https://www.datacamp.com/community/blog/ace-the-data-science-interview

In this episode of DataFramed, we speak to Nick Singh and Kevin Huo on their latest book Ace the Data Science Interview.

Ace the Data Science Interview

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<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>
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https://www.datacamp.com/community/blog/2022-data-trends-and-predictions

2022 data trends and predictions



<p>If you are considering starting a data science career, the sooner you start coding, the better. Learning to code is a critical step for every aspiring data scientist. However, getting started in programming can be daunting, especially if you don’t have previous coding experience.</p>
<p>To choose the right programming language, we must first look at what data scientists do in their daily work. A data scientist is a technical expert who uses mathematical and statistical techniques to manipulate, analyze and extract information from data. There are many domains within the data science realm, from machine learning and deep learning, to network analysis, natural language processing, and geospatial analysis.  To perform their tasks, data scientists rely on the power of computers. Programming is the technique that allows data scientists to interact with and send instructions to computers.  </p>
<p>There are hundreds of programming languages out there, built for diverse purposes. Some of them are better suited for data science, providing high productivity and performance to process large amounts of data. However, this group still comprises a good number of programming languages.</p>
<p>In this article, we will look at some of the top data science programming languages for 2022, and present the strengths and capabilities of each of them.</p>
<ul>
<li>Python</li>
<li>R</li>
<li>SQL</li>
<li>Java</li>
<li>Julia</li>
<li>Scala</li>
<li>C/C++</li>
<li>JavaScript</li>
<li>Swift</li>
<li>Go</li>
<li>MATLAB</li>
<li>SAS</li>
</ul>

<h1 id="python">Python</h1>

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<p>Ranked first in several programming languages popularity indices, including the <a target="_blank" href="https://www.tiobe.com/tiobe-index/">TIOBE</a> Index and the <a target="_blank" href="https://pypl.github.io/PYPL.html">PYPL</a> Index, the popularity of Python has boomed in recent years. Python is an open-source, general-purpose programming language with broad applicability not only in the data science industry, but also in other domains, like web development and video game development.</p>
<br>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image2_m8wk5a.png"></center>
<center>Source: <a target="_blank" href="https://www.tiobe.com/tiobe-index/">TIOBE Index</a></center>

<p>Any data science tasks you can think of can be done with Python. This is mainly thanks to its rich ecosystem of libraries. With thousands of powerful packages backed by its huge community of users, Python can perform all kinds of operations, from data preprocessing, visualization, and statistical analysis, to the deployment of machine learning and deep learning models. Here are some of the most used libraries for data science and machine learning purposes:</p>
<p><strong>NumPy</strong>: is a popular package that offers an extensive collection of advanced mathematical functions. Many packages are based on Numpy objects, like the famous NumPy arrays.</p>
<p><strong>pandas</strong>: is a key library in data science, used for performing all kinds of manipulation of databases, also called DataFrames.</p>
<p><strong>Matplotlib</strong>: the standard Python library for data visualization.</p>
<p><strong>scikit-learn</strong>: built on top of NumPy and SciPy, it has become the most popular Python library for developing machine learning algorithms.</p>
<p><strong>TensorFlow</strong>: developed by Google, it is a powerful computational framework for developing machine learning and deep learning algorithms.</p>
<p><strong>Keras</strong>: an open-source library designed to train neural networks with high performance.</p>
<p>Due to its simple and readable syntax, Python is often referred to as one of the easiest programming languages to learn and use for beginners. If you are new in data science and don’t know which language to learn first, Python is one of the best options.</p>
<p>If you want to be a Python expert, DataCamp is here to help. Check out the <a target="_blank" href="https://www.datacamp.com/learn/python">Python courses</a> in our catalog and start your training to become a successful data scientist.</p>
<h1 id="R">R</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image10_zoxunl.png"></center>

<p>Not yet as highly trending as Python according to the popularity indices, R is a top option for aspiring data scientists. Frequently portrayed in data science forums as the main competitor of Python, learning one of these two languages is a critical step to break into the field.</p>
<p>R is an open-source, domain-specific language, explicitly designed for data science. Very popular in finance and academia, R is a perfect language for data manipulation, processing and visualization, as well as statistical computing and machine learning.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image9_ezal9o.png"></center>
<center>Source: <a target="_blank" href="https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html">KDnuggets</a></center>


<p>Like Python, R has a large community of users and a vast collection of specialized libraries for data analysis. Some of the most notable ones belong to Tidyverse family, a collection of data science packages. It includes dplyr, for data manipulation, and the powerful ggplot2, the standard library for data visualization in R. As for machine learning tasks, libraries like caret will make your life much easier when developing your algorithms.</p>
<p>Although it is possible to work with R directly on the command line, it is common to use Rstudio, a powerful third-party interface that integrates various capabilities, such as data editor, data viewer, and debugger.</p>
<p>Whether you are new to data science or want to add new languages to your arsenal, learning R is a perfect choice. Check out our rich catalog of <a target="_blank" href="https://www.datacamp.com/learn/r-programming">R courses</a> to start sharpening your skills.</p>
<h1 id="sql">SQL</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image17_jc0qta.png"></center>

