https://www.datacamp.com/community/podcast/harnessing-the-power-of-collaboration

<p>In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration.</p>

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration.

Harnessing the Power of Collaboration

https://www.datacamp.com/community/blog/harnessing-the-power-of-collaboration

https://www.datacamp.com/community/podcast/operationalizing-machine-learning-with-mlops

<p>In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators.&nbsp;</p>

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators. 

Operationalizing Machine Learning with MLOps

https://www.datacamp.com/community/blog/operationalizing-machine-learning-with-mlops

https://www.datacamp.com/community/news/datacamp-announces-new-funding-wm23nnrviyf

With significant growth investment from Spectrum Equity, DataCamp expands curriculum and enterprise features.

DataCamp Announces New Funding

https://www.datacamp.com/community/news/request-for-proposal-topical-projects-for-january-2019-sxeje87et4q

Learn how to become a DataCamp project instructor and have your project launch in January 2019.

Request for Proposal: Topical Projects for January 2019

https://www.datacamp.com/community/news/data-science-at-datacamp-ppx7ezzh2a

Two months after starting as Chief Data Scientist at DataCamp, I share some thoughts on the role and the team's future.

Data science at DataCamp

<br><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><br>
<p>In 2021, we witnessed the impact of COVID-19 on accelerating digital transformation. This rapid digitization has led to an explosion in the amount of data organizations are generating. As organizations look to reap the value of this data in 2022, they will adopt new technologies, upskill their workforce, and embrace new ways of work. Here are nine data science trends and predictions to expect in 2022.</p>
<h2 id="-1-organizations-accelerate-culture-transformation-programs">#1 Organizations accelerate culture transformation programs</h2>
<p>Data culture is the collective behaviors and beliefs of people who value, practice and encourage the use of data to improve decision-making. It sets the foundation and mindset for companies to effectively capture value from their ever-growing datasets.</p>
<p>Unfortunately, the <a target="_blank" href="https://c6abb8db-514c-4f5b-b5a1-fc710f1e464e.filesusr.com/ugd/e5361a_d59b4629443945a0b0661d494abb5233.pdf">lack of data culture</a> is a major roadblock in an organization’s roadmap to becoming data-driven.   </p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642644861/image2_t1r0vh.png" /></center>

