<p>Table of Contents</p>
<ol>
<li>Introduction: the Data Analyst role     </li>
<li>From 9 to 5: A Day in the Life of a Data Analyst in 2022</li>
<li>Most Common Tools for Data Analysts</li>
<li>Where you can Learn these Skills</li>
<li>Other Important Skills for a Successful Career as a Data Analyst</li>
<li>Resume &amp; Interviewing: Data Analyst Job Search Prep</li>
<li>Building a Career</li>
<li>Get Started Today</li>
</ol>
<p><strong>Data analysis can offer you job security, the potential for remote work, high pay, and interesting challenges… intrigued?</strong></p>
<p>Careers in data analysis have become very popular in the last 5-10 years for all of these great reasons. While analysts have existed in organizations for years, new tech tools and a major influx in data collection in organizations is reigniting serious interest in this career path.</p>
<p>From tracking sales and orders, to customer and stakeholder management, data is an integral part of every organization. “The use of analytics by businesses can be found as far <a target="_blank" href="https://www.dataversity.net/brief-history-analytics/#">back as the 19th century;</a>” however, as more of the world gets online, making data meaningful has grown to become a critical part of most modern businesses&#39; survival.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image3_ygyck1.png"></centre>
<centre> <a target="_blank" href="https://www.datasciencecentral.com/data-science-job-market-shrinking-not-so-fast/">Ref: Data Science Job Market Shrinking?</a> </centre>


