We recently spoke with two of our certified DataCamp learners on how getting certified and our personalized career coaching helped them land their dream jobs.
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After learning and applying your skills on DataCamp, getting certified is the next step to help you launch or further your career in data science.
Peter is the perfect example of this experience.
From a young age, you could catch Peter playing multiple sports such as basketball, hockey, and his personal favorite, baseball. His love for playing sports spurred his interest in working in the field. After completing his studies at Carleton University, he landed a job as a sports statistician at the university. During this experience, Peter quickly came to realize that he needed to further upskill himself to continue his professional development in the sports analytics space.
This is where DataCamp came in. Peter spent a total of three years learning and practicing to continuously expand his skillset. Case studies provided the ideal opportunity to apply his skills to a portfolio, often working on one for two weeks.
Once DataCamp launched Certification, Peter jumped on the opportunity and successfully completed the assessments. After getting certified, Peter got access to our career services team that helped guide him through his job search.
<blockquote>
“We had a whole conversation around my resume. Working with the DataCamp career services team really helped me position myself for success during my job search.”
</blockquote>
Remember that little seed that was planted to work in the sports industry? Certification was his ticket in. Through this experience, Peter was able to land a job on the analytics team for the Canadian Olympics team! Given the success of the Canadian team at this year&#39;s Summer Olympics, we’re sure there will be lots for Peter to dive into: seven gold medals, six silver and 11 bronze!
Next up, we’d like to introduce you to Sebastian. Having recently moved to the United States (during a pandemic), Sebastian took this opportunity to further upskill his data science skills through DataCamp. After getting certified and working with our career services team, Sebastian landed his dream job in the pharmaceuticals industry at Johnson &amp; Johnson!
<blockquote>
“The process was awesome! Personalized sessions with top insight and recommendations!”
</blockquote>
Having moved from Colombia, Sebastian was not used to writing cover letters in the past. Our career services team was able to help him bridge this gap, and help position him for success.
In his next role, Sebastian will be working as a data analyst at Johnson &amp; Johnson, developing dashboards for insights, and in the future, aided with machine learning.
We wish them both the best of luck in their future endeavors!
Interested in getting certified by DataCamp? <a target="_blank" href="http://datacamp.com/certification">Get started</a> on your certification journey today
<center><a target="_blank" href="http://datacamp.com/certification"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1628780064/certification_banner_ogbksj.png" /></a></center>

https://www.datacamp.com/community/blog/certified-learners-landing-dream-jobs

After training on DataCamp and successfully getting certified as professional data scientists, see how Peter and Sebastian landed roles in their target industries through DataCamp’s help.