<p>In 2022, we anticipate chief data officers (CDO) to focus on the key building blocks of data culture, which include proper data governance, data literacy programs, and nurturing a data-driven mindset. These are catalysts for organization-wide data-driven decision-making processes.</p>
<h2 id="-2-organizations-will-scale-data-governance">#2 Organizations will scale data governance</h2>
<p>The growing demand for self-serve analytics fueled the need for compliant, actionable, and high-quality data. Yet, the challenge of measuring and maintaining data quality scales in tandem with the size and complexity of the datasets. As such, companies are adapting their data governance strategies.</p>
<p>One such strategy is to adopt <a target="_blank" href="https://www.montecarlodata.com/what-is-data-observability/">data observability</a> in data pipelines. Simply put, data observability aims to identify, troubleshoot, and resolve data issues in near real-time.</p>
<p>In 2022 and beyond, more companies will scale their data governance programs and adopt new and modern tools to monitor and detect data quality issues.</p>
<h2 id="-3-nlp-ushers-in-a-new-generation-of-low-code-data-tools">#3: NLP ushers in a new generation of low-code data tools</h2>
<p>NLP has grown tremendously in the past few years, thanks to the arms race for ever-larger large language models (LLM) like T5, GPT-3, and Megatron-Turing NLG.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642644862/image3_mt06mh.png" />Large language models get larger over the years (<a href="https://huggingface.co/blog/large-language-models">Source</a>)</center></p>
<p>LLMs are pushing the limits of what NLP can do. The most recent models surprised the community with their ability to generate various types of texts (like computer code and guitar tabs) without any explicit training.</p>
<p>Such NLP models have the potential to usher in the age of low-code and no-code tools. Today, <a target="_blank" href="https://blogs.microsoft.com/ai/from-conversation-to-code-microsoft-introduces-its-first-product-features-powered-by-gpt-3/">Microsoft’s Power App</a> allows non-technical users to build apps with natural speech. Such tools will continue to lower the barriers to coding and facilitate the rise of citizen developers and citizen data scientists within organizations.</p>
<h2 id="-4-l-d-becomes-a-fabric-of-company-culture">#4 L&amp;D becomes a fabric of company culture</h2>
<p>Driven by the C-suite’s concern over their <a target="_blank" href="https://www.pwc.com/gx/en/issues/reinventing-the-future/take-on-tomorrow/covid-disparity-evaluation.html">organization’s lack of key skills</a>, companies will continue to invest heavily in learning and development (L&amp;D) programs in 2022. The benefits of such L&amp;D programs are obvious–the World Economic Forum predicted that <a target="_blank" href="https://www.pwc.com/gx/en/issues/upskilling/upskilling-for-shared-prosperity.html">38% of GDP can be gained from upskilling</a> by 2030.</p>
<p>As the workforce continues to cope with the pandemic, we expect companies to allocate their L&amp;D budgets on virtual learning ecosystems that facilitate effective learning and provide communities of practice. Companies looking to improve their employees’ data literacy at scale can leverage existing L&amp;D programs as internal data science skill academies.</p>
<h2 id="data-trend-5-mlops-will-continue-to-mature-within-organizations">Data Trend #5:  MLOps will continue to mature within organizations</h2>
<p>MLOps is a set of practices that combine machine learning, data engineering, and DevOps. It includes standardized processes that automate the machine learning workflow.</p>
<p>Companies can only extract value from machine learning at scale with production-level AI systems. This explains why the demand for MLOps is expected to grow immensely. In fact, the industry is estimated to be valued at <a target="_blank" href="https://www.cognilytica.com/2020/04/02/infographic-the-rapid-growth-of-mlops/">$126.1 billion</a> by 2025.</p>
<p>In the upcoming year, MLOps tools like <a target="_blank" href="https://www.kubeflow.org/">KubeFlow</a> and <a target="_blank" href="https://mlflow.org/">MLFlow</a> will continue to mature. It is only a matter of time before they become a staple for all data science teams.  </p>
<h2 id="data-trend-6-responsible-ai-becomes-more-operationalized">Data Trend #6: Responsible AI becomes more operationalized</h2>
<p>Unfortunately, many existing AI systems today are rife with hidden biases. As such, regulators <a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/what-the-draft-european-union-ai-regulations-mean-for-business">in the EU</a> are planning to hold such AI accountable, and many are expected to follow suit. Companies must ensure that their AI systems remain fair and responsible. Those that fail to do so risk damaging their reputations and are culpable for exacerbating inequality.</p>
<p>That is why companies are increasingly operationalizing principles of Responsible AI to ensure that AI remains fair, interpretable, privacy-preserving, and secure. An example of a framework is PwC’s <a target="_blank" href="https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html">Responsible AI Toolkit</a>, which addresses the various dimensions of Responsible AI.</p>
<h2 id="data-trend-7-rise-of-the-data-mesh">Data Trend #7: Rise of the data mesh</h2>
<p>Most data architectures today are in the form of data lakes. That might change soon as a new form of data architecture addresses the weakness of data lakes.</p>
<p>A new alternative, coined by Zhamak Dehghani is called the <a target="_blank" href="https://martinfowler.com/articles/data-monolith-to-mesh.html">data mesh</a>. A data mesh has distributed “data products”–each handled by a cross-functional team of data engineers and product owners. Adopting a data mesh architecture allows companies to <a target="_blank" href="https://www2.deloitte.com/nl/nl/pages/strategy-analytics-and-ma/articles/from-data-mess-to-a-data-mesh.html">deliver data faster and achieve greater business domain agility</a>.</p>
<p>Soon, as the pains of using data lakes become more acute, companies will begin experimenting with data mesh, as did <a target="_blank" href="https://databricks.com/session_eu20/data-mesh-in-practice-how-europes-leading-online-platform-for-fashion-goes-beyond-the-data-lake">Zalando</a> and <a target="_blank" href="https://medium.com/intuit-engineering/intuits-data-mesh-strategy-778e3edaa017">Intuit</a>.</p>
<h2 id="data-trend-8-new-generation-of-tooling-will-improve-the-data-team-s-productivity">Data Trend #8: New generation of tooling will improve the data team’s productivity</h2>
<p>Various data science productivity tools emerged in 2021 and will continue to gain popularity in the years to come. These tools reduce the need for manual work and allow data scientists to perform higher-value tasks.</p>
<p>Such productivity tools include AutoML tools (like <a target="_blank" href="https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html">H2O AutoML</a> and <a target="_blank" href="https://www.automl.org/automl/autopytorch/">Auto PyTorch</a>), which automates the process of machine learning model selection and even hyperparameter tuning.</p>
<p>A multitude of <a target="_blank" href="https://blogs.nvidia.com/blog/2021/06/08/what-is-synthetic-data/">synthetic data generation tools</a> have also been on the rise. Their ability in creating balanced and labeled datasets at scale is particularly attractive for data-hungry companies.</p>
<p>Large data science teams might also find collaboration tools like <a href="https://databricks.com/solutions/data-science">Databricks</a> and <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a> useful. These tools allow data scientists to collaborate asynchronously in data exploration and ML modeling.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/workspace"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642779150/chrome-capture_krnwqo.gif" /></a></center>
<h2 id="data-trend-9-the-talent-crunch-and-flexible-work-will-broaden-and-improve-the-search-for-data-talent">Data Trend #9: The talent crunch and flexible work will broaden and improve the search for data talent</h2>
<p>As the Great Resignation rages on, the talent crunch becomes ever more severe. This phenomenon prompts organizations to rethink how they recruit and retain data talent.</p>
<p>In particular, we expect companies to prioritize skills over zip codes in their hiring policies, as evident from the <a target="_blank" href="https://www.linkedin.com/business/talent/blog/talent-strategy/global-data-shows-surge-in-remote-work">280% increase in remote job postings</a> on LinkedIn since March 2020. Moreover, as FAANG companies embrace the work-from-home arrangement, we expect other tech companies to follow suit and provide their employees with flexible working options too.</p>
<p>For more on our data trends and predictions for 2022, download the free white paper, or register for our upcoming webinar where we’ll deep-dive into what’s coming in data science in the new year!</p>
<br><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/2022-data-trends-and-predictions