<p>Much of the world&#39;s data is stored in databases. SQL (Structured Query Language) is a domain-specific language that allows programmers to communicate with, edit and extract data from databases. Having a working knowledge of databases and SQL is a must if you want to become a data scientist.</p>
<p>Knowing SQL will enable you to work with different relational databases, including popular systems like SQLite, MySQL, and PostgreSQL. Despite the tiny differences between these relational databases, the syntax for basic queries is pretty similar, which makes SQL a very versatile language.</p>
<p>Whether you choose Python or R to start your data science journey, you should also consider learning SQL. Due to its declarative, simple syntax, SQL is very easy to learn compared to other languages, and it will help you a lot along the way.</p>
<p>Want to get started in SQL? Have a look at the different <a target="_blank" href="https://www.datacamp.com/learn/sql">SQL courses</a> and <a target="_blank" href="https://www.datacamp.com/tracks/sql-fundamentals">skill tracks</a> offered by DataCamp and get ready to become a query master.</p>
<h1 id="Java">Java</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image16_c7j66k.png"></center>

<p>Ranked #2 in the PYPL Index #3 in the TIOBE Index, Java is one of the most popular programming languages ​​in the world. It’s an open-source, object-oriented language, known for its first-class performance and efficiency. Endless technologies, software applications and websites rely on the Java ecosystem.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image15_oykldb.png"></center>
<center>Source: <a target="_blank" href="https://pypl.github.io/PYPL.html">PYPL Index</a></center>

<p>Although Java is a preferred choice when developing websites or building applications from scratch, in recent years, Java has gained a prominent role in the data science industry. This is mainly because of the Java Virtual Machines, which provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala.</p>
<p>Due to its high performance, Java is a suitable language for developing ETL jobs and performing data tasks that require big storage and complex processing requirements, like machine learning algorithms.</p>
<h1 id="julia">Julia</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image4_omnnyl.png"></center>

<p>Julia can be considered a data science rising star. Despite being one of the youngest languages on this list, (it was released in 2011) Julia has already impressed the world of numerical computing. Sometimes referred to as the inheritor of Python, Julia is a highly effective tool compared to other languages used for data analysis.</p>
<p>Although it has gained notoriety thanks to its early adoption by several major organizations, including many in the financial industry, Julia still lacks the maturity to compete with top data science languages. It still has a small community and doesn&#39;t have as many libraries as its main competitors, Python or R.</p>
<p>Julia’s main downside is its youth, but there are numerous reasons to keep an eye on it. Let’s see how it evolves in the coming years.</p>
<h1 id="scala">Scala</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image6_ks3ls8.png"></center>

<p>Although it’s not very common to see Scala in the top rankings of programming languages, (it holds the #18 position in the PYPL Index and #33 in TIOBE) speaking about this programming language is mandatory in the context of data science.</p>
<p>Scala has recently become one of the best languages for machine learning and big data. Released in 2004, Scala is a multi-paradigmatic language explicitly designed to be a clearer and less wordy alternative to Java.</p>
<p>Scala also runs on the Java Virtual Machine, thereby allowing interoperability with Java and making it a perfect language for distributed big data projects. For example, the Apache Spark cluster computing framework is written in Scala.</p>
<h1 id="#C/C++">#C/C++</h1>
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<p>Considered two of the most optimized languages, being familiar with C and its close relative C++, can be very useful when it comes to addressing computationally intensive data science jobs.</p>
<p>C and C++ are comparatively faster than other programming languages, making them well-suited candidates for developing big data and machine learning applications. It isn’t a coincidence that some of the core components of popular machine learning libraries, including PyTorch and TensorFlow, are written in C++.</p>
<p>Due to their low-level nature, C and C++ are among the most complicated languages to learn. Therefore, although they may not be the first choices when embarking into the world of data science,  once you get a solid understanding of the fundamentals of programming, mastering them is a smart move that can make a great difference to your resume.</p>
<h1 id="javascript">JavaScript</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image11_h2oqqk.png"></center>

<p>JavaScript is on top of the preferred programming languages, according to the <a href="https://insights.stackoverflow.com/survey/2021">Stack Overflow Developer Survey 2021</a>. JavaScript is a multiparadigm, versatile language, widely known for its capacity to build rich and interactive web pages.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image5_hrf2tr.png"></center>
<center>Source: <a target="_blank" href="https://insights.stackoverflow.com/survey/2021">Stack Overflow</a></center>

<p>Although the majority of JavaScript users work in the web development sector, in recent years the language has gained notoriety in the data science industry. Today, JavaScript supports popular libraries for machine learning and deep learning, such as TensorFlow and Keras, as well as incredibly powerful visualization tools, like D3.</p>
<p>Thanks to the support of popular libraries for machine learning, and due to its broad popularity amongst web developers, it’s a smooth entry option for all front-end and back-end programmers who want to break into data science.</p>
<h1 id="swift">Swift</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image7_xgg8mn.jpg"></center>

<p>One of the downsides of Python and R is that neither of them were built with mobile devices in mind. In the coming years, we can expect an even bigger advancement of mobile, wearables and the IoT (Internet of Things). Swift was developed by Apple to make it easier to create apps and, with that, grow its app ecosystem and increase customer retention. Soon after its release in 2014, Apple and Google started working together to make it a key tool in the interplay between mobile and machine learning.</p>
<p>Swift is now compatible with TensorFlow and is interoperable with Python. An additional advantage of Swift is that it is no longer limited to the iOS ecosystem and it has turned open-source to work on Linux.</p>
<p>For these reasons, if you are a mobile developer and feel curious about data science, Swift is what you’re looking for.</p>
<h1 id="go">Go</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image13_up9uzk.png"></center>