How DataCamp’s Certification is helping learners land their dream jobs

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1628101335/degreed-partnership-1_pmiyaq.png" /></a></center>
DataCamp is pleased to announce that we have partnered with Degreed to offer <a target="_blank" href="http://www.datacamp.com/degreed-data-science-training">free data literacy training to organizations</a>, regardless of whether they already have a DataCamp subscription or not.
<h3 id="who-is-degreed-">Who is Degreed?</h3>
Used by a third of Fortune 50 companies, Degreed is a workforce upskilling platform that connects all your learning, talent development, and internal mobility opportunities to intelligence on the skills your business needs next. Degreed personalizes what is offered to learners by recommending training based on the learner’s strengths, career aspirations, and the skills that businesses require in the modern day.
<h3 id="about-the-courses-on-offer">About the courses on offer</h3>
The three courses we are offering through Degreed are as follows: <a target="_blank" href="https://www.datacamp.com/courses/data-science-for-everyone">Data Science for Everyone</a>, <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-for-everyone">Machine Learning for Everyone</a>, and <a target="_blank" href="https://www.datacamp.com/courses/data-engineering-for-everyone">Data Engineering for Everyone</a>. These do not require any previous experience or knowledge of data science and involve no coding, so they are an excellent way to begin your move towards organization-wide data fluency. 
<ul>
<li><a target="_blank" href="https://www.datacamp.com/courses/data-science-for-everyone">Data Science for Everyone</a> is where we always recommend people begin if they are new to our site and data science in general, as it explains the fundamentals of data science and the different roles within this field.
</li>
<li><a target="_blank" href="https://www.datacamp.com/courses/data-engineering-for-everyone">Data Engineering for Everyone</a> hones in on one of these fields, one that has seen particularly explosive demand in recent years. Discover how data engineers lay the groundwork that makes data science possible.
</li>
<li><a target="_blank" href="https://www.datacamp.com/courses/machine-learning-for-everyone">Machine Learning for Everyone</a> is where you’ll learn everything you’ve been too afraid to ask about machine learning and introduces you to the hot topic of artificial intelligence and machine learning.
</li>
</ul>
By the end of these three courses, your employees will have an understanding of core data science concepts and will be able to have more informed conversations with colleagues who are specialized in these areas.
<h3 id="why-have-we-partnered-">Why have we partnered?</h3>
Our partnership can be explained in three words: a shared mission. DataCamp’s focus has always been on data science and its democratization, and that of Degreed is on offering the most current, best-quality learning experiences. As data science improves everyday life more and more, we saw an opportunity to help businesses overcome the data skills gap, by combining DataCamp&#39;s hands-on approach to learning and Degreed&#39;s 10+ years of supporting career growth and business opportunities within their upskilling platform.
Needless to say, we’re looking forward to this!
Interested in learning more?
<a target="_blank" href="http://datacamp.com/degreed-data-science-training">Get in touch</a> to learn how you can unlock access to our free data literacy courses and have your questions answered.

https://www.datacamp.com/community/blog/degreed-partnership

DataCamp is pleased to announce that we have partnered with Degreed to offer free data literacy training to organizations, regardless of whether they already have a DataCamp subscription or not. 

Announcing our partnership with Degreed!

https://www.datacamp.com/community/blog/the-future-of-responsible-ai

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.

The Future of Responsible AI

<h3 id="the-demand-for-data-science-roles-and-how-getting-certified-can-help-you-land-one">The demand for data science roles and how getting certified can help you land one</h3>
Data science is one of the most in-demand skills according to a recent report by the <a target="_blank" href="http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf">World Economic Forum</a> - in every country in which they investigated the job market, a majority of their top ten “emerging” roles involved data science in some capacity. Because of the abundance of jobs in this field, understanding how it works and its capabilities will be a boon to your professional development. 
Furthermore, proving to potential future employers that you possess the required skills that come with a data science role will become even more important during a job search. That said, the opportunity to develop those data science skills is no longer accompanied by a large barrier to get started, instead, you can take control of your future. 
<h3 id="how-to-get-certified-online">How to get certified online</h3>
Throughout the pandemic, we’ve seen education move online at an unprecedented rate, and now certifications have increasingly moved into the online sphere as well. There are plenty of available options to demonstrate your knowledge without leaving your home, giving you the opportunity to get certified at your own convenience. But with so many certifications out there, it can be hard to know where to begin, so we’ve compiled a list of the top five online data science certifications.
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<a target="_blank" href="https://online-learning.harvard.edu/series/professional-certificate-data-science">Harvard Professional Data Scientist</a>
$793
Powered by edX, this certification makes use of real-world case studies to teach data science, primarily focusing on R. From the basics of data science to machine learning, this certification makes use of questions based on the practical application of what is being taught: movie recommendations, election forecasting, and the financial crisis of 2007-08. This will enhance your real-world problem-solving skills in the eyes of employers.
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<a target="_blank" href="https://www.datacamp.com/certification">DataCamp Data Science Professional</a>
$29/month
Our certification will test you on the skills required to succeed as a professional data scientist. With a focus on R or Python and SQL, our certification consists of three assessments, a coding challenge, and a case study. During the case study section, you will present to both a technical and non-technical audience which mirrors real world environments.
Once certified, you will get access to our career services team. Our career services team is specialized in helping certified learners land their dream jobs in data science. Whether through tailored career advice, resume reviews, general best practices, or job matching, our team will help position you for success.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1628099774/top-5-online-data-science-certifications-3_hoclp1.png" /></a></center>
<a target="_blank" href="https://www.coursera.org/professional-certificates/ibm-data-science">IBM Data Science Professional Certificate</a>
$40-80/month
Run by IBM and Coursera, IBM’s certification covers the basics of data science while also going into detail on analyzing and visualizing data, primarily using Python. At the end you will complete a practice exercise, giving you something to add to your portfolio and demonstrate your skills to employers.
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<a target="_blank" href="https://www.coursera.org/specializations/excel-mysql">Excel to MySQL</a>
$40-80/month
Run by Duke University and Coursera, this certification will teach you how to answer business questions using data. Consisting of five different courses, this certification will cover the usage of SQL, Tableau, and Excel for business analytics.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1628099774/top-5-online-data-science-certifications-5_phkufk.png" /></a></center>
<a target="_blank" href="https://www.tableau.com/en-gb/learn/certification">Tableau</a>
$100 for Desktop Specialist
Tableau offers three different exams (Desktop Specialist, Certified Associate, and Professional) in using its product at various tiers. Each tier offers an ever-increasing collection of rewards such as a lower refresher exam fee. The exam is online and open-book, so if you forget one or two details, don’t sweat it. This certification will test your ability to use Tableau itself and create visualizations, which will enable you to better communicate your findings.
If you feel ready to prove your knowledge, go ahead and take our certification—and if you feel you could do with a bit more practice first, there are plenty of courses and exercises right here <a target="_blank" href="https://www.datacamp.com/">on DataCamp</a>!

