microsoft fabric-min

Posted on November 16, 2023 by Andrew (Sal) Salazar .

Data-Streams-Flowing-into-A-Single-Hub
 
In the ever-evolving landscape of data analytics, the introduction of Microsoft Fabric marks a significant leap forward. This innovative platform is redefining how businesses approach data analytics and integration in the era of AI. Here’s an in-depth look at the numerous benefits of Microsoft Fabric and how it’s changing the way businesses are able to work with and use their data.

1. Unified Data Analytics Solution

Microsoft Fabric presents a comprehensive analytics solution, consolidating data processes into a single, integrated system. This includes data movement, storage in data lakes, data engineering, and integration. By uniting these elements, Microsoft Fabric addresses the complexities often found in big data management, making it easier for businesses to handle vast amounts of information efficiently. This consolidation fosters a more streamlined workflow, leading to better data management and analysis, and ultimately driving informed business decisions.

2. Empowerment Across the Board

One of the core strengths of Microsoft Fabric is its accessibility to all levels of users. Whether you are a novice or a seasoned data professional, the platform’s intuitive design and robust features enable everyone to leverage its capabilities effectively, fostering a data-driven culture within organizations.

Image of digital brain hovering over a mobile phone.

3. AI and Machine Learning Integration

Central to Microsoft Fabric is its seamless integration with AI and machine learning. This allows for sophisticated analytics, providing predictive insights that are crucial for strategic decision-making. By leveraging AI and machine learning, businesses can anticipate market trends, customer behavior, and operational inefficiencies, enabling proactive measures and strategies. This forward-thinking approach not only enhances current operations but also positions companies to adapt quickly to future challenges and opportunities.

4. Enhanced Business Intelligence

Microsoft Fabric excels in managing and analyzing data, which significantly streamlines business intelligence tasks. The platform’s efficiency in processing and interpreting data leads to quicker, more accurate insights. This rapid access to vital information can transform strategic planning and execution, allowing businesses to make well-informed decisions swiftly. Resulting in a competitive advantage and market leadership.

5. Scalability and Flexibility

Designed with various business sizes in mind, Microsoft Fabric’s architecture offers scalability and flexibility. This ensures that as businesses expand, their data analytics capabilities can seamlessly grow alongside them. This adaptability means companies can avoid extensive system overhauls or significant new investments in data infrastructure, making growth more manageable and less disruptive.

data infrastructure

6. Reducing Delivery Time

With Microsoft Fabric, the time taken from data acquisition to actionable insights is greatly reduced. This efficiency is crucial in today’s fast-paced business environment where quick decision-making can be a competitive advantage.

7. Comprehensive Data Warehousing

Finally, Microsoft Fabric shines in data warehousing. Its robust solutions for storing and retrieving large volumes of data are instrumental in forming a solid foundation for any data-driven business. This comprehensive approach to data warehousing ensures that all data is not only securely stored but also easily accessible for analysis, providing a dependable and efficient backbone for all data-related activities.

8. Supporting Data Culture

Microsoft Fabric plays a pivotal role in promoting a data culture within organizations. By making data analytics accessible and efficient, it encourages a more data-centric approach in all business operations. Allowing various departments and teams to seamlessly work together with the same data in different ways.

Your Path to Data Mastery with Colaberry and Microsoft Fabric

Microsoft Fabric stands as a cornerstone in the modern data landscape, offering unparalleled advantages to businesses seeking to harness the power of their data. The platform’s capabilities in integrating AI, machine learning, and comprehensive data solutions are transforming the way organizations approach data science & analytics.

infographic of Colaberry's solutions stack

However, understanding and implementing these sophisticated technologies can require expertise and guidance. This is where Colaberry steps in. As a Microsoft Partner with a deep understanding of Microsoft Fabric, we at Colaberry are uniquely positioned to help your organization unlock the full potential of this powerful tool. Whether you’re looking to integrate AI into your data strategy, streamline your analytics processes, or simply seek advice on how to navigate the complexities of data science, our team of experts is here to assist.

Together, we can turn data into one of your greatest assets. Reach out to us now, and let’s start a conversation about your data needs and how we can meet them.

 

 
 

microsoft partner logo

 
 
 
data analytics dashboard AI

Posted on October 4, 2023 by Andrew (Sal) Salazar .

data analytics dashboard AI
Debunking common misconceptions about Data Analytics to empower businesses of all sizes. Discover the key factors to consider when choosing a data science consulting firm. Learn how expertise, customization, communication, data security, and ROI play crucial roles.
 
Data analytics has become an integral part of decision-making processes in businesses across various industries. However, there are still many misconceptions surrounding this field that need to be debunked. In this blog post, we’ll address some common myths about data analytics and shed light on the truth behind them.