<p>This new environment, combined with the rise of Massive Open Online Courses (MOOCs) and other open courses, has created a new market for data analytics careers. While graduate programs and certificates are rapidly appearing, professionals still have the chance to make their way into this field from diverse disciplines, using open courseware and self-paced material available online.</p>
<p>Makeup of education for Data Analysts in the US in 2021:</p>
<p><centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image2_vaxjpb.png"></centre>
Source: <a target="_blank" href="https://www.zippia.com/data-analyst-jobs/demographics/">zippia</a></p>
<p>Specialists from all backgrounds have found lucrative opportunities in this field by focusing on the core skills needed to solve business problems, and support non-technical managers.<br>While this role is still rapidly evolving, many things have remained the same: the need for critical thinking, the ability to collaborate with and present to diverse audiences, subject matter expertise, and an understanding of the fundamental need for each step of the data analytics process. We will cover all of these areas in detail in the rest of this article.</p>
<p>##From 9 to 5: A Day in the Life of a Data Analyst in 2022##
<strong>Choose your own adventure!</strong> Most modern data analyst roles fall into one of two categories: you’re either the “data person” in a non-technical department, or an analyst in a technical data-focused department, working alongside other analysts, engineers, and scientists. Your days will look very different depending on the type of data analyst you are.</p>
<p><strong>When you are “the data person” in a non-technical department</strong>, you will collaborate with your manager and colleagues every day. From gathering, cleaning, and analyzing data, you will support their efforts to make data-driven decisions. These roles usually heavily focus on <a href="https://www.analyticsvidhya.com/blog/2021/06/must-known-data-visualization-techniques-for-data-science/">reporting and visualizations</a>, so you’ll likely <a target="_blank" href="https://www.datapine.com/blog/data-dashboards-definition-examples-templates/">manage a dashboard</a> or present regularly to a manager or non-technical colleague. The goal of these roles is to optimize existing data flows to augment subject matter expertise.</p>
<p>While this role has been represented for decades in business and other organizations, increasingly, management has been prioritizing these hires. Where an experienced manager&#39;s intuition used to reign, demonstrable proof is now the standard for important decisions. The more you can understand your organization’s motivations, the more success you will have in this role.</p>
<p>Critical thinking, technical skills which focus on managing visualizations for large datasets, and knowledge of <a target="_blank" href="http://makemeanalyst.com/basic-statistics-for-data-analysis/">basic statistics</a> are all key skills for this type of role. This is also one of the most collaborative types of data analytics roles, so diplomacy, <a href="https://hackernoon.com/the-ultimate-guide-to-strategic-thinking-for-data-scientists-g4j3uti">strategic thinking</a>, and project management skills are also beneficial.</p>
<p>Your days will be filled with these conversations and collaborations, with your priority being ownership and maintenance of the relevant core datasets which inform your department’s actions. This type of data analyst role is great for learning more about how an organization functions and deepening your specific subject matter expertise. Since it leans heavily towards a more traditional business or managerial function, these roles will follow organizational trends: expect to leave when your colleagues do (5pm), and relationships and management knowledge will be more critical to your career growth than increased technical skill.</p>
<p><strong>When you’re on a core data team</strong>, life will look very different. These teams tend to manage the data for an entire organization, and report directly to executive leadership, who likely have a forward-thinking, tech-savvy perspective.</p>
<p>Often, these teams act as consultants for the rest of the organization: gathering requirements, developing data pipelines, and serving dashboarding tools for analysts on these non-technical teams to manage. Work will likely be a mix of accommodating legacy systems, along with experiments with cutting edge technology. Your work will focus on understanding data storage, pipeline, and analysis best practices so there can be some consistency throughout the entire organization. SQL skills will be indispensable in this type of role, since you’ll be treated as an expert on specific datasets and the overall system across the organization. Being able to quickly verify a non-technical stakeholder’s ideas or concerns will make you a standout on this team.</p>
<p>If you’re in a large organization, expect to find yourself learning a lot about databases, and the challenge of uniting legacy tools with modern systems and expectations. On these teams, your key stakeholders will be: solution architects, who manage the organization’s overall data storage; systems analysts, and software engineers; along with SCRUM or other types of project managers. Since this role errs on the technical side, your career growth will largely depend on advanced skill in a specific technology (most likely SQL), and excellent people and project management skills.</p>
<p>While these two descriptions cover the experiences in most data analyst roles, there are still a few niche data analysts whose lives are even more different: <a target="_blank" href="https://hevodata.com/learn/marketing-data-analyst/#:~:text=Marketing%20Data%20Analysts%20are%20the,impact%20of%20their%20Marketing%20Strategies.">marketing data analysts</a>, <a target="_blank" href="https://jobdescriptionandresumeexamples.com/financial-data-analyst-job-description-key-duties-and-responsibilities/">financial data analysts</a>, and <a target="_blank" href="https://www.cleverism.com/job-profiles/product-analyst/">product data analysts</a>.</p>
<h2 id="most-common-tools-for-data-analysts">Most Common Tools for Data Analysts</h2>
<p>In most organizations, <a target="_blank" href="https://docs.microsoft.com/en-us/learn/certifications/mos-excel-expert-2019/">advanced Excel</a> skills will never go out of style. Regardless of how high tech or innovative your company is, a spreadsheet will never stop being a part of your workflow. It’s an easy way to share your data with non-technical peers, and do some meaningful analysis on the fly. Microsoft offers certifications for advanced Excel skills, and there are online courses to prepare for the required exam. Online courses and certifications are a great way to learn the most important and common functions of Excel.</p>
<p>Next, <a target="_blank" href="https://searchdatamanagement.techtarget.com/definition/SQL#:~:text=SQL%20(Structured%20Query%20Language">structured query language (SQL)</a>%20is,on%20the%20data%20in%20them.&amp;text=SQL%20became%20the%20de%20facto,late%201970s%20and%20early%201980s.) is the most important code you will write as a data analyst. SQL has stood the test of time, and been the core tool for data retrieval for many years. Some methods to focus on when you’re just starting to learn are SELECT, JOIN, and HAVING.</p>
<p>Ongoing reporting and visualization is key in most data-related jobs. <a target="_blank" href="https://www.tableau.com/">Tableau</a>, <a href="https://looker.com/">Looker</a>, and <a href="https://powerbi.microsoft.com/en-us/">Power BI</a> are some common tools which can be used for the most common KPIs across organizations. They all have powerful technical capabilities, but are user-friendly enough for non-technical managers. They’re listed from greatest to least in terms of price; you’ll often find Looker and Power BI in departments or organizations where budgets are smaller.</p>