https://www.datacamp.com/community/blog/top-5-data-science-certifications

In this blog post, we discuss five of the best online certifications, each of which specializes in different software.

Top 5 online data science certifications

To help learners find or build a community of fellow data enthusiasts, we’re launching a new Groups experience. Our goal is to connect learners with their peers on DataCamp and help them achieve their goals together while giving group creators powerful new tools to keep their groups engaged and progressing.
<h3 id="meet-fellow-data-enthusiasts-from-your-organization">Meet fellow data enthusiasts from your organization</h3>
DataCamp is home to over 8,6M data science learners. Imagine that there are colleagues from your organization or university, who, just like you, use DataCamp to expand their data skills and work on projects and problems similar to yours. Now you can connect with them using Groups to support and motivate each other in your data journey. How? Simply verify your academic or work email and get access to all free Groups created by learners from your organization. 
<h3 id="extra-push-you-needed-to-stay-on-track">Extra push you needed to stay on track</h3>
Online learning can be challenging, and many of us often need an extra bit of motivation to keep a learning streak. If only there was someone to hold us accountable, right? With Groups you now have a space and tools to do so! You or your fellow members can create Assignments—time sensitive learning goals—to help you complete a course, a track, or earn a certain amount of daily XP.
Every completed chapter gets you closer to your learning goal—and closer to the top of the Leaderboard. It’s visible to all members of the group and rankings can be filtered by time period, earned XP, or number of completed courses. If everyone’s watching, are you sure you want to stay at the bottom of the leaderboard?
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1627934305/introducing_groups_2_s1vqec.png" /></a></center>

<h3 id="track-your-learning-progress-as-you-go">Track your learning progress as you go</h3>

The true joy of learning is to see the results of your hard work. Groups provide you with two simple, yet powerful tools to monitor your skill growth. The Dashboard gives you a snapshot of your group’s activity—from time spent on the platform to total units completed. The Skill Matrix allows you to look closely into individual progress, identify key skill gaps and create learning paths to close them in the most efficient way.

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1627934305/introducing_groups_1_cggktv.png" /></a></center>

Groups are free for all DataCamp users. While you can invite an unlimited number of members, we recommend keeping it smaller (5-10) to ensure you have a more targeted overview of your group’s learning progress.

Curious to see how it works? Join Groups today!