2022 data trends and predictions

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642616851/DC_for_Classrooms_logo_1_ejektm.png" /></center><br>

<p>DataCamp is proud to announce our partnership with Tableau to make world-class data science software and education widely available to the teachers and students of the world. Every teacher with both a DataCamp for Classrooms account and a Tableau for Teaching license <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">can apply</a> to gain free access to DataCamp’s interactive Tableau courses for themselves and their students. We see this as an important opportunity to bring STEM education fully into the 21st century with a partner just as dedicated as us to democratizing data science.</p>
<p>Data is being consumed at an astronomical rate but are all students given the same opportunity to see and understand data? Do instructors have the resources easily and readily accessible to prepare the next generation of students? The answer, sadly, is a resounding no.</p>
<p>“Tableau for Teaching is excited about the partnership with DataCamp,” says Jessica Lyons, Senior Manager of <a target="_blank" href="https://www.tableau.com/academic/teaching">Tableau for Teaching</a>. “The mission of arming students and instructors with the resources needed to create a data-literate world aligned perfectly with DataCamp’s goals."</p>
<p>The feeling is mutual. At DataCamp, we see Tableau as an ideal platform to get high school and college students excited about data. No other data visualization tool caters to novices and experts alike as deftly as Tableau does. Teachers armed with these resources will empower their students to better understand and analyze the data in their lives, preparing them for tomorrow’s data-driven industries. With just some basic knowledge of Excel/Spreadsheets and a good data set, young people can create powerful and inviting visualizations of the data that matters to them most.</p>
<h2>How do teachers apply?</h2>
<p>It’s a simple three-step process for qualified teachers to apply for Tableau access on DataCamp:</p>
<ol>
<li><a target="_blank" href="https://www.datacamp.com/groups/classrooms">Apply and get approved for DataCamp for Classrooms</a></li>
<li><a target="_blank" href="https://www.tableau.com/academic/teaching#form">Apply and get approved for Tableau for Teaching</a></li>
<li>Fill out the <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">official DataCamp + Tableau application form</a></li>
</ol>
<p>DataCamp and Tableau representatives will review all applicants within a week of the submission date to verify their status in both programs. Once verified, you&#39;ll receive a confirmation email once Tableau training has been added to your DataCamp group. All the users you invite into that DataCamp for Classrooms account will get immediate, free access to our Tableau courses.</p>
<h2>What do students and teachers get with DataCamp + Tableau?</h2>
<p>DataCamp for Classrooms offers free unlimited Professional licenses for you and your students to learn on DataCamp. That means access to over 350 courses in R, Python, SQL, Git, Scala, Spreadsheets, Excel, as well all of our projects, practice modules, the mobile app, and more. And teachers get a group dashboard with all the functionality they need to run a successful classroom: the ability to create teams, give assignments, and monitor progress. College/university teachers from around the world and high school teachers in the US and UK are eligible to apply.</p>
<p>By also becoming a recipient of Tableau for Teaching and <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">completing this form</a>, teachers will gain access to our six outstanding courses on Tableau, <a target="_blank" href="https://app.datacamp.com/learn/courses/introduction-to-tableau">including Introduction to Tableau</a>, <a target="_blank" href="https://app.datacamp.com/learn/courses/connecting-data-in-tableau">Connecting Data in Tableau</a>, and <a target="_blank" href="https://app.datacamp.com/learn/courses/analyzing-data-in-tableau">Analyzing Data in Tableau</a>—everything you and your students need to go from novices to experts. Just like all our other courses, they can run entirely in-browser (no additional software required), or users can download the datasets and run our exercises on their desktop-based Tableau clients. Previously reserved for Premium and Enterprise DataCamp customers, these in-demand courses will completely change how educators around the world teach students Tableau.</p>
<p>If you’re a teacher, apply to these revolutionary edtech programs today (students, please refer this offer to your STEM teachers)! This is the only way to learn to become a Tableau whiz at your own pace for free.</p>
<p>To apply for free Tableau courses through DataCamp for Classrooms, <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">click here</a>.</p>
<p>To apply for a free DataCamp for Classrooms account, <a target="_blank" href="https://www.datacamp.com/groups/classrooms">click here</a>.</p>
<p>To apply for a free Tableau for Teaching license, <a target="_blank" href="https://www.tableau.com/academic/teaching#form">click here</a>.</p>


