management, and business development. Mr. Sanjay has innovated, erected and managed e-marketing campaign for various organizations with his awe-inspiring analytical and practical approach driving fuel to the business growth.


The Benefits of Active Learning for Data Science Skills

Since the late 1970s, educators have promoted the adoption of active learning principles in teaching practices. Active learning is a method that involves students directly in the learning process. This contrasts with traditional learning methods, like lectures, where students passively receive information without taking measures to engage with the material and ensure they have sufficiently understood it. Active learning involves getting students to do activities and to think about the purpose behind these activities[1]. At The Data Incubator, we believe that active learning is the best way to approach data science training and education, and we’ve built our curricula based on these concepts. Below we discuss the benefits of active learning and how we’ve employed active learning methods in our data science training programs.

In the seminal text “A Taxonomy of Educational Objectives”[2], Bloom defines the six learning objectives of cognitive domain, the area of mental skill acquisition.[3] They are:
 

 
Effective learning in the cognitive domain is achieved by activating all of these objectives. Everyone learns differently; you often hear people state “I’m a visual learner” or “I’m an auditory learner”. The problem with this thinking is that it over simplifies how people learn into only two categories. Additionally, it adheres to the traditional passive learning techniques that rely on audio and visual cues only. With regards to the six objectives listed above, passive learning only addresses the “low order” thinking skills, remembering and understanding,without activating the “high order” thinking skills . During a one or two hour lecture, there’s no opportunity for students to effectively apply and analyze the information – let alone to evaluate or create something from the new information. While students may do so on their own time after lecture, the opportunity is lost to solidify the concepts when information has been most recently seen.

Critics of active learning often decry it as just another fad. However, numerous research studies have refuted this claim. A review of active learning studies found support for various forms of active learning[4]. Given the various studies analyzed in the review, the author suggests that introducing activities during lecture and promoting student engagement will improve learning outcomes.

The success of active learning has led institutions of higher learning to implement active learning principles. For example, MIT has replaced their traditional passive learning introductory physics classes with what they refer to as TEAL, Technology-Enabled Active Learning. These changes were prompted by low lecture attendance and high failure rate in the previous traditional lecture style courses. A study on TEAL performance reveals improvements in conceptual understanding, class attendance, and passing rate[5]. The study shows the failure rate dropped from 13% to 5% and lecture attendance increased from 50% to 80%, compared to a control group.

 

Active Learning at The Data Incubator

We understand the benefits of active learning, and we built our curricula based on the evidence that active learning supports better outcomes for students than passive learning. Our data science training programs include various features that promote active learning.

Interactive Lectures: Lectures are presented via an interactive learning environment, where students can follow along and interact with the material on their own device. Students are encouraged to experiment with the variables in real time during lectures, to see how they affect results. Additionally, we demonstrate concepts using interactive figures and plots, allowing students to study the effect of changing parameters. One example lecture activity would be to visualize the effect on performance of a machine learning model by adjusting a hyperparameter. Students can engage with the visualization and confirm the effect we’re discussing in the lecture. Students are no longer merely remembering a fact, they’re analyzing and applying the concepts to actively engage in the learning process.

Flexible Format: Additionally, we avoid long lecture formats to encourage active learning- for this reason, a typical day of data science training will involve frequent breaks from lecture. During these breaks, students work on small exercises that reinforce the concepts that was just discussed. Breaks from lecture are important because people have limited attention spans. Additionally, they ensure students have a chance to employ “high order” thinking skills to essential concepts before moving on to more advanced material. If there’s not enough time to apply and analyze the material, students will not be able to effectively learn new material presented.

Real-world Miniprojects: We include a miniproject as part of each teaching module we create. Miniprojects help students to meet all of the learning objectives outlined by Bloom’s taxonomy by having students start applying the information they’ve just learned on a real-world problem, using real-world data. Students are challenged go beyond remembering and understanding material, to exercise “high order” thinking skills by evaluating lecture material against practical examples and creating solutions with hands-on practice. For example, students will evaluate different machine learning models to determine not only which approach would be best for a given application, but also what makes it better than other models in that particular instance.