<center><a target="_blank" href="https://learn.datacamp.com/groups/explore"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1628279231/introducing_groups_3_hjlzoy.png" /></a></center>

<h2 id="advanced-functionality-for-teams-and-organizations-big-and-small">Advanced functionality for teams and organizations, big and small</h2>
Would you like to take your collaborative learning to the next level? Our <a target="_blank" href="https://www.datacamp.com/groups/business#businessPlans">business plans</a> provide you with the more robust functionality to drive data science learning across teams and geographies.
<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>

https://www.datacamp.com/community/blog/datacamp-groups

To help learners find or build a community of fellow data enthusiasts, we’re launching a new Groups experience.

Meet Groups: your hub for collaborative data science learning

We sat down with our DataCamp Workspace users to learn more about their unique experiences while using the tool.. Who would’ve thought that a simple in-browser tool could transition to such a multi-purpose platform? Keep reading to find out how.
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<h3 id="the-ideal-tool-to-create-your-portfolio">The ideal tool to create your portfolio</h3>
Our first stop is with <a target="_blank" href="https://www.datacamp.com/profile/rpallangyo">Richard Pallangyo</a>—originally from Tanzania, Richard is currently living in the United States. After successfully completing his undergraduate degree, he is now looking to pursue a career in data science and software.
Through his learning journey on DataCamp, Richard discovered Workspace: “As long as I have access to the internet, I think I will prefer to use Workspace.” We couldn’t agree more! Richard has a total of nine Workspace publications that he used to build a portfolio for various job interviews. During one of his interviews, he leveraged his DataCamp profile to share his extensive portfolio.
<blockquote>
“They found one of my projects to be very interesting and wanted me to talk about it in the interview.” - Richard Pallangyo
</blockquote>
<h3 id="the-data-visualization-opportunities">The data visualization opportunities</h3>
Next, we talked to <a target="_blank" href="https://www.datacamp.com/profile/jorgitorr">Jorge Rodriquez Ramos</a>, a postdoctoral researcher in biophysics. In Jorge’s world of academia, the go-to programming language is usually MATLAB, but after initially discovering R, he came across Python and never turned back.
Jorge got started with DataCamp through a colleague&#39;s recommendation and ended up completing 14 courses!.
After upskilling on DataCamp, Jorge utilized Workspace to put his newfound learnings to the test. He used Workspace to create simple but very clear data visualizations to share with his colleagues, as an alternative to the more traditional, static plots that are often still used in the academic world. Jorge knows people who still use Fortran 90 and 77 to code, so by using Workspace to create easy-to-read visualizations, he is really “ahead of the curve.”
<h3 id="putting-newly-acquired-data-skills-into-practice">Putting newly acquired data skills into practice</h3>
Lastly, we talked to <a target="_blank" href="https://www.datacamp.com/profile/nsanjaysanju676">Sanjay</a>, a pre-final year student in India pursuing math and computing majors while balancing freelance work as a probability and statistics subject matter expert.
His friends introduced him to DataCamp and he quickly became a fan of the skill and career tracks: “The instructors are highly knowledgeable, everything is practically oriented—it’s amazing.” His friends also reminded him that DataCamp has a new product called Workspace, which he then started to explore. (My two cents on this? Sanjay has good friends.)
Sanjay&#39;s Workspace publication on COVID-19 data was based on a dataset from Kaggle and was created for the sole purpose of an academic assignment his professor gave him. He started working outside of Workspace and quickly realized that Workspace would make his life a lot easier. He created an in-depth analysis based on his DataCamp learnings, published them, and submitted them by simply sending his professor a link.
<blockquote>
“I learned most of my information from DataCamp’s skill tracks and the career tracks. I applied those skills in my Workspace project.” - Sanjay N.
</blockquote>
<h3 id="what-s-next-">What’s next?</h3>
Workspace is your one-stop solution to write, run, and share your analysis. And with such a flexible and versatile tool, the opportunities are endless.
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https://www.datacamp.com/community/blog/how-learners-use-workspace

From upskilling to portfolio curation - see how learners are utilizing Workspace.