https://www.datacamp.com/community/blog/datacamp-and-tableau-partner-to-teach-young-people-data-visualization

DataCamp has partnered with Tableau for Teaching to provide world-class courses on Tableau to teachers and students around the globe for free. Find out how you can help improve your students’ data literacy with DataCamp for Classrooms.

DataCamp and Tableau Partner to Teach Young People Data Visualization

<p><center><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Python+vs+R.pdf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603467084/Tutorial__R_or_Python_for_Data_Analysis_fkkn9s.png" /></a></center><br></p>
<p>It&#39;s hard to know whether to use Python or R for data analysis. And that’s especially true if you&#39;re a newbie data analyst looking for the right language to start with.</p>
<p>But it is possible to figure out the strengths and weaknesses of both languages. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer: What should I use for machine learning? I need a fast solution, so should I use Python or R?
Python vs. R for Data Analysis
At DataCamp, we often get emails from learners asking whether they should use Python or R when performing their day-to-day data analysis tasks. Both Python and R are among the most popular languages for data analysis, and each has its supporters and opponents. While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R&#39;s functionality was developed with statisticians in mind, thereby giving it <a href="http://www.quora.com/Which-is-better-for-data-analysis-R-or-Python">field-specific advantages</a> such as great features for data visualization.</p>
<p>Our infographic &quot;When Should I Use Python vs. R?&quot; is for anyone interested in how these two programming languages compare to each other from a data science and analytics perspective, including their unique strengths and weaknesses. Click the image below to download the infographic and access the embedded links.</p>
<p><center><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Python+vs+R.pdf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603890265/Python_vs_R_zerop6.pdf" /></a></center><br></p>
<p>For additional insight into these languages, read our blog post on <a href="https://www.datacamp.com/community/blog/when-to-use-python-or-r">Python vs. R for Data Science: What’s the Difference?</a> To explore these languages for yourself, try our <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science/">Introduction to Python</a> and <a href="https://www.datacamp.com/courses/free-introduction-to-r/">Introduction to R</a> courses.</p>


https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis

Wondering whether you should use Python or R for data analysis? You’ve come to the right place. 

Python vs. R. What is the difference between Python and R? Find a fun infographic & see why you should learn Python or R for data science today!

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