<p>When data gets too big and messy, <a target="_blank" href="https://www.python.org/">Python</a> &amp; <a target="_blank" href="https://jupyter.org/">Jupyter Notebooks</a> are another tool for data analysts. We’re nearly entering data science territory with these tools - most data analyst jobs will not center this approach because it takes a high level of skill to be able to make python more valuable than the tools listed above. However, by using jupyter notebooks or google colab, you’re more able to collaborate with others, do more complex analysis, and demonstrate an entire narrative of analysis and visualization in one place. You’re likely to see these tools required for marketing data analyst roles, which tend to focus on A/B Testing.</p>
<p>While many claim there is still debate over which language is the best for data analytics, <a target="_blank" href="https://www.upgrad.com/blog/data-science-programming-languages/">Python has consistently outstripped R as the most popular language</a> both overall, and in the private sector.</p>
<p><centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image1_bvenrw.png"></centre>
<a target="_blank" href="https://towardsdatascience.com/which-programming-language-should-data-scientists-learn-first-aac4d3fd3038">Ref: Which Programming Language Should Data Scientists Learn First?</a></p>
<p>However, if R seems more appealing, or you have experience with it from work in academia, you still can’t go wrong. If you’re fluent in one language, being able to program in the other is a small challenge. <strong>What’s most important to consider when choosing a programming language for data analysis is that you feel comfortable</strong> and able to dedicate yourself to it entirely. For these tools to make sense in a workplace or your career, you need strong proficiency and know-how to navigate results.</p>
<h2 id="where-you-can-learn-these-skills">Where you can Learn these Skills</h2>
<p>Data Camp is an excellent place to start.</p>
<p>Data Camp has both skill and career tracks which can guide you through all the tools and best practices necessary to break into a career in data analytics: data analyst with R, data analyst with Python, and data analyst with SQL.</p>
<p>Otherwise, here are a few other places you can start your learning journey:</p>
<ul>
<li><a target="_blank" href="https://app.datacamp.com/learn/courses/introduction-to-sql?utm_source=adwords_ppc&amp;utm_medium=cpc&amp;utm_campaignid=1565261447&amp;utm_adgroupid=63048150921&amp;utm_device=c&amp;utm_keyword=free%20sql%20courses&amp;utm_matchtype=e&amp;utm_network=g&amp;utm_adpostion=&amp;utm_creative=295185739362&amp;utm_targetid=aud-299261629574:kwd-300479289579&amp;utm_loc_interest_ms=9026841&amp;utm_loc_physical_ms=9043090&amp;gclid=CjwKCAiAz--OBhBIEiwAG1rIOq9900hmaq8oQSZO9RhJAyUUmf0pL0cYp8lorXx1lPWX9AR6bSUw8xoCnGQQAvD_BwE">SQL</a></li>
<li><a target="_blank" href="https://www.learnpython.org/">Python</a></li>
<li><a target="_blank" href="https://www.coursera.org/learn/r-programming">R</a></li>
<li><a target="_blank" href="https://www.edx.org/learn/excel">Advanced Excel</a></li>
<li><a target="_blank" href="https://www.udacity.com/course/data-visualization-in-tableau--ud1006">Tableau</a></li>
</ul>
<h2 id="other-important-skills-for-a-successful-career-as-a-data-analyst">Other Important Skills for a Successful Career as a Data Analyst</h2>
<p>Despite all the tech that comes with this career, data analytics takes much more than specialized computer skills to be successful. Being able to successfully integrate within an institution through organizational knowledge, relationship building, and business best practices will help you avoid stagnation. A few key areas include:</p>
<ul>
<li><a target="_blank" href="https://www.atlassian.com/agile/product-management">Product management</a></li>
<li><a target="_blank" href="https://www.pmi.org/about/learn-about-pmi/what-is-project-management">Project management</a></li>
<li>Collaborating with stakeholders</li>
<li>Becoming a subject matter expert (SME)</li>
<li>Business administration/organizational awareness for public, NGO, and academic work</li>
</ul>
<h2 id="resume-interviewing-data-analyst-job-search-prep">Resume &amp; Interviewing: Data Analyst Job Search Prep</h2>
<p>Here are some of the most common themes in interviews for data analyst roles:</p>
<ul>
<li>SQL skills<ul><li> Prepare to be tested on the fundamentals in many technical interviews</li>
<li>Understand JOINs, HAVING, etc.</li></ul>
</li>
<li>Tableau<ul><li> Prepare to be asked about past dashboards you’ve created - having a <a target="_blank" href="https://public.tableau.com/en-us/s/">Tableau Public</a> profile with a few examples will be sure to impress your interviewer</li>
</ul></li>
<li>Questions about your experience:<ul><li> Describe a project where you had to collaborate with non-technical stakeholders</li>
<li> Describe a situation where there was low data quality, but a stakeholder still wanted analysis completed/a decisive outcome which you couldn’t give</li>
<li>Describe a data workflow you’ve worked with in the past (tools, process, stakeholders, visualizations, etc.)</li>
</ul></li>
</ul>
<p>Like all interviews, the <a target="_blank" href="https://www.indeed.com/career-advice/interviewing/how-to-use-the-star-interview-response-technique">STAR method</a> is a solid approach to all your responses. As it is a technical role, you will have to continuously educate yourself on new tools and processes. Admitting you don’t know something, but are willing to find an answer, demonstrates an understanding of what’s required for the day-to-day work of a data analyst.</p>
<p>Many major companies hire and pay well for experienced Excel and SQL developers. These are some core technologies that will never go out of style. There are data analysts who’ve become experts in these two tools alone and built highly successful careers for themselves.</p>
<p>Regardless of your approach, be sure to list all technologies you have knowledge of or experience with, so your interviewer knows to ask you about anything relevant. Just because they are not using a specific technology now, does not mean they won’t in the near future, and vice versa. Be transparent about your skills and show them what you can do.</p>
<h2 id="building-a-career">Building a Career</h2>
<p>Choosing to build a career right now as a data analyst is a great choice. Our world is in the middle of a great AI “arms race” between major economies, and the need for data-savvy professionals is only going to grow.</p>
<p>If working in the private sector doesn’t interest you, there are plenty of other opportunities to apply your skills to something more meaningful. Here are a few examples:</p>
<ul>
<li><a target="_blank" href="https://online.hbs.edu/courses/big-data-social-good/">Data for social good</a></li>
<li><a target="_blank" href="https://datarade.ai/data-categories/esg-data">ESG/sustainability reporting</a></li>
<li><a target="_blank" href="https://www.scmp.com/yp/learn/college-uni-life/university-programmes/article/3071524/how-data-analytics-helped-obama-win">Digital media strategy used in Obama’s presidential campaign</a></li>
</ul>
<h2 id="get-started-today">Get Started Today</h2>
<p>If the idea of solving problems, collaborating with diverse people, and making a difference in any organization you’re a part of motivates you, consider becoming a data analyst.</p>
<p>From critical thinking, to database knowledge and strong people skills, you’d be investing in a high value career with endless potential. Learning these skills will take commitment and focus. New technologies are developed constantly, but focusing on the core skills mentioned in this article will ensure that you have a strong foundation and long career.</p>
<p>Our best advice is to use the resources that are available online today. There are excellent programs such as Data Camp, which are developed by experts and experienced professionals who have faced these challenges firsthand. Trust them and the learning process, and you’re sure to find your version of success. Best of luck, we look forward to seeing you in the data analyst community!</p>