How Workspace is enabling learners across the globe

<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>
<h3 id="many-machine-learning-projects-fail">Many Machine Learning Projects Fail</h3>
Many organizations are investing heavily in AI and machine learning. However, that does not mean organizations are reaping the value of machine learning within their organizations. According to <a target="_blank" href="https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/">VentureBeat</a>, 87% of data science projects do not make it to production. In their <a target="_blank" href="https://www.youtube.com/watch?v=EzmTZlho-EI">keynote at VentureBeat’s Transform 2019</a>, Deborah Leff, CTO for Data Science and AI at IBM, and Chris Chap, Senior Vice President of Data and Analytics at Gap, argue that many teams lack the right leadership and support for conditions that lead to success in these projects.
Leff and Chap describe data quality, data democratization, and organizational flow issues as key hurdles that many organizations do not have the proper tools or structure to overcome. Data teams that cannot access high-quality and relevant data or work in the correct collaborative environment are not able to succeed in these projects, regardless of their competence. They also describe issues with model decay where some organizations implement models that stop working without noticing for over three years. This was especially clear with the onset of <a target="_blank" href="https://tdwi.org/articles/2021/03/08/adv-all-covid-19-pandemic-accelerating-need-for-model-monitoring.aspx">Covid-19</a>, where lockdowns forced completely different behaviors onto consumers and many retail analytics models broke in production.
Another important consideration in ML projects is the rise in the use of open source technologies for testing and producing models. These include open-source programming languages like Python, and its host of libraries like Scikit-learn, pandas, PySpark, and more. Changing the version of one of these in the production environment can greatly impact the success of the project or make it stop working entirely. Managing these versions and open source frameworks requires an organized system to be effective at scale.
<h3 id="the-need-for-mlops">The need for MLOps</h3>
Machine Learning Model Operationalization Management <a target="_blank" href="https://www.datacamp.com/community/podcast/operationalizing-machine-learning-with-mlops">(MLOps)</a> is a way of standardizing and streamlining a machine learning project’s lifecycle management. In their book, <a target="_blank" href="https://content.dataiku.com/oreilly-mlops">Dataiku</a> discusses three key factors why MLOps is becoming so important to enterprises: there are many dependencies, organizations lack data literacy and common data language, and data scientists don’t have engineering skills.
Many dependencies: Data is a reflection of real-world behaviors and processes, and naturally changes over time (e.g., toilet paper buying habits during Covid-19). As data changes over time, which happens constantly, models in production must continuously adapt to these changes so that organizations keep solving the original problem they intended to solve with these models.
No common data language: In the machine learning life cycle, projects involve people from a diverse set of teams with different backgrounds. This includes data scientists, data engineers, but also functional subject matter experts. These groups do not share the same skills or data fluency to communicate about data in the same way, and their alignment is crucial to deploy models in production and evaluate their success.
Data scientists do not have engineering skills: Data scientists typically specialize in building models and evaluating them, not in writing applications to be deployed. As organizations rely on more models and more complex models, data scientists are facing a trade-off between developing and iterating on models and maintaining them in production.
<h3 id="why-mlops-is-useful">Why MLOps is Useful</h3>
Machine learning models are being increasingly deployed within business processes. This increase in volume necessitates the use of MLOps to mitigate risks associated with these models, deploy these models responsibly, and use them at scale.
There are many risks associated with deploying a model:
<ul>
<li>The model can be unavailable for some time. Technology can potentially be unreliable in the production environment. If an organization relies heavily on these models, it can face significant challenges when the technology becomes unavailable.
</li>
<li>The model can return poor results for a sample. It may perform very well for most groups but can give poor results for one group. This needs to be corrected or understood to avoid making poor business decisions based on model outputs for the underperforming group. This poor performance can also lead to ethical issues related to AI models underperforming for some protected variables (such as race, age, gender, etc.)
</li>
<li>The model can become less fair or accurate in the future. As the world changes, the data that was used in training becomes less representative of the world the model is attempting to understand. This can lead to poor performance on future data.
</li>
<li>Data science talent may leave the organization over time. Maintaining these models requires technical skills and bandwidth data scientists may not always have to ensure perfect reliability of these models.
</li>
</ul>