<p>Go (or GoLang) is a language with increasing popularity, especially for machine learning projects. Google introduced it in 2009 with C-like syntax and layouts. According to many developers, Go is the 21st-century version of C.</p>
<p>More than a decade after its launch, Go is becoming extremely popular due to its flexible and easy-to-understand language. In the context of data science, Go can be a good ally for machine learning tasks. Despite its prospects, the data science community of Go is still very small.</p>
<h1 id="matlab">MATLAB</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image1_dwh0d6.png"></center>

<p>MATLAB is a language mainly designed for numerical computing. Broadly adopted in academia and scientific research since its launch in 1984, MATLAB provides powerful tools to carry out advanced mathematical and statistical operations, making it a great candidate for data science.</p>
<p>However, MATLAB has an important downside: it is proprietary. Depending on the case (academic, personal or business use), you may have to pay a large amount of money to get a license, making it less attractive than other programming languages that can be used for free.</p>
<h1 id="sas">SAS</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image12_lqtj5r.jpg"></center>

<p>SAS (Statistical Analytical System) is a software environment designed for business intelligence and advanced numerical computing. SAS has been around for a long time, and it’s widely adopted across major firms in many sectors, creating a big market for SAS developers.</p>
<p>However, SAS is steadily losing popularity against other data science programming languages like Python and R. This is mainly because, as occurred with MATLAB, you need a license to use SAS. This creates a barrier to entry for new users and companies, who will feel prone to use free, open-source languages.</p>
<h1 id="conclusion">Conclusion</h1>
<p>We hope this post will help you navigate the rich and diverse landscape of data science programming languages.
There is no single language that is best in absolute terms to solve all the problems and situations that may arise during your work as a data scientist. However, if you are a newcomer in data science, our recommendation is to start by picking either Python or R. You can enroll in our free <a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python Tutorial</a> and <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r?utm_source=blog_launch_new_intro&amp;utm_medium=blog&amp;utm_campaign=launch_new_intro">Introduction to R Tutorial</a> to see which one you like the most. From there, the key to success is patience and practice. To get hands-on programming experience, we recently launched <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a>, an online environment to write code, apply your skills  and create your data science portfolio.</p>
<p>Once you feel confident with your chosen language, you could level up with solid SQL training. Fortunately, DataCamp offers a good number of <a target="_blank" href="https://www.datacamp.com/learn/sql">SQL courses</a>.</p>
<p>From there, the sky&#39;s the limit. Becoming knowledgeable in multiple programming languages is an asset, and moving between languages according to the needs of your organization will help you become a versatile data scientist and develop a more successful career.</p>


https://www.datacamp.com/community/blog/blog-top-programming-languages-for-data-scientists-in-2022

Thinking about breaking into data science but don’t know which programming language to choose? Here’s all you need to know about the programming languages that will lead the data science sector in 2022.

Discover top programming languages to add to your skill set as a data scientist.  Learn the pros and cons of the languages and which ones to keep an eye out for. 

Top programming languages for data scientists in 2022

<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>A third of organizations polled in the CIPD’s <a target="_blank" href="https://www.cipd.co.uk/knowledge/strategy/development/learning-skills-work#gref">learning and skills at work report</a> saw their training budgets slashed in 2021. Smaller budgets, coupled with more people working from home than ever before, meant that team managers and L&amp;D professionals had to do more with less. This resulted in a drastic shift to digital training. Analysis by the CIPD has shown that organizations that embraced this change are reaping the rewards, with an improved culture and increased demand for learning from their workforce.</p>
<p>But it wasn’t all positive. The report also revealed that 77% of team managers and L&amp;D professionals had no way of assessing the impact of their learning program. The most commonly self-reported method for evaluating training programs was “participant satisfaction surveys”—subjective reporting that’s difficult to link to ROI and your organization’s business goals.</p><br>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192579/Marketing/Blog/L_D_reporting_-_DataCamp.jpg" />CIPD: <a target="_blank" href="https://www.cipd.co.uk/knowledge/strategy/development/learning-skills-work#gref">Barriers to the evaluation of learning and development program and initiatives (%)</a></center>

<br><p>As shown in the image above, there were many barriers to better reporting—competing priorities and a lack of time being top of that list. At DataCamp, we believe it should be simple to report on the business impact of your learning program. That’s why all <a target="_blank" href="https://www.datacamp.com/groups/business">DataCamp for Business groups</a> now have more ways than ever before to access insights you can share with stakeholders quickly.</p>
<h1 id="how-to-level-up-your-reporting-in-2022-">How to level up your reporting in 2022:</h1>
<h2 id="1-create-professional-one-click-reports-with-custom-reports">1. Create professional one-click reports with Custom Reports</h2>
<p>We get it. Reporting on the impact of your learning programs takes time. That’s why we created Custom Reports. They’re pre-made reports that DataCamp admins can use to demonstrate your learning ROI and reveal measurable insights, including everything from a weekly status report to your most engaged learners or most popular courses—with new reports added all the time.</p>
<p>Create professional reports from our pre-made templates
Get detailed insights into your learning program
Schedule regular, automated reports, so you never miss a deadline</p>
<p>Ready to improve your reporting? <a target="_blank" href="https://support.datacamp.com/hc/en-us/articles/4412251811223-DataCamp-Custom-Reports-An-Overview">Learn how to get started on our help center</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192361/Marketing/Blog/DataCamp-for-Business-Reporting-with-Custom-Reports.gif" /></center>