https://www.datacamp.com/community/blog/blog-data-analyst-skills-for-career-success

Explore data analysis as a career and how to up-skill for the role. 

Learn about the data analyst job roles and the skills required to ace them.

Data Analyst Skills for Career Success

<h2 id="introduction">Introduction</h2>
<p>Data science is everywhere right now. One after the other, companies around the world are turning to data science to solve the most diverse problems out there. This situation has put data scientists in an advantageous position regarding employment and payment.</p>
<p>Naturally, a lot of people are becoming interested in learning how to become a data scientist. If you are reading this article, you may be one of those people. Here, we will show you an effective and attractive way to learn what you need to get started in the data science world.</p>
<h2 id="data-scientist-learning-path">Data scientist learning path</h2>
<p>The path to becoming a data scientist is long. You need to master a wide set of skills such as coding, math, and communication. In this article, we propose a checklist for those who are considering data science as a good choice for a professional career. The checklist is for beginners, yet if you are not quite a beginner you may still gain some useful insights.</p>
<p>The main requirement to become a good data scientist is to be passionate about what you do. It will consume a lot of time to learn all that you need and to complete projects. Hence, the first step is to make sure you enjoy working with data science.</p>
<h3 id="1-find-whether-data-science-is-for-you">1: Find whether data science is for you</h3>
<p>We strongly recommend finding out about where data scientists can work and what their average work day might look like. Logistics aside, knowing whether data science is for you requires getting your hands on data and analyzing it. It could be a good start to read some articles about <a target="_blank" href="https://www.datacamp.com/community/blog/what-does-a-data-scientist-do">the tasks that a data scientist does</a>.</p>
<p>You could think that to be a data analyst you would need to know advanced math or programming, yet making simple intuitive analysis of available data and arriving at conclusions can be enough.</p>
<p>You could consider reading some articles like this one on <a target="_blank" href="https://fivethirtyeight.com/features/gun-deaths/">gun deaths in the United States</a> or this one on <a target="_blank" href="https://fivethirtyeight.com/features/dissecting-trumps-most-rabid-online-following/">online communities supporting Donald Trump</a>. The data used in these articles is available to download and you can open it with a tool like Excel and analyze it. For example, you can question whether the article seems to be right and why. It is also a great exercise to think about other questions that the article doesn’t answer. If you think answering those questions is an interesting thing to do, you will likely enjoy data science.</p>
<p>Data science is used in a wide variety of industries. If you are not motivated enough, think about a field you are passionate about, maybe sports, the stock market, or health care. Try to do a little research about how data science is being applied in a field you’re interested in; try to read some articles and analyze the data related to them. This will give you an idea of the work carried out by data scientists and if it is a good fit for you. If you think it is, then it’s time to take the second step, learning to code.</p>
<h3 id="2-learn-to-code">2: Learn to code</h3>
<p>Humans are unable to make all the calculations required to process great amounts of data. We have computers to do that for us, but we need to know how to make computers do what we want them to do. For this reason, every data scientist needs to know at least one programming language.</p>
<p>The two main languages for data science are Python and R. Python serves a multitude of purposes, whilst R is a language just for doing data science.</p>
<p>There are a lot of resources out there to help you learn any programming language. For example, you can take the <a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Python</a> or <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r">R</a> introductory free courses by DataCamp. This way, you’ll create your working environment, and get used to the language syntax and the specific tools used to conduct data science in that language.</p>
<p>Don’t aim to be completely proficient in the language before advancing to the next step. Coding skills come with practice. Just try to learn the basics and understand the different elements of the language. Becoming an expert is something that takes time and will start with the next step.</p>
<h3 id="3-begin-making-projects">3: Begin making projects</h3>
<p>By now, you will likely have read some interesting articles and have a bunch of questions that you think are interesting too. You may also have basic knowledge about a language for data science and have written some code. It’s time to conduct your first data science projects.</p>
<p>If you feel you are not ready to start a project from scratch, I strongly recommend <a target="_blank" href="https://www.datacamp.com/projects">DataCamp’s guided projects</a>. This way you won’t be alone and you can start to gain some insights into data science projects. After that, you can start other projects that you are passionate about. It doesn’t matter if you are not doing anything new, or if you’re not convinced your conclusions or outcomes are particularly useful. Thinking about how good your solutions are is a great exercise. If you can identify the weaknesses of your methodologies and algorithms, you are on the right path to becoming an awesome data scientist.</p>
<p>No matter what projects you decide to do, you will always need data. Collecting the data is not always an easy task. You can look at <a target="_blank" href="https://datasetsearch.research.google.com/">Google Dataset Search</a>, <a target="_blank" href="https://www.kaggle.com/datasets">Kaggle</a>, and the <a target="_blank" href="https://archive.ics.uci.edu/ml/datasets.php">UCI Machine Learning Repository</a>, among other places.</p>
<p>Always approach your projects like something that will be published and needs to be understood by many people. These projects will be your main presentation card when you begin to look for a job or an internship. Take your time, add some documentation and try to get the code to be as clean as possible.</p>
<p>After doing a project, it’s time to let others know about it, get some feedback, and get more visibility.</p>
<h3 id="4-publish-your-projects-and-interact-with-others">4: Publish your projects and interact with others</h3>
<p>Publishing your progress is not just a great way to become more visible to potential employers, it is also the best way to learn and get more expertise. Both the feedback you get from other people and the effort you put into making others understand your work will help you to grow. Bear in mind that an important part of the data scientist’s job is to communicate your results to others.</p>
<p>Uploading your projects to GitHub, and starting to build a portfolio with them is a great way to practice communicating your findings to others. Sharing your progress on social media platforms like LinkedIn or Twitter is also recommended. If you want to go even further, you could start writing your blog to document your progress and to share what you have learned with others. There are other options to share with and learn from others, such as on <a target="_blank" href="https://www.reddit.com/r/datascience">Reddit</a>, <a target="_blank" href="https://www.quora.com/topic/Data-Science">Quora</a>, and on <a target="_blank" href="https://www.kaggle.com/discussion">Kaggle’s discussions</a>.</p>
<p>This step makes the difference between slow and fast progress in data science. There is a lot to learn, but learning it alone is a very difficult task.</p>
<p>At this point, you should have some basic knowledge of how the data science pipeline works and why. You will likely have gained some basic statistics and math knowledge during your journey so far, but it is maybe intuitive and poorly formalized. Having a solid theoretical background is very important and will let you face the most diverse and difficult scenarios. With the next step, you’ll complete the checklist to become a data scientist.</p>
<h3 id="5-gradually-gain-a-theoretical-understanding">5: Gradually gain a theoretical understanding</h3>
<p>This is the last step of the data scientist learning path. Trying to understand the theory at the beginning would be a mistake. With the development of several tools in recent years, doing data science is easier than ever. You don’t need to know the theory behind the algorithms to implement a decent solution. On the other hand, math, statistics, probability, and machine learning theory can be very hard and take time to understand; you may become discouraged if you don’t have a solid, pre-existing math background.</p>
<p>That’s why you should begin building things, focusing on learning to code and solving problems without paying too much attention to why and how those algorithms work. You might be amazed to find just how many projects you can complete this way. Only after building some projects will you be in the perfect situation to get a deeper understanding of the theory.</p>
<p>However, theoretical knowledge is what will set you apart from other data science practitioners. It will allow you to solve very complex and specific problems, validate your methodologies, and make better decisions.</p>
<p>You will need to learn about algebra, calculus, statistics, probabilities, optimization, and machine learning theory, among other fields. It is advisable to stay focused on your area of interest so you invest most of your time learning what you will really need in that area. DataCamp offers a lot of introductory materials in this regard. Also, there are more advanced courses available for free like the <a target="_blank" href="https://www.coursera.org/learn/machine-learning">machine learning course by Andrew Ng on Coursera</a>, which is free if you decide not to get the certificate. This <a target="_blank" href="https://www.udacity.com/course/intro-to-data-science--ud359">free data science course</a> on Udacity is also a good option for this stage.</p>
<p>Although these five steps will have set you up well for becoming a successful data scientist, this is not the end of your path. A data scientist should always be learning because this field is progressing at a mind-blowing pace. You should keep learning to write better code, exploring new tools and languages, building new projects, and learning yet other theoretical concepts.</p>
<h2 id="conclusions">Conclusions</h2>
<p>In this article, we proposed a five-step learning path to becoming a data scientist. We strongly recommend you focus on building and making a portfolio of projects. In this regard, I highly recommend the <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a>, a place when you can practice a wide variety of skills and build your portfolio through your DataCamp profile. This can even hold you in good stead when looking for jobs. Having a good theoretical understanding is very important, yet learning the theory should happen organically as you work on projects you are interested in. You should keep in mind that the data scientist learning path is way larger than five steps, so keep learning and building.</p>


https://www.datacamp.com/community/blog/blog-how-to-become-a-data-scientist

Find out if data science is for you, what courses to start with, how to build a portfolio and become a data scientist. 