These risks scale depending on the impact the model has on the organization and the probability of one of these risk events occurring. Many of these risks can be mitigated by ensuring the training data is a good reflection of data that will be seen in the production environment and ensuring the production environment is stable as models are very sensitive to changes. MLOps is increasingly necessary for mitigating risks associated with the increasing number of machine learning models being deployed for important decisions.
As the use of machine learning models in decision-making becomes more prevalent, it is increasingly important to ensure they are <a target="_blank" href="https://www.datacamp.com/community/blog/the-case-for-responsible-ai">deployed responsibly</a>. There are a lot of causes of <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/data-literacy-for-responsible-ai">algorithmic bias</a> that need to be avoided when decisions are automated through AI systems. Failure to successfully minimize bias can lead to punishment through <a target="_blank" href="https://www.theverge.com/2021/4/20/22393873/ftc-ai-machine-learning-race-gender-bias-legal-violation">government regulation</a> or <a target="_blank" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3654943">loss of demand and profits</a> for a brand. To responsibly deploy AI systems, models must be trained on compliant and unbiased data sources, have interpretable and explainable results, and accountability throughout the organization where it is easy to find where in the development pipeline something potentially went wrong to fix it effectively. To deploy AI models responsibly, organizations require a streamlined ML model life cycle. 
Deploying more and more models requires MLOps to be successful. It is necessary to have a streamlined process to keep track of versioning, to understand model performance versus retrained model performance, and to ensure that a model continues to perform well over time. Without these MLOps, it is very difficult to successfully add value and business impact with these models. 
<h3 id="the-roles-of-an-mlops-program">The Roles of an MLOps Program</h3>
While MLOps is nascent and is still evolving rapidly, it is necessary to have strong people in an organization in the following roles: Subject Matter Experts, Data Scientists, Data Engineers, and Software and DevOps Engineers.
Subject matter experts are business-oriented individuals who understand and inform how to measure and continually evaluate the success of models during training and after deployment. For a successful MLOps program, they must develop an easy-to-understand business performance measurement and a feedback loop for distinguishing models that are not performing relative to their business expectations. Additionally, risk subject matter experts are an important part of the MLOps program. Their goal is to minimize and clarify the risk a company faces due to the deployment of ML models in production.
Course Recommendations: <a target="_blank" href="https://www.datacamp.com/tracks/data-literacy-fundamentals">Data Literacy Skill Track</a>
Data scientists are responsible for building the models which address the business questions generated by the subject matter experts. These models should be deployable in production environments with production data. Data scientists should ensure these models are easily and safely deployable in production, have tests for quality assurance and potential improvements, and have easy visibility of all deployed models from one location.
Course Recommendations: Machine Learning Scientists Career Track (<a target="_blank" href="https://www.datacamp.com/tracks/machine-learning-scientist-with-r">R</a>/<a target="_blank" href="https://www.datacamp.com/tracks/machine-learning-scientist-with-python">Python</a>), <a target="_blank" href="https://www.datacamp.com/courses/image-processing-in-python">Image Processing in Python</a>, <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-with-pyspark">Machine Learning with PySpark</a>
Data engineers are responsible for creating data pipelines for these models. For a successful MLOps program, performance for all deployed models must be easily visible as well as full detail for each pipeline to see where addressable issues may exist.
Finally, software and DevOps engineers are in charge of helping with the successful integration of these models with the other non-machine learning applications. For a successful MLOps practice, they must integrate data engineering best practices to ensure that these models can work in parallel with existing applications reliably, continuously, and seamlessly.
Course Recommendations: <a target="_blank" href="https://www.datacamp.com/tracks/data-engineer-with-python">Data Engineer with Python Career Track</a>
There are a lot of key roles that must be successfully utilized for a successful MLOps program. Successful deployment of hundreds of models, at scale, requires members outside of the data experts, including subject matter experts and software engineers, to generate positive business outcomes. Organizations striving to develop successful MLOps programs must aim to build a common data language across all stakeholders through upskilling and continuous learning.
Learn more about <a target="_blank" href="https://www.datacamp.com/groups/business">DataCamp for Business</a>: 
<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>
DataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.

https://www.datacamp.com/community/blog/the-past-present-and-future-of-mlops

MLOps is not just for data experts but for everyone from data teams to SMEs and to IT. Here's the rundown on why MLOps is important,  what problems it aims to solve, and how in this jargon-free blog post. 