<h2 id="2-plug-and-play-connect-your-learning-data-to-tableau-and-power-bi">2. Plug and play—connect your learning data to Tableau and Power BI</h2>
<p>With our 2021 update to Data Connector, you can now create reports and dashboards directly in Tableau or Power BI using DataCamp’s built-in Athena connection. The Athena plug-in makes it easier to view, query, and visualize learning insights directly in your platform of choice.</p>
<p>Ready to analyze and visualize your data in Tableau or Power BI? Invite your engineering team to <a target="_blank" href="https://enterprise-docs.datacamp.com/data-connector/using-the-data-connector/analyzing-data">follow these steps</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192282/Marketing/Blog/DataCamp-for-Business-Reporting-with-Data-Connector.gif" /></center>

<h2 id="3-build-custom-reports-in-python-or-r-with-workspace">3. Build custom reports in Python or R with Workspace</h2>
<p>If your organization has enabled the Workspace editor, you can build your reports from scratch in Python or R, or choose from one of our ready-to-use Python Custom Reports.</p>
<ul>
<li>Analyze your organization’s learning data from the comfort of your browser</li>
<li>Share your insights while staying in control of who gets to see them</li>
<li>Comment directly on your Workspace reports, just like in Google Docs</li>
</ul>
<p>Want to build your own Custom Reports? <a target="_blank" href="https://app.datacamp.com/workspace/">Learn more</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192284/Marketing/Blog/DataCamp-for-Business-Reporting-with-workspace.gif" /></center>

<br><p>Let’s make 2022 the year that you go beyond employee satisfaction surveys and level up how you report on the difference you’re making to your workforce. <a target="_blank" href="https://www.datacamp.com/groups/business">Learn more about DataCamp for Business</a> or if you’re an existing customer, log in to try these new features.</p>
<h2 id="you-might-also-like-">You might also like:</h2>
<h4 id="white-paper-what-300-l-d-leaders-have-learned-about-building-data-fluency">White Paper: What 300+ L&amp;D Leaders Have Learned About Building Data Fluency</h4>
<p>Two of the most important actions required to deliver organization-wide data literacy are strong executive support and demonstrating value across multiple teams.</p>
<h5 id="-download-the-white-paper-https-www-datacamp-com-resources-whitepapers-what-300-l-and-d-leaders-have-learned-about-data-fluency-"><a target="_blank" href="https://www.datacamp.com/resources/whitepapers/what-300-l-and-d-leaders-have-learned-about-data-fluency">Download the white paper</a></h5>
<h4 id="customer-success-story-autodesk-bold-">Customer Success Story: Autodesk<bold></h4>
<p>Find out how this Fortune 1000 software company stayed ahead of the competition.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640194048/Marketing/Blog/Autodesk_and_DataCamp_-_customer_success_story.png" /></center>

<h5 id="-read-the-case-study-on-datacamp-https-www-datacamp-com-resources-case-studies-autodesk-case-study-"><a target="_blank" href="https://www.datacamp.com/resources/case-studies/autodesk-case-study">Read the case study on DataCamp</a>.</h5>


https://www.datacamp.com/community/blog/3-ways-to-level-up-your-team-reporting-in-2022

77% of learning professionals don’t know how to assess the impact of learning programs. That’s why, in 2022, DataCamp is making it easier for admins to report on the difference you’re making, using Custom Reports and our new Power BI and Tableau connectors. 

DataCamp is making it easier for admins to report on the difference you’re making, using Custom Reports and our new Power BI and Tableau connectors. 

3 ways to level up your team reporting in 2022

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641824947/DataCamp-subtitles-hello-world_gotbkm.png" /></center><br>

<p>It’s now easier to learn in your native language, with video subtitles available in 16 languages—including Arabic, Indonesian, Polish, Thai, Turkish, and Vietnamese.</p>
<p>All videos are also available with English, Spanish, French, Chinese (simplified), Portuguese, German, Korean, Japanese, Russian, and Italian subtitles.</p>
<h2 id="how-to-turn-on-video-subtitles-in-datacamp-">How to turn on video subtitles in DataCamp:</h2>
<p>To use these new subtitles, click on the closed captions (CC) icon when watching any video and select your preferred audio or subtitle options.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641824947/DataCamp-video-subtitles-_msggpy.png" /></center><br>

<p>Please continue to let us know <a target="_blank" href="https://support.datacamp.com/hc/en-us/requests/new?ticket_form_id=360000029413">how we can improve product features</a> like our translations and recommend which languages we should consider adding next. We would love to hear your <a target="_blank" href="https://support.datacamp.com/hc/en-us/requests/new?ticket_form_id=360000029453">feedback on our courses as well</a>! Together, we can make DataCamp a place where people from all walks of life can learn the language of data.</p>
<h2 id="you-might-also-like-">You might also like:</h2>
<h4>Customer Success Story: DataBird</h4>
<p>DataCamp helps a French bootcamp address the country&#39;s demand for data skills.</p>
<ul>
    <li><a target="_blank" href="https://www.datacamp.com/resources/case-studies/databird-case-study">Read the case study on DataCamp</a></li>
</ul>
<h4>Telling Effective Data Stories with Data, Narrative, and Visuals</h4>
<p>Brent Dykes discussed how data, narrative, and visuals can drive effective data stories in a recent webinar. Learn his best practices in this article.</p>
<ul>
<li><a target="_blank" href="https://www.datacamp.com/community/blog/telling-effective-data-stories-with-data-narrative-and-visuals">Read the best practices</a></li>
</ul>


https://www.datacamp.com/community/blog/six-new-subtitle-languages-added-to-datacamp

Learn in your native language! All DataCamp videos are now available in 16 languages, including Thai, Arabic, Turkish, Vietnamese, Polish, and Indonesian subtitles.