Myth #1: Data analytics is only for big companies

One of the most prevalent misconceptions is that data analytics is only for big companies with massive amounts of data. This couldn’t be further from the truth. While it’s true that large corporations may have more resources to invest in data analytics, small and medium-sized businesses can also benefit greatly from it.

Data analytics helps businesses of all sizes make informed decisions, optimize processes, and identify opportunities for growth. Working with the right firm, like Colaberry, using the latest tech and cloud-based solutions, even startups and small businesses can harness the power of data to gain a competitive edge.

 

Myth #2: Data analytics is all about numbers and statistics

While numbers and statistics play a significant role in data analytics, it is not just about crunching numbers. Data analytics involves the extraction of valuable insights from data to drive strategic decision-making. It encompasses a holistic approach that combines technical skills with business acumen.

Data analysts not only analyze data but also interpret and communicate the results to stakeholders. They translate complex findings into actionable insights that can guide business strategies. So, it’s not just about numbers; it’s about understanding the story that the data is telling and using it to drive business success.

Myth #3: Data analytics is a one-time process

Another common misconception is that data analytics is a one-time process. In reality, it is an ongoing and iterative process. Data analytics involves continuous monitoring, analysis, and optimization to ensure accurate and up-to-date insights.

Businesses need to establish a data-driven culture where data is regularly collected, analyzed, and acted upon. By embracing data analytics as an ongoing practice, organizations can make data-driven decisions that lead to improved performance and better outcomes.

zoomed in image of data analytics dashboard

Myth #4: Data Analytics is Solely for Tech-Specialists

Contrary to popular belief, data analytics is not an esoteric domain exclusively for experts in technology. With a competent data team at the helm, data becomes an accessible asset that empowers the entire organization to make more informed decisions.

Key visualization tools and effective communication strategies are instrumental in ensuring that leadership comprehends the insights data provides. Putting together the right team doesn’t have to be a chore if you work with a firm like Colaberry. A well-equipped data team can demystify complex information, making it comprehensible for everyone.

Myth #5: Data analytics can replace human intuition

While data analytics provides valuable insights, it is not a substitute for human intuition and expertise. Data analytics should be seen as a tool to augment decision-making rather than replace it.

man-with-tablet-data-analytics

Human intuition, experience, and domain knowledge are essential in interpreting data and making informed judgments. Data analytics can help validate or challenge our assumptions, but it is ultimately up to humans to make sense of the insights and take appropriate actions.

Data analytics is a powerful tool that can revolutionize the way businesses operate. By debunking these common misconceptions, we hope to encourage more organizations to embrace data analytics and leverage its potential for growth and success.
Whether you’re a large corporation or a small startup, if you’re ready to start putting your data to work, you should reach out to Colaberry today. 
We’re ready to help you start making better decisions, optimize processes, and gain a competitive advantage in today’s data-driven world. 

 
 

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data science consultant sitting in front of laptop holding phone.

Posted on September 22, 2023 by Andrew (Sal) Salazar .

 

data-science-diagram
 
In today’s rapidly evolving technological landscape, businesses are increasingly relying on data to drive their decision-making processes. Data science consulting has emerged as a valuable service, providing businesses with the expertise needed to analyze and interpret complex data sets.

However, traditional hiring models for data science consultants can be costly and time-consuming. That’s where on-demand talent comes in, offering numerous benefits for businesses seeking data science consulting services. Let’s explore the advantages of on-demand talent for data science consulting and why it is becoming an increasingly popular choice for businesses.
 
Are you ready to try on-demand talent for your date needs? Then you should reach out to Colaberry today to discuss your business goals and data needs.
 

infographic of Colaberry's solutions stack

Cost-effectiveness

One of the primary benefits of on-demand talent for data science consulting is its cost-effectiveness. Hiring a full-time data science consultant can be expensive, requiring businesses to provide a competitive salary, benefits, and other overhead costs. On the other hand, on-demand talent allows businesses to access highly skilled data scientists on a project-by-project basis, significantly reducing costs. By paying only for the services they need, businesses can allocate their resources more efficiently and achieve a higher return on investment.

data science consultant sitting in front of laptop holding phone.

For example, a startup with limited funds may require data science consulting services to develop a predictive model for their business. Instead of hiring a full-time data scientist, they can engage an on-demand talent platform, where they can find experienced data science professionals to work on their project within their budget. This cost-effective approach enables startups and small businesses to leverage data science expertise without breaking the bank.