The Past, Present, and Future of MLOps

The world stage of health crisis has undoubtedly generated that all people in their daily lives find themselves in the need to increase the use of technological platforms and tools; it is in this context, where I decided to further expand my knowledge regarding the use of technological tools, since this undoubtedly contributes to facilitating the daily tasks that are carried out on a daily basis.
So when I learned about Marki, I loved the idea and did not hesitate to join. It is a nonprofit association that seeks to contribute to the digitalization of microenterprises through advice provided by volunteers. By belonging to Marki, I was able to easily learn about digital marketing; The most remarkable thing about this experience was the dynamics that existed in the learning process, because not only did I learn from expert mentors, but I also reinforced what I had learned with other fellow volunteers, then managed to advise another micro-entrepreneur myself.
Additionally, one of the benefits of belonging to Marki was that I got a “DataCamp Donates” membership, which consisted of accessing one year of free access to DataCamp’s courses, considered to be one of the best platforms for learning data science in the world. When I found out about this, I asked Marki&#39;s administrative team without hesitation to have free access to the DataCamp, as it would prove quite useful for those who, like me, are convinced of the use of digital tools and data analysis to achieve business growth over time.
So my advice would be: if you’re like me and you find yourself interested in specializing more in the use of business intelligence tools and in the areas of analysis and data science; do not hesitate to take advantage of the DataCamp membership that Marki can offer you to continue growing professionally.
So far, I have developed R and Python courses related to the Data Analyst Career Track. This is helping me to develop the statistical part of my thesis project; whose theme addresses the study of the socioeconomic determinants of crime in Peru. In that sense, in my thesis I have put into practice what I learned about Ron Datacamp, in aspects such as database management (use of data from the ENAHO), operationalization of variables, and analysis of correlations and regressions; this has allowed me to develop a deep analysis of my data and obtain favorable results to the hypotheses of my research.
In a similar manner, I am now confident enough that in the medium term I will be able to form groups that will allow me to participate in hackathons that seek to solve social problems.
For now, I have participated in the first “Seedbed” of 2021, carried out by the organization Data Science Research Perú. In that seedbed I developed a project that sought the implementation of a supervised classification model to perform a Sentiment Analysis based on the techniques of Natural Language Processing (NLP) and was able to gauge approval rates for the educational program Beca 18.
The results were favorable, and my project was one of the eight finalists. I was able to achieve this result in part because of the courses that I took on DataCamp. First of all, the project was developed using Python, and in my case everything I know about Python I learned from DataCamp. On the other hand, the courses that I took related to the topic of Sentiment Analysis on DataCamp allowed me to understand fundamental concepts such as data preprocessing, the Bag of Words matrix, TF-IDF transformation and the use of different supervised classification models.
Finally, for everything mentioned in this article, I’m very thankful for the opportunity that Marki and DataCamp gave me to improve my data analysis skills. I can&#39;t wait to see what comes next!
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Sergio Abanto is an economist who is ready to collaborate in projects that contribute to achieving greater welfare and social development of the people and families of Peru.
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Marki Perú is a nonprofit organization that seeks to promote digitizing micro and small businesses in Peru through education. With the help of digital marketing and e-commerce experts, we train university students who volunteer to assess micro-entrepreneurs in their path of digitizing their businesses.

https://www.datacamp.com/community/blog/marki-peru

A DataCamp Donates learner from Peru shares his journey developing his data analysis skills to achieve greater social welfare in his country.

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