Six new subtitle languages added to DataCamp

<h1 id="introduction">Introduction</h1>
<p>In 2012, <a target="_blank" href="https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century">Harvard Business Review named data scientist as the sexiest job of the 21st century</a>. Ten years later, despite the emergence of AutoML platforms, (e.g., AWS Redshift ML and Google Cloud AutoML) and the pandemic-era slowdowns, data scientist remains one of the most wanted roles around. In fact, the data scientist ranks the highest paid role in the tech industry <a target="_blank" href="https://www.hiringlab.org/2018/03/15/data-science-job-postings-growing-quickly/">with an average yearly salary of $150,000</a>.</p>
<p>Although their specific responsibilities may vary from industry to industry and from company to company, most data scientists share the mission to help organizations create value from data. They do this by exploring patterns and trends in a large amount of data, communicating results to a broad range of stakeholders, and building and maintaining models to enable automated decision-making. Hence, becoming a data scientist requires a unique, diversified skill set covering statistics, coding, business sense, and communication—all to be demonstrated through a series of interview questions and take-home challenges.</p>
<p>With this blog post, we would like to share a few tips, from the perspectives of both an applicant and an interviewer, to help you stand out in a data scientist interview and get your dream offer.  </p>
<h1 id="tip-1-get-familiar-with-the-role-and-the-company">Tip #1: Get Familiar with the Role and the Company</h1>
<p>As mentioned above, “data scientist” is a vague term that can refer to any roles that revolve around data. Two data scientists from different companies or different industries may find themselves engaged in completely different types of tasks. Hence, it is essential to read through the responsibilities section in the job description, or ask the interviewer, “What would a day in this role look like? Would this role spend most of the time exploring and visualizing data, or building models?” <b>The more specifics you learn about this role, the sooner you will learn whether this role matches your profile and interests. This helps you target the positions that are a better fit and increases your chance of landing the job you truly want.</b></p>
<p>You could find useful tips on how to improve personal branding and profile matching on career websites like indeed and linkedin. If you are looking for career advice tailored for data science and analytics, <a target="_blank" href="https://www.datacamp.com/careerservices">the career services of Datacamp</a> would be very helpful - here you can find personalized sessions from career coaches who are specialized in this area.   </p>
<p>Once you have targeted the right role for you, the next step is to show the company your passion for their business. This can be a little tricky for those who are just starting off as a data scientist or those who want to switch industries, as you may not have much experience in solving business questions in the industry that you’re applying for.</p>
<p><b>One thing we find really helpful is to work on projects that are relevant to the target industries and mention them in the resume and interview processes.</b> For example, if you’d never received training in finance but wanted to apply for a data scientist role at an investment bank, working on a guided project like this one <a target="_blank" href="https://www.datacamp.com/projects/738">Modeling the Volatility of US Bond Yields</a> on DataCamp can help you learn what kinds of business questions a data scientist working in finance is expected to solve.</p>
<p>Kaggle is also a good platform where you can find a lot of interesting competition and datasets. By working on these projects and talking about them during the interviews, you will not only build basic knowledge of that industry but also show the company that you are so interested in their business that you are spending your spare time exploring their use cases.</p>
<h1 id="tip-2-let-your-technical-expertise-shine-through-the-take-home-challenges">Tip #2: Let Your Technical Expertise Shine Through the Take-home Challenges</h1>
<p>Usually, the hiring process of data scientists involves a take-home challenge where candidates are given one or multiple datasets, and a few business questions to solve. Although the submission requirements may vary, the candidates are usually expected to share the code, models, and analysis output.</p>
<p><b>Many candidates see this as a great opportunity to showcase their technical skills.</b> R and Python are the main data science languages and both have their own must-have packages for data wrangling, statistical modeling, and machine learning e.g., pandas, sklearn and statsmodels for python, as well as tidyr, dplyr and caret for R. If you are new to data science, it is recommended to take a few career track courses like these on DataCamp: <a target="_blank" href="https://www.datacamp.com/tracks/data-scientist-with-python">Data Scientist with Python</a> and <a target="_blank" href="https://www.datacamp.com/tracks/data-scientist-with-r">Data Scientist with R.</a> In addition to an extensive range of courses, Datacamp also provides a workspace where one can practice with pre-written code templates and pre-configured datasets for those who are looking to gain some hands-on experience to close the learning-doing gap.</p>
<p>What can sometimes be underestimated is the role of data storytelling. <b>Good data scientists are also effective storytellers who can communicate their model outputs well enough to convince stakeholders and thus drive real changes.</b> As the saying goes, “a picture is worth a thousand words”. Therefore, make sure you include intuitive data visualization in your analysis report to help interviewers understand how you uncover the patterns in the data, as well as show how your model can create value. In addition to common packages such as matplotlib (Python), seaborn (Python) and ggplot2 (R), <a target="_blank" href="https://plotly.com/graphing-libraries/">plotly</a> is a graphing library worth trying if you want to build a web-based, interactive dashboard. This course on DataCamp could be helpful: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-plotly-in-python">Introduction to Data Visualization with Plotly in Python.</a></p>
<p><b>Alternatively, you may try no-code BI tools such as Tableau, Power BI, and Google Data Studio.</b> These tools give you two advantages. On one hand, they are popular visualization tools that are used by most non-tech stakeholders such as data analysts and product managers. Hence, mastering these tools shows that you can easily integrate with the data analytics stacks that the company uses. On the other hand, they provide more customization than Python or R packages do, which allow you to easily build a slide-style, interactive analysis report. If you are new to these tools, this <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a> course on DataCamp is a good starting point.</p>
<p><b>Last but not least, the delivery of your code also matters.</b> Unless otherwise specified, it is always a good idea to build your solution on Git and have all dependencies readily incorporated into your code. Nothing creates more frustration for interviewers than when they receive a zip file and try to run the code, yet only see error messages like “package XYZ can not be found”. Meanwhile, make sure your code comes with clean, proper documentation which makes it easy for people to follow your train of thought.</p>
<h1 id="tip-3-keep-in-mind-the-data-product-lifecycle-when-sharing-your-previous-project-experience">Tip #3: Keep in Mind the Data Product Lifecycle when Sharing Your Previous Project Experience</h1>
<p>Most of the time, candidates applying for a data science job are expected to share their previous project experience. A common pitfall is to spend too much time describing the technical effort e.g., how they clean data and fine-tune (hyper)parameters. This might lead to overloading your interviewers with the information they may not fully understand, and at the same detracts from the limited time they have to discover your business acumen and stakeholder management skills.</p>
<p>Hence, I would recommend that when preparing for the interview question about your previous projects, <b>make sure your answer follows the <a target="_blank" href="https://www.themuse.com/advice/star-interview-method">STAR framework and covers all stages</a>of the data product lifecycle, even if you are not directly involved in all of these areas</b>:</p>
<ul>
<li>Business goals and questions: What business problem(s) does your project try to solve? What goal(s) or KPI(s) does your project contribute to?</li>
<li>Data collection: What challenge(s) did you have in gathering the data and how did you tackle them?</li>
<li>Exploratory data analysis: How did you present your analysis result to non-tech stakeholders? What questions or criticisms did they raise and how did you respond to them?</li>
<li>Modeling and logic: Why did you choose the model(s) you finally implemented (discuss both technical and non-technical motivation)? What are the key limitations of your approach?</li>
<li>Testing and deployment: How did you test and deploy your model(s)?</li>
<li>Monitoring: What metrics did you choose to evaluate the model performance? What learnings did you have and how did you improve the model based on your learnings?</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641472767/image1_a1nwx4.png" /></center>
<center>Figure 1. Data Product Lifecycle</center>