How to Become a Data Scientist

https://www.datacamp.com/community/blog/how-data-culture-unlock-digital-transformation

Chief Data & Innovation Officer at Gulf Bank, Mai AlOwaish, outlines how a people and data-first approach can unlock the value of digital transformation for organizations across industries.

How Data & Culture Unlock Digital Transformation

<p>More and more people are breaking into data science every day. There are various reasons for this trend. First, booming demand: data science remains one of the top fastest-growing jobs on the market according to the <a target="_blank" href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">LinkedIn 2020 emerging jobs report</a>, despite the <a target="_blank" href="https://www.interviewquery.com/blog-data-science-interview-report/">slowdown of the industry</a> due to the COVID-19 pandemic.</p>
<p>Next, salary expectations: <a target="_blank" href="https://taylor-mark110.medium.com/demystify-data-scientists-salaries-in-2020-8c772baa1e15">Glassdoor 2020 statistics</a> show that the salary of an entry-level data scientist is about $89,000 USD. And finally, remarkable popularity: The Harvard Business Review declared in a memorable 2012 <a target="_blank" href="https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century">article</a> that data scientist was the sexiest job of the 21st century.</p>
<p>If you are considering starting a new adventure in the field of data science, don’t hesitate—go for it. To help you succeed in your journey, we have prepared a list of 10 lessons and practical tips that will help you navigate and find your place in the wonderful world of data science.</p>
<ol>
<li>Demystifying data science</li>
<li>What is a programming language?</li>
<li>In the beginning, it will hurt. But be patient.</li>
<li>You are not alone: data science resources</li>
<li>The art of coding</li>
<li>Where do I start learning?</li>
<li>Keep learning</li>
<li>Data science is a means to an end</li>
<li>With great power comes great responsibility</li>
<li>Conclusion: (You will) be data, my friend</li>
</ol>
<h2 id="demystifying-data-science">Demystifying data science</h2>
<p>Data science is often discussed alongside other tech buzzwords like big data, artificial intelligence and machine learning, making it difficult to get a clear understanding of what data science actually is.</p>
<p>In a few words, data science is an interdisciplinary field that combines scientific methods, programming, algorithms, and statistics to extract knowledge from data. Data science comprises a set of powerful tools and methodologies to deal with data that can be used in nearly every industry. The possibilities range widely, from basic data exploratory analysis and data collection techniques like web scraping, to some of the most valuable domain applications, such as recommendation engines, computer vision, autonomous cars, and natural language processing, where machine learning and deep learning play a critical role.</p>
<p>While data science is a natural choice for professionals with IT or programming backgrounds, the field is rapidly evolving, and today it is fair to say that everyone is welcome, no matter where you come from. The reason? As data science breaks into new disciplines, knowing the essential aspects of a certain area or field of inquiry is critical. In addition to technical and coding skills, data scientists should always have a certain degree of business domain expertise so they can understand what they are doing. This includes evaluating the input data, assessing the value and validity of the insights, and discerning what makes sense and what does not.</p>
<h2 id="what-is-a-programming-language-">What is a programming language?</h2>
<p>Learning to code is a necessary step to becoming a data scientist. There’s no alternative answer, despite the <a target="_blank" href="https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses">recent surge of “no-code” data science and AI platforms</a>. While these solutions allow non-technical business users to build applications and software (in an attempt to address the software developer skills shortage), the competencies, resources and mindset that data scientists provide are hardly replaceable, at least for now.</p>
<p>Programming is a central part of the daily life of a data scientist. But what is programming anyway? And what is a programming language?</p>
<p>Programming is the technique that allows the execution of automated tasks of a computer system. To communicate with a computer we use programming languages. A programming language is a set of semantic and syntactic rules that programmers use to write the instructions—also known as algorithms—for a computer to complete a specific task.</p>
<p>There are hundreds of programming languages. In data science, <a target="_blank" href="https://www.datacamp.com/community/blog/when-to-use-python-or-r">the two most popular programming languages are Python and R</a>. Both languages are great for any data science task you may think of. They are often portrayed as rivals, but a smarter approach is to see them as complementary languages; allies that can be combined to exploit their full potential and respective advantages. Fortunately,  <a target="_blank" href="https://app.datacamp.com/learn/courses">DataCamp has a large catalog of courses</a> where you can learn both Python and R.</p>
<h2 id="in-the-beginning-it-will-hurt-but-be-patient-">In the beginning, it will hurt. But be patient.</h2>
<p>Let&#39;s be honest: learning to code is hard. This statement applies to every person, no matter your background. It’s time to stop thinking that a person who studied computer science or math is a better-suited candidate for data science than a liberal arts graduate. The only difference between them is that the former probably started programming in college and the latter likely did not. But be sure that the former also struggled with coding at some point, especially at the beginning.</p>
<p>Coding is like going to the gym. The first days it hurts. Your muscles feel sore and stiff. You are not used to that pain and, while laying on the couch, you may be tempted to quit. But if you don’t give up, if you keep exercising, things will gradually improve. After some weeks, you will find yourself beating fitness milestones that seemed unattainable not long ago. Eventually, going to the gym will become a part of your routine, and one day you will realize you enjoy working out.</p>
<p>For most babies, it takes between nine and 14 months to start talking. Fortunately, a programming language is much simpler and more rudimentary than a human language. If you are determined, you should be able to write basic scripts within a few months. Just like going to the gym, you have to be patient when learning a programming language.</p>
<h2 id="you-are-not-alone-data-science-resources">You are not alone: data science resources</h2>
<p>Your data science adventure will be full of obstacles. You may get stuck while writing your code, sometimes you will not understand why your script is not running properly, and there will be times when you simply have no clue how to start a certain data science task.</p>
<p>No need to stress: you are not alone. One of the coolest things in data science—and, more broadly, the programming ecosystem—is that the internet is full of resources and information that can help you overcome the challenges you may encounter. You just have to ask the right questions to get the right answers.</p>
<p>Here is a list of resources that will come to your rescue throughout your data science journey:</p>
<ul>
<li><p>Stack Overflow: The Oracle of Delphi for programmers. With more than 16 million users, <a target="_blank" href="https://stackoverflow.com/">Stack Overflow</a> is a question and answer public platform for programmers. If you have a problem with your Python or R script, you will likely end up looking for solutions on Stack Overflow.</p>
</li>
<li><p>Tutorials: Troubles with regression analysis? Don’t know where to start with web scraping? Reading a tutorial on the subject can be a great starting point. You can find comprehensive tutorials on a large range of topics on well-established platforms, such as <a target="_blank" href="https://www.datacamp.com/community/tutorials">DataCamp</a>, and even YouTube.</p>
</li>
<li><p>Online courses: If you want to become a subject expert, sharpen your coding skills, or just want to broaden your data science horizons, a course is probably what you are looking for. There are many options on the market, including DataCamp. Don’t miss the opportunity to explore our large list of <a target="_blank" href="https://app.datacamp.com/learn/courses">courses</a>.</p>
</li>
<li><p>Data science books: Books will always be a great source of information. A growing number of data science books have been published over the last years, and many of them can be found online for free. Here’s a recommendation: <a target="_blank" href="https://www.oreilly.com/search/?query=data&amp;extended_publisher_data=true&amp;highlight=true&amp;include_assessments=false&amp;include_case_studies=true&amp;include_courses=true&amp;include_playlists=true&amp;include_collections=true&amp;include_notebooks=true&amp;include_sandboxes=true&amp;include_scenarios=true&amp;is_academic_institution_account=false&amp;source=user&amp;sort=relevance&amp;facet_json=true&amp;json_facets=true&amp;page=0&amp;include_facets=true&amp;include_practice_exams=true">O’Reilly books</a>.</p>
</li>
<li><p>Documentation: Last but not least, we need to mention package documentation. Documentation is one of the most important aspects of a good package or library. Documentation is the primary source for users to understand the aim of a package and how it works. Although it may not be your most exciting read, in many cases the solution to your problem will be a package function or parameter you were not aware of until you read the documentation.</p>
</li>
</ul>
<h2 id="the-art-of-coding">The art of coding</h2>
<p>After some months of coding workouts, you will feel more confident with your skills. During this time you will have internalized a lot of processes, syntax, and commands. Without noticing, coding automatisms will show up, making your scripting more fluid.</p>
<p>Eventually, you will start seeing the big picture: you will discover that coding is an art. For example, you will understand that there are many ways to solve a programming problem, but some are more efficient than others. Indeed, the search for efficiency will guide your work, both in terms of writing and running your code. This will lead you to learn new programming strategies.</p>
<p>Another important aspect you will start addressing is readability. Remember that readability is not only important for other programmers who may have to deal with your scripts, but also for your “future you.”  By making tiny changes in some elements, such as syntax structure, variable and function naming, and spacing and indenting, your code can look better and be more understandable. Also, making comments on your code and documenting your functions will make life much easier for you and other readers.</p>
<h2 id="where-do-i-start-learning-">Where do I start learning?</h2>
<p>We previously defined data science as an interdisciplinary field that combines scientific methods, programming, algorithms, and statistics to extract knowledge from data. Yes, that’s a lot to start with. Data science is such a demanding field and it’s easy for newcomers to get overwhelmed. If this is a comfort, know that every person breaking into data science, irrespective of their background, has to go through a certain learning process: it’s simply impossible to know everything straight away.</p>
<p>Where does one start, then? There is not a single answer for this, but you won’t get very far in your career without a foundation in programming, statistics, and math. Regarding programming, besides Python or R, knowing SQL is certainly a must. As for math and statistics, don’t be scared. It may take more time, but you will learn what a p-value or an artificial neural network is in your own time.</p>
<p>In the meantime, you can do other things to increase your visibility and chances of getting hired. A great idea is to create a portfolio. In this regard, we highly recommend trying our recently launched <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a>, an online environment to write code, apply your skills by analyzing interesting datasets, and build out your data science portfolio. In addition, you could write articles about the field, participate in <a target="_blank" href="https://www.datacamp.com/data-science-competitions">data science competitions</a>, or take data science certifications.</p>
<h2 id="keep-learning">Keep learning</h2>
<p>If you think that finding your first data science job is the end of the adventure, you’re wrong. Data science is a dynamic and rapidly evolving field. What is popular today can be obsolete tomorrow. To get an idea of the state of the data science landscape, see this picture.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643738465/image1_qike4y.jpg"></centre>
<centre>Source: <a href="https://mattturck.com/data2021/">FirstMark</a></centre>