Flexibility and Scalability

Another significant advantage of on-demand talent for data science consulting is the flexibility and scalability it offers. Traditional hiring models often require businesses to commit to long-term contracts, which may not be suitable for projects with varying demands or uncertain durations. On-demand talent, on the other hand, allows businesses to scale their data science consulting needs up or down according to their requirements.

A company may need data science consulting services only during the initial stages of a project to analyze market trends and customer behavior. Once the project reaches a certain stage, the need for data science expertise may decrease. With on-demand talent, businesses can easily adjust their resources, ensuring they have the right level of expertise at each stage of their project.

Access to Diverse Skill Set

Data science is a multidisciplinary field that requires expertise in various domains such as statistics, machine learning, programming, and data visualization. Hiring a full-time data science consultant with expertise in all these areas can be challenging. On-demand talent platforms, however, provide businesses with access to a diverse pool of data science professionals with specialized skills.

By leveraging on-demand talent, businesses can tap into the expertise of data scientists with specific skill sets that align with their project requirements. Whether it’s developing a recommendation system, implementing a natural language processing algorithm, or creating a data visualization dashboard, on-demand talent platforms can connect businesses with the right experts for the job. This diversity of skills ensures that businesses receive top-notch consulting services tailored to their specific needs.

Faster Time-to-Market

In today’s fast-paced business environment, time-to-market is crucial for gaining a competitive edge. Traditional hiring processes for data science consultants can be lengthy, involving multiple rounds of interviews and negotiations. On the other hand, on-demand talent platforms allow businesses to quickly find and engage data science professionals, significantly reducing time-to-market.

By leveraging on-demand talent, businesses can kickstart their data science projects without delay, ensuring they can capitalize on emerging opportunities and make data-driven decisions faster. This faster time-to-market enables businesses to stay ahead of the competition and adapt to changing market dynamics more effectively.

Access to Cutting-Edge Techniques and Tools

Data science is a rapidly evolving field, with new techniques and tools being developed regularly. Hiring a full-time data science consultant may limit businesses’ access to the latest advancements in the field. On-demand talent, on the other hand, provides businesses with access to data scientists who are up-to-date with the latest techniques and tools.

image of computer motherboard

By engaging on-demand talent, businesses can benefit from the expertise of data scientists who are constantly learning and experimenting with new methodologies. This ensures that businesses receive state-of-the-art data science consulting services, enabling them to gain insights and make decisions based on the most advanced techniques available.

The Benefits Are Clear

On-demand talent offers numerous benefits for businesses seeking data science consulting services. From cost-effectiveness and flexibility to access to diverse skill sets, faster time-to-market, and cutting-edge techniques, on-demand talent provides businesses with the expertise they need, when they need it.

Have you explored leveraging on-demand talent to maximize your resources, stay ahead of the competition, and make informed decisions based on data-driven insights? If not it may make sense to have a conversation with a talent provider like Colaberry, who can help you find the right talent in the right form to meet your business goals. 

 

 
 

microsoft partner logo

 
 
 

Posted on July 13, 2023 by Andrew (Sal) Salazar .

In today’s digital age, data science has emerged as one of the most captivating and sought-after professions. With businesses and organizations relying heavily on data-driven decision-making, the demand for skilled data scientists continues to soar. In this curated blog post, we will explore five undeniably compelling reasons why data science remains the sexiest job of the 21st century.

Flourishing Demand for Data Scientists

The growth of data-driven industries in recent years has been nothing short of remarkable. Every day, organizations across the globe collect vast amounts of data, and they need experts who can transform this raw information into valuable insights. Data scientists are the go-to professionals in this field, as they possess the skills and expertise to derive meaningful conclusions from complex data.

Moreover, emerging fields such as artificial intelligence and machine learning heavily rely on data science. Companies are constantly adopting these technologies to automate processes, reduce costs, and gain a competitive edge. The integration of data science in such forward-thinking industries ensures a sustained demand for data scientists well into the future.

Impressive Earning Potential

Aside from the intellectual allure of data science, let’s not forget the financial rewards it brings. Data scientists are among the highest-paid professionals in the job market, and their earning potential is substantial. Salaries for data scientists often surpass those of other technical roles due to the specialized nature of their work and the scarcity of skilled professionals

Furthermore, data science offers immense opportunities for career growth and advancement. As their skills and experience expand, data scientists can progress into managerial roles or specialize in niche areas such as deep learning or natural language processing. The demand for qualified professionals in these subfields is high, often resulting in even higher remuneration.
 