<p>Moreover, don’t forget to highlight the different roles that were involved (e.g., product managers, data analysts, data engineers, QAs, and business operation managers), their responsibilities, and how you interacted with them. Hearing this, your interviewers are likely to be impressed with both your business sense and teamwork.</p>
<h1 id="conclusions">Conclusions</h1>
<p>With this blog post, we looked at the recent growth in the demand for data scientists. We then discussed a few tips which can help you crack data scientist interview questions—both technical and non-technical. If you aspire to land a data scientist job in the future, you may be interested in trying out these tips or building your way toward your dream offer by starting with an <a href="https://www.datacamp.com/courses">accredited data scientist course.</a></p>


https://www.datacamp.com/community/blog/blog-applying-for-data-science-jobs

Learn how to apply for the jobs you desire and set your self apart.  Learn what makes you stand out and what steps to take stand out from crowd. 

Tips to set your self apart and get the data science role of your desire

applying-for-data-science-jobs

Applying for data science jobs and how to set yourself apart 

<p>AN ANALYSIS OF TOP 7 TRENDING DATA SCIENCE CAREERS TO PURSUE</p>
<p>Examining the incredibly popular field that is data science</p>
<p>Data processing, analysis, and visualizations were done using R and its corresponding packages. Notebook and pertinent files used can be found <a target="_blank" href="https://app.datacamp.com/workspace/w/03312344-a7b0-47cf-b97d-06125fabcbd8/edit">here</a>.</p>
<p>According to the Merriam-Webster dictionary, data is factual information used as a basis for reasoning, discussion, or calculation. By this definition, data is basically any piece of collected information that can be used and further processed and analyzed to gain insights. It is commonly associated with computers as data is normally produced and stored in a computer, yet data has been around much longer than we might imagine.</p>
<h1 id="history-of-data">History of Data</h1>
<p>The earliest example of humans storing and analyzing data dates back to 18000 BCE, when it was discovered that prehistoric humans were using tally sticks, as a means of carrying out rudimentary calculations. These Paleolithic tribespeople marked notches on sticks and bones to keep track of their activities, such as trading and monitoring supplies. By 2400 BCE, the abacus, a device used in performing calculations, was invented in Babylon.</p>
<p>Throughout history, constant development of data collection, processing, and analysis was seen through numerous writings in stone tablets, clay, papyrus, wood, and paper scrolls. Eventually, as more forms of data were discovered, the need for processing, collecting, storing, and analyzing it also evolved.</p>
<p>As human society became more advanced, so did its requirement for processing data. In the 1800s, the census started taking place in the United States. The number of data points in the census grew exponentially to the point that the U.S. Census Bureau estimated that it would take years or even decades to collect and analyze all the data in the census. This was largely problematic since compiling and analyzing the current census data would only be completed when the next census was about to start or had already started. Fortunately, a young engineer and inventor named Herman Hollerith developed the Hollerith Tabulating Machine–an electromechanical tabulating machine which reduced the time needed to collect and analyze census data from years, to just mere months. Because of this, Hollerith is regarded as the father of modern automatic computation and would later become known for founding IBM.</p>
<p>Fast forward to the 1900s, and the invention of the computer. With the advent of a more powerful computing machine comes a more sophisticated requirement for data storage. Fritz Pfleumer, a German-Austrian engineer, invented a method of storing information magnetically on tape. Some principles of his invention are still being used today for digital data storage. In this era, the term “business intelligence” gained popularity as the need for emerging software, and systems for analyzing commercial and operational performance, quickly rose.</p>
<p>The revolution of data truly changed when Tim Berners-Lee created the World Wide Web, also known as the Internet, in 1989. This led to automated information sharing between people all over the world. This means that today more data is being shared, created, and stored, leading to new ways for collecting, using, and analyzing data.</p>
<h1 id="transition-to-big-data-">Transition to “Big Data”</h1>
<p>Due to the incredible amount of internet growth in the 1990s and the steady development of personal computers and computing devices in general, the number of devices online (and consequently the amount of data created) grew quickly.</p>
<p>While the idea of Big Data had been around even before the 1990s, it was only in 2005 when Roger Mougalas officially gave it a label. He described it as “a large set of data that is almost impossible to manage and process using traditional business intelligence tools.”</p>
<p>Big Data is a term used to describe the large volume of data–both structured and unstructured–that overwhelms organizations on a day-to-day basis. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered.</p>
<p>Given the size and complexity of Big Data, the process of collecting, organizing, and analyzing it to discover patterns and other useful information has become part of helping a lot of organizations to make their business decisions. In turn this has given rise to data science–an interdisciplinary field which uses scientific methods, processes, algorithms, and systems from a large volume of data, to uncover patterns and enable business leaders to draw informed insights.</p>
<h1 id="what-is-data-science-">What is Data Science?</h1>