<p>We can draw two conclusions from this image. First, data science is a lifelong learning process. You must keep learning or you risk becoming obsolete.</p>
<p>Second, it’s impossible to know all the programming languages and technologies out there. So choose what to learn according to the needs in your job and what you are most passionate about.  </p>
<h2 id="data-science-is-a-means-to-an-end">Data science is a means to an end</h2>
<p>What’s the value of your work as a data scientist if you can’t communicate the relevance of the projects you’re working on? What’s the point of doing a deep and thorough data analysis if nobody understands what you’re doing?</p>
<p>The goal of data science is to extract insights from data and apply those insights to create value. In other words, data science is not an end in itself, but a means to create value. Skills such as good communication, storytelling, and creative thinking are key to translating insights into value. In this regard, as mentioned above, the industry can benefit from non-technical skills.</p>
<h2 id="with-great-power-comes-great-responsibility">With great power comes great responsibility</h2>
<p>This is probably the most important lesson. Data science is behind some of the most valuable applications and inventions in our lives. Our societies are rapidly changing, propelled by the disruptive force of data science and artificial intelligence, <a target="_blank" href="https://gowlingwlg.com/getmedia/8f0022f4-3021-4e02-bc1d-4adc9bb8cd1e/Tides-of-Disruption-How-to-navigate-business-transformation-Gowling-WLG.pdf.xml#:~:text=This%20report%2C%20Tides%20of%20Disruption,countries%20are%20for%20that%20change.">among other technologies</a>.</p>
<p>In this context of uncertainty and deep changes, it’s important to stay critical and cautious. Our daily work as data scientists involves dealing with tons of data, building models, and translating insights into value, but we should always try to go beyond our computers and question ourselves about the societal impacts of our work.</p>
<p>Being critical and accountable is the first step to preventing industry abuses and ensuring a fair future.</p>
<h2 id="conclusion-you-will-be-data-my-friend">Conclusion: (You will) be data, my friend</h2>
<p>To conclude this post, we want to share with you one last lesson: data science is not only changing society, but it will also change your life forever.</p>
<p>The world we live in is complex. We constantly deal with processes and systems that are beyond our comprehension. To address that complexity, we work with  models, which can be defined as a simplified description of reality. In this vein, data science provides a good number of models that can help us understand our world. For example, the relational model for database management can be very handy to structure information and describe complex phenomena. Social network models can help us understand how information flows from person to person or exploit the potential of the networks we are part of. Even more fascinating, the mathematical models that power machine learning applications are not only crucial for computers to learn, they also provide new perspectives to look at human intelligence, and thus to gain a deeper understanding of ourselves.</p>
<p>Finally, there’s another reason why data science will change you. We live in the age of Big Data. Tons of data is created and collected every day. Data is nothing but information of many types and sources. Reading allows us to learn from books and other source texts, and data literacy allows us to learn from data. This is quite a thing, as data offers unprecedented ways to address the study of any domain you may think of, from medicine, psychology, and art, to climate change, astronomy, and history.</p>
<p>Data science provides you with the tools to analyze data—what you do with them relies solely on your imagination and curiosity. So let’s continue the adventure: out there there is a whole world waiting to be (re)discovered by you. Make a start by <a target="_blank" href="https://www.datacamp.com/?utm_source=adwords_ppc&amp;utm_medium=cpc&amp;utm_campaignid=15903051076&amp;utm_adgroupid=131825743429&amp;utm_device=c&amp;utm_keyword=datacamp&amp;utm_matchtype=e&amp;utm_network=g&amp;utm_adpostion=&amp;utm_creative=575358952028&amp;utm_targetid=kwd-297372810188&amp;utm_loc_interest_ms=&amp;utm_loc_physical_ms=1007439&amp;gclid=Cj0KCQiAi9mPBhCJARIsAHchl1zQdlQ2of_WoSWSK53iN8pxOzfXz_JrKXWkg2VX5QDB6ObP0R-mrXMaAk_kEALw_wcB">building your data skills online</a>.</p>