“The power of data science lies in its ability to uncover hidden patterns & its potential to transform industries and shape the future. See why it’s still the sexiest career of the century”

Passionate Pursuit of Problem-Solving

Data scientists display an insatiable curiosity and a relentless pursuit of answers hidden within vast datasets. They are like modern-day detectives, applying their analytical skills to solve complex problems that can have far-reaching implications. This characteristic makes data science an inherently exciting and stimulating field to work in.

man looking at whiteboard that says what makes data science interesting?

The field of data science thrives on finding meaningful insights and patterns from seemingly chaotic data. Translating this raw information into actionable intelligence requires a combination of analytical thinking, creativity, and technological expertise. Data scientists revel in the challenges presented by complex datasets, pushing boundaries to extract hidden gems of knowledge.

Intersection of Multiple Disciplines

Data science transcends traditional academic boundaries, integrating various disciplines such as statistics, mathematics, and computer science. It is at the intersection of these diverse fields that data scientists bring invaluable expertise. They possess a skill set that combines statistical analysis and mathematical modeling with advanced coding and algorithm design.

Collaboration is a fundamental aspect of data science, as data scientists often work alongside professionals from different backgrounds. Interacting with business analysts, software engineers, and domain experts enhances the richness of the analysis. By collaborating with experts from diverse fields, data scientists can better understand the nuances of a problem and develop more comprehensive solutions.

Continuous Learning and Innovation

Data science is a rapidly evolving field, with new technologies and tools constantly emerging. Staying up-to-date with the latest advancements and acquiring new skills is an inherent part of a data scientist’s journey. This continuous learning ensures that data scientists remain at the forefront of innovation and maintain their competitive edge.

Access to new research and developments is also an inherent part of a data scientist’s role. The data science community is vibrant, with conferences, meetups, and publications constantly sharing groundbreaking discoveries and best practices. Data scientists have the opportunity to contribute to the advancement of the field and make their mark through pioneering research.
 

Conclusion

The remarkable allure of data science in the 21st century stems from its unique combination of intellectual stimulation, impressive earning potential, interdisciplinary collaboration, and constant learning. With its prominence across industries and the ever-growing demand for skilled professionals, data science unquestionably remains the sexiest job of the 21st century.

If you are passionate about solving complex problems, using data to drive meaningful insights, and being at the forefront of innovation, a career in data science is undoubtedly worth exploring. 
Contact Colaberry to learn about the most advanced training in data available and if a career in data is right for you. 

 

 

image of pipelines

Posted on March 13, 2023 by Andrew (Sal) Salazar .

The Solution to Your Data Talent Pipeline Needs

Do you have a reliable data talent pipeline/source?
Are you struggling to build a robust data talent pipeline for your organization? 40% of companies say they are experiencing a skills gap when it comes to data talent. As the economy starts to look better, this skills gap and talent shortage will only get worse. However, the good news is that there is a solution to your data talent pipeline needs, and it’s called Colaberry.

Colaberry is a cutting-edge data science and analytics training provider that focuses on bridging the talent gap in the data industry. We offer comprehensive and industry-relevant training programs that equip individuals with the skills and knowledge they need to succeed in today’s data-driven world.

Our training programs are designed and delivered by industry experts who have years of experience in the data science and analytics field. They use real-world scenarios and case studies (similar to the Harvard method of teaching) to help learners apply their knowledge to solve practical problems. Our approach to training has proven to be effective, and we have trained thousands of individuals who have gone on to work for and grow in both SMBs and Fortune 1,000s.

But Colaberry is more than just a training provider. We are also a talent partner for organizations looking to build a strong data talent pipeline. We work with organizations to identify their talent needs and provide customized training solutions that address their unique challenges. We also offer job placement assistance to our learners, ensuring that they find the right job opportunities that match their skills and interests.

diagram of colaberry structure

Imagine having a reliable source for top-tier talent that is catered to your company’s specific data needs. That’s what Colaberry delivers.
So, if you’re struggling to build a robust data talent pipeline for your organization, look no further than Colaberry. We have the expertise, experience, and resources you need to succeed in today’s data-driven world. To learn more contact us today.

Andrew “Sal” Salazar
[email protected]
682.375.0489
LinkedIn Profile

Open AI chatgpt image with black background

Posted on March 13, 2023 by Andrew (Sal) Salazar .

The One Question to Ask Chat GPT to Excel in Any Job

Have you ever found yourself struggling to complete a task at work or unsure of what questions to ask to gain the skills you need? We’ve all been there, trying to know what we don’t know. But what if there was a simple solution that could help you become better at any job, no matter what industry you’re in?

At Colaberry, we’ve discovered the power of asking the right questions at the right time. Our one-year boot camp takes individuals with no experience in the field and transforms them into top-performing data analysts and developers. And one of the keys to our success is teaching our students how to use Chat GPT and how to ask the right questions.