<p>According to IBM, data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. The field typically requires computer science and pure science skills as data scientists apply scientific methods in their approach, and use predictive analytics and artificial intelligence to extract insights from the data.</p>
<p>Nowadays, “data science” is often used by businesses and organizations as a catch-all term for the process of handling large amounts of data, whether it is preparing, cleaning, analyzing, or visualizing the data to reveal patterns. These are just some of the data science careers one can pursue:</p>
<ol>
<li><b>Data Scientist:</b><br>
<p>Data scientists need to be able to apply mathematics, statistics, and the scientific method; use multiple tools and techniques for cleaning and preparing data; perform predictive analytics and artificial intelligence; and explain how these results can be used to provide data-driven solutions to business problems. They require much more technical skills than data analysts. For starters, you may take data science-oriented courses like these on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python for Data Science</a> and <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a>.</p>
</li>
<li><b>Data Analyst:</b><br>
<p>Data analysts collect, process, and perform statistical data analyses to arrive at meaningful conclusions for organizations. They transform and manipulate large data sets into usable forms, such as reports or presentations. They also aid in the decision-making process by looking into important patterns, and gleaning insights from the data which they can then effectively communicate to organizational leaders to aid in business decisions. These courses on DataCamp may help you kick-start your data analyst career: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> and <a target="_blank" href="https://www.datacamp.com/courses/exploratory-data-analysis-in-sql">Exploratory Data Analysis in SQL</a>.</p>
</li>
<li><b>Data Engineer:</b><br>
<p>Data engineers are responsible for preparation, processing, and managing gathered and stored data for analytical or operational uses. Like the traditional engineers, data engineers build and maintain data “pipelines” which connect data from one system to another, making information accessible for data scientists. Because of this, data engineers are required to understand several programming languages used in data science such as Python, R, and SQL. Topics related to data engineering like these are available on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-data-engineering">Introduction to Data Engineering</a> and <a target="_blank" href="https://www.datacamp.com/courses/data-engineering-for-everyone">Data Engineering for Everyone</a>.</p>
</li>
<li><b>Data Architect:</b><br>
<p>Data architects mainly design and create blueprints for data management systems that data engineers then build. Analogous to the traditional architects, data architects are the “visionaries” as they are responsible for visualizing and designing an organization’s data management framework. In addition, data architects improve the performance of existing systems, making sure they are accessible to database administrators and analysts. You may take courses related to data architecture like these that are available on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/aws-cloud-concepts">AWS Cloud Concepts</a> and <a target="_blank" href="https://www.datacamp.com/courses/database-design">Database Design</a></p>
</li>
<li><b>Business Intelligence (BI) Developer:</b><br>
<p>BI developers are specialized engineers who use software tools to transform data into useful insights that help business decisions. They are responsible for simplifying technical information for everyone else in the company to easily understand. In short, they create and run reports containing data they find using business intelligence tools, and transform the information into more layman terms. Courses on using tools related to business intelligence such as Microsoft Power BI and Tableau are available on DataCamp such as these: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-power-bi">Introduction to Power BI</a> and <a target="_blank" href="https://www.datacamp.com/courses/analyzing-data-in-tableau">Analyzing Data in Tableau</a></p>
</li>
<li><b>Statistician:</b><br>
<p>Given that statistics is one of the main foundations of data science, many statisticians can easily transition into the data science field. Statisticians are mainly responsible for the collection and handling of data. They decide what data they need and how to collect it. Additionally, they design experiments, analyze and interpret the data, and report conclusions. You may check-out these introductory courses to statistics on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-statistical-modeling-in-r">Introduction to Statistical Modeling in R</a> and <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-statistics-in-spreadsheets">Introduction to Statistics in Spreadsheets</a>.</p>
</li>
<li><b>Machine Learning Engineer:</b><br>
<p>Machine learning engineers are another group of specialized engineers who are focused on researching, building, and designing artificial intelligence (AI) and machine learning (ML) systems to automate predictive models. Basically, they develop algorithms that use input data and leverage statistical models to predict an output, while continuously updating outputs as new data becomes available. DataCamp has numerous courses related to machine learning which you may find useful such as these: <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/machine-learning-with-caret-in-r">Machine Learning with caret in R.</a></p>
</li>
</ol>
<p>A quick job search online can give you an idea as to which of the data science career paths listed above is the most in demand. The graph below shows the job openings per career path according to <a target="_blank" href="https://www.indeed.com/jobs?q=data%20scientist&amp;vjk=ecfd94ad120e8423">Indeed USA</a> as of December 8, 2021.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image1_hntxle.png" /></center>