Group Learning: Students are encouraged to work in groups; not only does this prevent students from falling behind (by externalizing accountability and encouraging collaboration), it enables them to exercise the “high order” thinking skills required to meet those learning objectives. Peer-to-peer engagement helps build confidence in students by creating more opportunities for reinforcing the course material. When reviewing or explaining a piece of information to a fellow student, that student is engaged in applying, analyzing, evaluating, and creating information based on the course material.

Active learning has been extensively explored and advocated by teaching experts because of the vast amount of benefits it realizes over passive learning. It helps to maintain student concentration and deepens learning towards the “high order” thinking skills. It also helps to engage students who might otherwise struggle. Active learning is the guiding principle behind the creation of all of the data science training curricula at The Data Incubator because of these proven benefits. Data science is not a spectator sport – it requires engagement with the material to master data science skills.

 

References

1.) Bonwell, Charles C., and James A. Eison. (1991). Active Learning: Creating Excitement in the Classroom. ASHE-ERIC Higher Education Report No. 1. Washington D.C.: The George Washington University, School of Education and Human Development.
2.) Bloom, Benjamin Samuel. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.
3.) Anderson, Lorin W.; Krathwohl, David R., eds. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn and Bacon.
4.) Prince, Michael. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education. 93. 223-231.
5.) Dori, Yehudit & Belcher, John. (2005). How Does Technology-Enabled Active Learning Affect Undergraduate Students’ Understanding of Electromagnetism Concepts?. Journal of the Learning Sciences. 14(2), 243-279.

 

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Here’s How to Survive the Rise of A.I. – Become a Data Facilitator

Front office jobs at investment banks are increasingly being taken over by intelligent machines. Many current front office employees are worried about being displaced by artificial intelligence, and their fears are not unfounded. Huy Nguyen Trieu, former head of macro structuring at Citigroup, has a positive message for traders who risk being replaced by automation: become a data facilitator.

 

Huy Nguyen Trieu left Citi in 2016. After 13 years in financial engineering at SocGen and RBS, before becoming an Managing Director and head of macro structuring at Citi, he has shifted his focus to acting as a thought leader in the fintech space. Currently a fintech fellow at London’s Imperial College and mentor at fintech accelerator, Level 39, Nguyen Trieu is both fintech guru and entrepreneur. And his current focus is centered on the issue of long term employability in investment banks.

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Announcement – The Data Incubator Partnership with MRI Network

Today, we’re excited to announce …

The Data Incubator recently teamed up with MRI Network to increase its access to hiring partnerships worldwide. MRINetwork is comprised of over 1,500 search professionals who specialize in hundreds of industries, many of who came from the industries in which they now recruit. MRI recruiters combine their in depth understanding of industry; with the knowledge of who is who in almost any discipline in order to jump start the search for an enterprise’s next impact player or an entire division.

The addition of MRI Network, and its network of existing clients, will add thousands of hiring partners on top of TDI’s existing 300+ hiring partnerships. As the need for data scientists has increased exponentially over the past few years, MRI provides TDI students with immediate access to new data science positions in geographies worldwide, as well as greater access to companies with a fundamental need for the data science talent required to harness the power of their data.

TDI’s partnership with MRI represents a key inflection point in the already rapid growth of our company. TDI will be increasing the size of its fellowship program, as well as its online programs, to ensure that we can keep up with the needs of one of the world’s premier staffing solutions provider in MRI. Placement services will now be open to those participating in our Data Science Foundations and Machine Learning online programs. If you are interested in a career in data science, please apply for either for one of our programs today.
 

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Data Scientist Salaries

money-548948_960_720At The Data Incubator we’ve worked with hundreds of Fellows looking to enter industry and our alumni work at companies including LinkedIn, Palantir, Amazon, Capital One, and the NYTimes.  

Starting salary is one of the most common concerns for professionals entering any field, but as we’ve only been using the job title “Data Scientist” for about eight years it can be particularly challenging for prospective data scientists to find good information on their job market. LinkedIn and Facebook were the first to give employees on their data teams the title of data scientist, but now there are thousands of data scientists working across all industries alongside data engineers, data analysts, and quantitative analysts.

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