Everyones talking about Chat GPT but the key to mastery with it lies in knowing how to ask the right question to find the answer you need. What if there was one question you could ask Chat GPT to become better at any job? This a question that acts like a magic key and unlocks a world of possibilities and can help you gain the skills you need to excel in your career. 

Are you ready? The question is actually asking for more questions. 

“What are 10 questions I should ask ChatGPT to help gain the skills needed to complete this requirement?”

By passing in any set of requirements or instructions for any project, Chat GPT can provide you with a list of questions you didn’t know you needed to ask. 

In this example, we used “mowing a lawn”, something simple we all think we know how to do right? But, do we know how to do it like an expert?

Looking at the answers Chat GPT gave us helps us see factors we might not ever have thought of. Now instead of doing something “ok” using what we know and asking a pointed or direct question, we can unlock the knowledge of the entire world on the task!

And the best part? You can even ask Chat GPT for the answers.

Now, imagine you had a team of data analysts who were not only trained in how to think like this but how to be able to overcome any technical obstacle they met.

If you’re looking for talent that not only has a solid foundation in data analytics and how to integrate the newest technology but how to maximize both of those tools, then Colaberry is the perfect partner. We specialize in this kind of forward-thinking training. Not just how to do something, but how to use all available tools to do something, to learn how to do it, and more. Real-life application of “smarter, not harder”.

Our approach is built on learning a foundation of data knowledge that is fully integrated with the latest tech available, to speed up the learning process. We use Chat GPT and other AI tools to help our students become self-sufficient and teach them how to apply their skills to newer and more difficult problem sets. 

But, they don’t do it alone. Our tightly knit alumni network consists of over 3,000 data professionals throughout the US, and many of Colaberry’s graduates have gone on to become Data leaders in their organization, getting promoted to roles such as Directors, VPs, and Managers. When you hire with Colaberry, you’re not just hiring one person – you’re hiring a network of highly skilled data professionals.

So why not take the first step toward unlocking the full potential of your data? Let Colaberry supply you with the data talent you need to take your company to the next level. 

Contact us today to learn more about our services and how we can help you meet your unique business goals.

Want more tips like this? Sign up for our weekly newsletter HERE  and get our free training guide: 47 Tips to Master Chat GPT.

Jupyter Hub Architecture Diagram

Posted on March 15, 2021 by Yash .

Serving Jupyter Notebooks to Thousands of Users

In our organization, Colaberry Inc, we provide professionals from various backgrounds and various levels of experience, with the platform and the opportunity to learn Data Analytics and Data Science. In order to teach Data Science, the Jupyter Notebook platform is one of the most important tools. A Jupyter Notebook is a document within an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

In this blog, we will learn the basic architecture of JupyterHub, the multi-user jupyter notebook platform, its working mechanism, and finally how to set up jupyter notebooks to serve a large user base.

Why Jupyter Notebooks?

In our platform, refactored.ai we provide users an opportunity to learn Data Science and AI by providing courses and lessons on Data Science and machine learning algorithms, the basics of the python programming language, and topics such as data handling and data manipulation.

Our approach to teaching these topics is to provide an option to “Learn by doing”. In order to provide practical hands-on learning, the content is delivered using the Jupyter Notebooks technology.

Jupyter notebooks allow users to combine code, text, images, and videos in a single document. This also makes it easy for students to share their work with peers and instructors. Jupyter notebook also gives users access to computational environments and resources without burdening the users with installation and maintenance tasks.

Limitations

One of the limitations of the Jupyter Notebook server is that it is a single-user environment. When you are teaching a group of students learning data science, the basic Jupyter Notebook server falls short of serving all the users.

JupyterHub comes to our rescue when it comes to serving multiple users, with their own separate Jupyter Notebook servers seamlessly. This makes JupyterHub equivalent to a web application that could be integrated into any web-based platform, unlike the regular jupyter notebooks.

JupyterHub Architecture

The below diagram is a visual explanation of the various components of the JupyterHub platform. In the subsequent sections, we shall see what each component is and how the various components work together to serve multiple users with jupyter notebooks.

Components of JupyterHub

Notebooks

At the core of this platform are the Jupyter Notebooks. These are live documents that contain user code, write-up or documentation, and results of code execution in a single document. The contents of the notebook are rendered in the browser directly. They come with a file extension .ipynb. The figure below depicts how a jupyter notebook looks:

 

Notebook Server

As mentioned above, the notebook servers serve jupyter notebooks as .ipynb files. The browser loads the notebooks and then interacts with the notebook server via sockets. The code in the notebook is executed in the notebook server. These are single-user servers by design.