<p>Data architect appears to be the most in demand data science career path, due to their importance in creating data management systems which the other data science professionals then use. Next on the list is machine learning engineer, given the importance of utilizing artificial intelligence to predict outcomes in a lot of tech companies.</p>
<p>The least in demand are statisticians, mainly because a lot of the traditional statisticians are now becoming data scientists. This trend is expected to continue given how easy it is for statisticians to move from pure statistics to data science as they already possess the fundamental knowledge needed to become a fully-fledged data scientist.</p>
<h1 id="data-science-is-not-slowing-down">Data science is not slowing down</h1>
<p>The field of data science rose to immense popularity in the 2010s, especially when “Data Scientist” was touted by Harvard Business Review as the “sexiest job of the 21st century.” It became so popular that the title “Data Scientist” became a ubiquitous and trendy expression. While the term “sexiest job” does sound ridiculous at first, it mostly refers to how data scientists are not only in demand, but are also expensive to hire and difficult to retain.</p>
<p>While there is no question that data science is extremely popular nowadays, the better question is, will it remain this popular in the future? Based on employment projections, that does seem to be the case. Data from the 2020-2030 Employment Projections of the <a target="_blank" href="https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm">U.S. Bureau of Labor Statistics</a> shows that data science careers, which include statistics, data science, and other mathematical and science based occupations such as data engineering, will exhibit a very high growth rate in terms of percent change from 2020 to 2030. Statisticians ranked 14th overall, while data scientists and other mathematical science occupations ranked 31st overall out of 790 jobs included in the data.</p>
<p>While the shares of statisticians and data scientists from the total labor force are small compared to other occupations, those numbers are expected to increase in following years as the data science career path becomes more and more popular. The figure below shows how statisticians, data scientists, and other mathematical science occupations stack up against other jobs with high projected growth.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image3_bwn60b.png" /></center>


<h1 id="a-really-expensive-field">A really expensive field</h1>
<p>One major reason why data science is popular is because it is one of the highest paying jobs in the world. The figure below shows the range of salaries for 10 different occupations–including data scientists and data analysts–in New York City. The data was obtained from <a target="_blank" href="https://teleport.org/cities/new-york/salaries/">Teleport</a>, a website which aggregates data on living conditions (e.g., salaries) in different cities.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image2_qhdjq6.png" /></center>

<p>According to Teleport, data scientists had the 4th highest annual median salary in New York City at \$114,105, just behind C Level Executives and healthcare workers. In fact, in some other cities like Manila, data scientists rank as high as 2nd highest, just behind C Level Executives. This speaks volume as to how lucrative this job is. Data analyst jobs, whilst not as lucrative as data science positions, are still not to be slighted. At an annual median salary of $61,818, data analysts still earn around the average household income in New York City.</p>
<h1 id="ever-growing-in-the-job-market">Ever growing in the job market</h1>
<p>Another major reason why data science is popular is because of how organizations these days integrate the principles of data science in their day-to-day operations. The graph below shows the top 10 industries that involve data science in their work. The sample data of 8,000 data science oriented companies are extracted from <a target="_blank" href="https://www.diffbot.com/products/extract/testdrive/?token=210ced42a6e6d0cc4ac7f15885a94233.">Diffbot</a>, the world’s largest knowledge graph.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image4_spyov2.png" /></center>

<p>It comes to no surprise that Software Companies–including tech companies like Google, Apple, and Uber–came out on top. After all, the exceptional growth of big data was caused by the creation of the internet, which is closely tied to software and technology. Data science really is about processing a lot of information using various tools and technology.</p>
<p>Next on the list are financial services companies, which has been brought about by the rise of fintech companies. A portmanteau of “finance” and “technology,” fintech companies integrate technology and innovation in their services and offerings to improve their delivery to customers and disrupt traditional financial services. Since it involves handling a vast amount of data such as customer information, financial services companies saw the potential of data science tools to help streamline and optimize their processes and improve their services.</p>
<h1 id="in-conclusion-is-it-worth-pursuing-a-career-in-data-science-">In conclusion, is it worth pursuing a career in data science?</h1>
<p>With everything that has been explained above, the answer is a definite yes! As to which specific data science career path you should pursue, this depends primarily on your individual strengths and general interests. What is important is that any of the careers mentioned above are sure to be worthwhile.</p>
<p>A career path which is very lucrative and appears to show no signs of slowing down in terms of popularity, data science will continue to shape and influence how businesses and organizations operate for many years to come.</p>


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