https://www.datacamp.com/community/blog/ten-lessons-for-future-data-scientists

Considering a new career in data science? This guide will prepare you for the journey.

A guide to starting your career in data science and improving on skills. 

Ten Lessons for Future Data Scientists 

<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 a recent episode of <a target="_blank" href="https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need">DataFramed</a>, Andy Cotgrave, technical evangelist at Tableau, shared the importance of data storytelling in driving change with analytics. In this two-part blog post, we deep dive into seven concrete tips that Andy provided on telling better data stories.</p>
<p>Read <a target="_blank" href="https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-1">Part I</a> if you haven't already!</p>
<h2 id="4-use-a-narrative-structure-in-data-stories-">4. Use a narrative structure in data stories.</h2>
<p>Stories generally follow a common arc – the protagonist faces a challenge, goes on to resolve it, then returns to normalcy. An example of this arc is the Hero’s Journey.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320567/image8_grp6ly.png" />Figure 1: The hero’s journey</center><br>

<p>The audience is <a target="_blank" href="https://www.forbes.com/sites/kateharrison/2015/01/20/a-good-presentation-is-about-data-and-story/?sh=7599eca6450f">22 times more likely</a> to remember a fact when told a story. Clearly, data visualizations stand to benefit from adopting the age-old narrative structure.</p>
<p>Adopting the narrative structure, a data story starts with a background on the current situation, continues with evidence that builds up to the central insight, and ends with a call to action. (Figure 2)</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320742/image4_nnm9k9.png" />Figure 2. The narrative structure of a data story</center><br>


<p>An excellent example of a data story, the <a target="_blank" href="https://www.gatesfoundation.org/goalkeepers/report/2021-report/#LongTermInvestmentsPayOff">Goalkeeper’s Report by the Gates Foundation</a> demonstrated how the pandemic exacerbated inequality. (Figures 3A, 3B, and 3C)</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320251/poverty_s2wd18.gif" />Figure 3A: To set the scene, the data story first established the impact of COVID-19 on poverty-stricken countries. [<a target="_blank" href="https://www.gatesfoundation.org/goalkeepers/report/2021-report/#LongTermInvestmentsPayOff">Source</a>]</center><br>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320508/image7_bmdtdx.png" />Figure 3B: The data story provided rising insights by highlighting the widening chasm between the haves and the have-nots pre-pandemic and post-pandemic.</center><br>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319219/image2_lyw5ro.png" />Figure 3C: The data story closes with a call to action–for greater investments in women, communities, and innovations.</center>

<h2 id="5-adapt-data-stories-to-the-medium">5. Adapt data stories to the medium</h2>
<p>It is tempting to reuse existing charts across multiple mediums. Data storytellers who do so are doing themselves a disservice.</p>
<p>Charts that are not optimized for their medium might fail to deliver their message effectively. A chart originally designed for email might contain minuscule texts that cannot be read on a screen.</p>
<p>The Economist portrayed how data stories can be adapted from print media to social media. The printed chart (designed for serious readers) was dense in information. It was streamlined for Instagram (designed for casual scrollers).</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320591/image5_qjtkiu.png" />Figure 4: The Economist’s printed chart (left) is adapted for social media (right) [<a target="_blank" href="https://medium.economist.com/charting-new-territory-7f5afb293270">Source</a>]</center>