Hub

Hub is the architecture that supports serving jupyter notebooks to multiple users. In order to support multiple users, the Hub uses several components such as Authenticator, User Database, and Spawner.

Authenticator

This component is responsible for authenticating the user via one of the several authentication mechanisms. It supports OAuth, GitHub, and Google to name a few of the several available options. This component is responsible for providing an Auth Token after the user is successfully authenticated. This token is used to provide access for the corresponding user.

Refer to JupyterHub documentation for an exhaustive list of options. One of the notable options is using an identity aggregator platform such as Auth0 that supports several other options.

User Database

Internally, Jupyter Hub uses a user database to store the user information to spawn separate user pods for the logged-in user and then serve notebooks contained within the user pods for individual users.

Spawner

A spawner is a worker component that creates individual servers or user pods for each user allowed to access JupyterHub. This mechanism ensures multiple users are served simultaneously. It is to be noted that there is a predefined limitation on the number of the simultaneous first-time spawn of user pods, which is roughly about 80 simultaneous users. However, this does not impact the regular usage of the individual servers after initial user pod creation.

How It All Works Together

The mechanism used by JupyterHub to authenticate multiple users and provide them with their own Jupyter Notebook servers is described below.

The user requests access to the Jupyter notebook via the JupyterHub (JH) server.
The JupyterHub then authenticates the user using one of the configured authentication mechanisms such as OAuth. This returns an auth token to the user to access the user pod.
A separate Jupyter Notebook server is created and the user is provided access to it.
The requested notebook in that server is returned to the user in the browser.
The user then writes code (or documentation text) in the notebook.
The code is then executed in the notebook server and the response is returned to the user’s browser.

Deployment and Scalability

The JupyterHub servers could be deployed in two different approaches:
Deployed on the cloud platforms such as AWS or Google Cloud platform. This uses Docker and Kubernetes clusters in order to scale the servers to support thousands of users.
A lightweight deployment on a single virtual instance to support a small set of users.

Scalability

In order to support a few thousand users and more, we use the Kubernetes cluster deployment on the Google Cloud platform. Alternatively, this could also have been done on the Amazon AWS platform to support a similar number of users.

This uses a Hub instance and multiple user instances each of which is known as a pod. (Refer to the architecture diagram above). This deployment architecture scales well to support a few thousand users seamlessly.

To learn more about how to set up your own JupyterHub instance, refer to the Zero to JupyterHub documentation.

Conclusion

JupyterHub is a scalable architecture of Jupyter Notebook servers that supports thousands of users in a maintainable cluster environment on popular cloud platforms.

This architecture suits several use cases with thousands of users and a large number of simultaneous users, for example, an online Data Science learning platform such as refactored.ai

Image of man with green code projected on him and wall behind

Posted on September 11, 2020 by Yash .

A LinkedIn post by Eric Weber on career paths originating from Data Science started me thinking along the same lines. Through this blog, it is my humble intention to curate the different changing roles that have evolved in Data Science, as well as speculate on how these new roles came into being. 

When I first started hearing about Data Science, way back in the early 2000s, some of the earliest roles were Data analysts. Then, it seemed like an outgrowth from the Data entry roles that came into existence in the late 20th century. Then in 2011, McKinsey came out with its paper “Big Data: The next frontier for Innovation, competition, and Productivity” which brought to the forefront the inability of many organizations to harness the power of data because of a lack of skilled personnel. Visharg Shah’s article on Medium explains how the field of data science grew rapidly. the reasons can be summarized as due to these reasons – 

  • Exponential growth in data collected from various sources by companies
  • Massive growth in processing power and processing capacity toward the analysis process
  • Availability of tools for analysis and open access to frameworks and modules simplifying the task
  • Organizations find value in analyzing historical data and big data collected
  • Increased importance in using data for decision-making augmenting traditional methods
  • Democratization of data science leading to increasing adoption by most industry segments
  • Digitization of historic data especially those held by public agencies 

Even as the companies scrambled to hire the best data science talent, they conceded that the job market sorely lacked the talent for these roles. 

Following that by the mid-2015s, the roles had evolved into the roles of Data Scientist, Data Analyst, Data Engineer, and Business Analyst. These roles were segmented within the field of Data science depending on the skills needed and the role within the organization.

As companies have started to dig deeper into their data, they realized that a person with just knowledge of data science might not be enough. Organizations expected more from their hires, seeking those – Who knew what type of data they were dealing with, who had domain expertise in the industry, what insights could be developed, how to visualize these analytics, predict trends in the industry, etc. New roles came into existence like Linguistics Analyst (Uses knowledge of different cultures to provide an accurate picture for decision-makers including dialect, nuances, body language, and cultural context) or Sustainable Chemistry Analyst (Performs product testing, maintaining a database of sustainable chemicals with extensive operational responsibilities). Thus, we can see that at a basic level, the roles have not changed much, and yet they have.

Let us consider an entry-level Procurement Operations Analyst role that I found from Amazon to make my point.

Main Responsibilities

  • In partnership with the Procurement Operations Manager, provide procurement operations support for the fulfillment center, including forecasting of non-inventory products, inventory management, non-inventory flow and space models, cycle counts, supplier management, procurement transaction, and expediting support
  • Led team of non-inventory receivers to ensure the building has adequate resources and is set up for success
  • Develop deep knowledge of non-inventory items and align with like buildings to drive best practices
  • Manage KPI to measure, control, and benchmark procurement processes including the creation of recurring metrics reports driving improvements for the Operations network
  • Develop relationships across the building and network to ensure best practices are being shared and implemented
  • Align with internal customers, Finance, and Procurement Operations to understand budgetary targets by building and developing methods of measuring and defining savings, value, and other category metrics
  • Using input from the category team, build the category metrics model to track and monitor the performance of the category strategy
  • Measure actual vs planned savings; as savings trends are identified, own action plans to meet goals and develop solutions
  • Work in partnership both internally and with suppliers to develop innovative solutions to provide Procurement support to the Operations network
  • Develop and implement ways to measure suppliers to drive continuous performance improvement on behalf of XXXXXXXX
  • Coordinate the demand identification, procurement, and inventory management of all non-merchandise items required for building operations. This includes corrugate, packing materials, labor, janitorial services, etc.
  • Partner with the Category team to manage and maintain supplier scorecards
  • Partner with AP, Suppliers, and various internal teams to ensure the timely resolution of vendor payment issues
  • Support the procurement operations and category management teams
  • Work is done in a warehouse environment that requires frequent walking around the building. You should feel comfortable working in an environment with varying temperatures as many buildings have dock doors that open throughout shifts.

Basic Qualifications

  • Completed Bachelor’s Degree in Supply Chain Management, Business Administration, Engineering, IT, or related field, OR 2+ years of XXXXXXXX experience
  • 3+ years of experience in supply chain operations
  • 1+ years’ experience using Microsoft Office, particularly Excel and analytical platforms, including but not limited to the ability to analyze data using pivots and V-Lookups
  • 1+ years of people management experience
  • Experience understanding process flows and suggest improvements to deliver cost savings, inventory reduction, or other benefits to the site.
  • Supplier/ vendor relationship management experience

Preferred Qualifications

  • Procurement experience preferred
  • Experience in Coupa or other financial management/procurement software
  • Experience with cost accounting
  • Lean / Six-Sigma knowledge
  • Must be highly self-motivated and customer-centric
  • Ability to work with ambiguity
  • Provide a positive customer experience internally and externally

If we look at the highlighted content, we can see clearly how Amazon seeks to utilize data science for procurement roles. Understanding data points, KPIs and other industry thought processes and finally data-driven decision-making become important along with a need to have domain expertise. For someone beginning to learn data sciences, it is essential to find application areas and actively work on projects to remain relevant in the job market. I believe that future trends would lead to even more specialization based on software/applications as well as industry segments thus resulting in more roles. 

For now, I aggregated data points on various roles based on the input of LinkedIn users who commented on the LinkedIn post I mentioned earlier. Various roles that I was able to aggregate are:

  1. Analytics engineer
  2. Analytics manager
  3. Analytics Translator
  4. Applied scientist
  5. BI Consultant
  6. Big Data Developer/Architect
  7. Biostatistician
  8. Business intelligence engineer
  9. Category Manager
  10. Chief Data Officer
  11. Chief Privacy Officer
  12. Continuous Improvement Managers
  13. Data Architect 
  14. Data Domain Leader
  15. Data Engineer
  16. Data Ethicist
  17. Data Governance Analyst 
  18. Data Governance Lead 
  19. Data Maintenance Specialist
  20. Data Management Lead 
  21. Data Miner 
  22. Data Modeller 
  23. Data Platform Engineers 
  24. Data Quality expert
  25. Data steward
  26. Data visualization engineer
  27. Database Developer
  28. DataOps Engineers 
  29. Decision scientist
  30. Director – Data Governance
  31. Econometrician
  32. Enterprise Architect
  33. Enterprise Data Architect
  34. ETL Developer
  35. Financial Analyst
  36. Insights specialist
  37. Learning Analytics 
  38. Logistics Manager
  39. Machine learning engineer
  40. Market Research Analyst 
  41. Marketing Scientist
  42. Metrics analyst
  43. Ontologist (Semantics expert)
  44. Operations Analyst
  45. Operations Manager
  46. Performance improvement leader
  47. Pricing Manager
  48. Product analyst
  49. Product Manager
  50. Product Specialist
  51. Psychometrician
  52. Research scientist
  53. Sales and marketing analyst
  54. Scientist Educational 
  55. Citizen Data Scientist

This list of job roles is not comprehensive and many more roles are emerging every day. I want your help in my attempt to compile an exhaustive list of job roles involving data science. I want to create this list to identify domain expertise and data science skills that will help guide data science enthusiasts in the right direction. Do you know of any roles where data science or data analysis is recently become essential? Did we miss out on any roles? Let us know your thoughts.

The post was written by Yashwant Kuram

Image of Colaberry Data Scientist at Amazon Deepracer Workshop

Posted on August 11, 2020 by Kevin Guisarde .

Ready, Set, AWS DeepRacer

It was a bright Friday afternoon in July in the lovely city of Boston, Massachusetts. We had close to 30 participants ready to race their cars. Sounds straight out of a Fast and Furious movie, right? What if I told you that these participants were actually racing autonomous vehicles on a virtual race track created on Amazon Sagemaker?

This project was the brainchild of Kristina, Pawan, and Sathwik. Earlier this year, they attended a workshop on reinforcement learning for autonomous vehicle racing conducted by Amazon. When they took the idea of conducting workshops in collaboration with Amazon Web Services (AWS) to Ram, the CEO of Colaberry, he was very enthusiastic. And that was what led me to create Colaberry – AWS Deepracer course on Deepracer for quite a few days along with a fellow intern, Manaswi.

We found that Deepracer can be an important tool to introduce people to the world of Machine Learning: they can see its real-world applications and use it in a fun way. The virtual vehicle applies the ML reward function by rewarding an autonomous vehicle for following the track properly and penalizing it for “bad” behaviors like going off track. This reward function utilizes Python programming, and advanced participants in the AWS Deepracer league even prepared their own codes using an exhaustive list of input parameters. For those unfamiliar with coding, the platform also had pre-created reward functions.

As an introductory workshop, we refrained from using complex code, instead explaining the pre-created codes. Advanced users or those seeking to participate in the community race the next day could use the advanced notebooks available. These notebooks provided a deeper understanding of hyperparameters, reward graphs, and other tips that can help a person succeed in the race.

Are you ready to race?

This post was written by Badri Yashwant Kuram, a Colaberry Intern. 

 

Our Program 

Colaberry has been providing one-of-a-kind, career-oriented training in data analytics and data science since 2012. We offer instructor-led onsite and online classes. Learn with us in person on our campus in Plano, Texas, or remotely from the comfort of your home. We have helped over 5,000 people to transform their lives with our immersive boot camp-style programs.

In-Demand Skills

Colaberry training programs equip you with in-demand tech and human skills.  Our up-to-date lessons and carefully crafted curriculum set you up for success from day one. Throughout the training and the job search, our mentors will support and guide you as you transition into a fast-paced and exciting field of data analytics and data science. 

Project-Based Learning

Our programs integrate projects that are based on real-world scenarios to help you master a new concept, tool, or skill. We work with you to build your portfolio to showcase your skills and achievements. 

Award Winning Learning Platform

You will be learning using our homegrown technology platform Refactored AI which is recognized as the “Most Promising Work of the Future Solution” in global competition by MIT SOLVE. Our platform also received General Motors’ “Advanced Technology” prize and McGovern Foundation’s “Artificial Intelligence for Betterment of Humanity” prize. 

 Placement Assistance

Colaberry’s program, platform, and ecosystem empower you with skills that you need to succeed in your job interviews and transition into high-paying careers. Over 1/3rd of Colaberry graduates receive job offers after their first in-person interview. We provide you with continuous mentoring and guidance until you land your job, as well as post-placement support for twelve months so that you not only survive but thrive in your career. 

Financial Aid

At Colaberry, we strive to create opportunities for all. We work with each individual to ensure the cost of the training does not hold them back from becoming future-ready. We offer various payment plans, payment options, and scholarships to work with the financial circumstances of our learners. 

Military Scholarship

Colaberry is committed to supporting men and women who have served our country in uniform. As part of this commitment, we offer Military Scholarships to enable active-duty and retired military members to transition into civilian life. We have already helped numerous veterans by creating a pathway to rewarding and exciting careers in data science and data analytics. We hold alumni events and provide an extensive support system and a strong community of veterans to help our students succeed. Contact our enrollment team to find out more about how we can help you.