<h2 id="6-declutter-your-data-stories">6. Declutter your data stories</h2>
<p>Clutter makes data stories harder to read. The additional cognitive load distracts the audience from the main message.</p>
<p>The onus is on the data storyteller to identify and remove superfluous elements that do not contribute to the intended message. The result of decluttering is striking.</p>
<p>In particular, our whitepaper “<a target="_blank" href="https://www.datacamp.com/resources/whitepapers/8-rules-for-better-data-storytelling">8 Rules for Better Data Storytelling</a>” suggests data storytellers to:</p>
<ul>
<li>Remove chart borders</li>
<li>Remove gridlines or axes</li>
<li>Clean up axis labels</li>
<li>Label data directly (as opposed to using a legend)</li>
<li>Remove data markers</li>
<li>Use special effects (bold, underline, italics, shadows) sparingly</li>
</ul>
<h2 id="7-explain-data-stories-in-stages">7. Explain data stories in stages</h2>
<p>When presenting complex data stories, one practical tip is to reveal the elements of the chart gradually. When the information is presented in digestible pieces, the audience is more likely to engage with the presentation.</p>
<p>A prime example is the talk “<a target="_blank" href="https://www.youtube.com/watch?v=jbkSRLYSojo">The Joy of Stats</a>” by Hans Rosling. Watch how Rosling masterfully told his data story.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/jbkSRLYSojo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<h2 id="data-storytelling-is-here-to-stay">Data storytelling is here to stay</h2>
<p>Data storytelling will soon <a target="_blank" href="https://www.forbes.com/sites/brentdykes/2020/01/22/why-data-storytellers-will-define-the-next-decade-of-data/?sh=56a32d24fb9c">become a necessary skill set</a> for all in the coming decade as data insights become a cornerstone of organizations. To learn more tips and tricks on data storytelling, listen to Andy’s podcast, or learn from his most recent webinar on DataCamp.</p>
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https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-2

Seven Tricks for Better Data Storytelling: Part II

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<p>With proper design skills, data teams have the power to frame arguments and persuade with data. That was the key takeaway from a recent episode of <a target="_blank" href="https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need">DataFramed</a> featuring Andy Cotgrave, the technical evangelist at Tableau. In this two-part blog post, we distill seven concrete tips for better data storytelling from the podcast.</p>
<h2 id="1-craft-data-stories-with-a-message-in-mind-">1. Craft data stories with a message in mind</h2>
<p>Subtle differences in design can profoundly impact the audience’s interpretation of a data visualization. These designs can come in the form of color choice, title, and annotations.</p>
<p>One chilling example is Simon Scarr’s Iraq Bloody Toll that emphasizes the death toll from the US military engagement in Iraq. The downward bar chart colored in red is strongly evocative of the bloodbath in Iraq, making the visual metaphor highly memorable. Interestingly, a simple reorientation and color change alters the message entirely.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643385426/image7_tafrhv.gif" />Figure 1: Iraq’s Bloody Toll by Simon Scarr [<a target="_blank" href="http://www.simonscarr.com/iraqs-bloody-toll">Source</a>]</center><br>



<p>Data storytellers ought to wield this power to convey arguments responsibly and deliberately. When delivering a supposedly objective data story, data storytellers should pay attention to details in charts that might amplify biases. For instance, axes should start with zero to avoid misleading correlations.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319150/image1_ybw363.png" />Figure 2. The same data points can convey dramatically different results [<a target="_blank" href="https://www.datasciencejunction.com/2019/01/how-to-be-cautious-about-misleading.html">Source</a>]</center>


<h2 id="2-position-elements-in-a-data-story-appropriately">2. Position elements in a data story appropriately</h2>
<p>The Gutenberg principle states that a user’s eyes travel according to a Z-shaped path from top-left to the bottom-right when reading content.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319221/image6_tdj1dv.png" />Figure 3. The Gutenberg principle [<a target="_blank" href="https://www.oreilly.com/library/view/practical-game-design/9781787121799/f77e030b-0f77-4774-9025-000c0d7732e6.xhtml">Source</a>]</center><br>

<p>Applied to data stories, this principle tells us that the most important information should be on the top left. More granular details should be fleshed out towards the bottom of the chart. A call-to-action can also be included in the bottom-right of the chart where the terminal section is.</p>
<h2 id="3-understand-your-audience-using-design-principles">3. Understand your audience using design principles</h2>
<p>Design principles can be used to understand an audience’s experience with data stories. In the book Design of Everyday Things, the author Don Norman argues that end-users process all objects on <a target="_blank" href="https://www.interaction-design.org/literature/article/norman-s-three-levels-of-design">three levels of emotions</a>–visceral, behavioral, and reflective.</p>
<ul>
<li><p>The visceral level:
Within the first second of seeing a chart, users judge a chart based on its appearance. That is the visceral level of processing, which refers to the ingrained and automatic qualities of human emotions. As such, a chart’s aesthetic is key to keeping an audience’s attention on the data story.</p>
</li>
<li><p>The behavioral level:
The user then judges a visualization based on its usefulness, which is referred to as the behavioral level of processing. In other words, the audience must be able to see the author’s point with the visualization.</p>
</li>
<li><p>The reflective level:
After interacting with the visualization, users reflect on their experience. For instance, a user is likely to reuse a data dashboard if they find their experience pleasant upon reflection.</p>
</li>
</ul>
<h2 id="more-tips-on-data-storytelling">More Tips on Data Storytelling</h2>
<p>This article covered how data teams can enhance data stories with thoughtful designs. In the <a target="_blank" href="https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-2">second part of the blog post</a>, we will provide more actionable tips for presenting excellent data stories.</p>
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https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-1

Seven Tricks for Better Data Storytelling: Part I

<ol>
<li>Introduction</li>
<li>What are the responsibilities of a data engineer?</li>
 <ul>
<li>ETL Pipelines</li>
<li>Data Warehouses</li><br>
</ul>
<li>Data engineering vs data science</li>
<li>Why has data engineering increased in popularity?</li>
<li>What are the skills of a data engineer?</li>
<li>How to learn data engineering</li>
</ol>
<h2 id="introduction">Introduction</h2>
<p>Data is everywhere. In recent years, with the growing number of connected devices and the expansion of their use, the world has been producing a vast amount of data that cannot be handled with traditional methods anymore.</p>
<p>According to Statista, as of 2021 there are <a target="_blank" href="https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/">23.8 billion devices connected to the internet</a> and 58% of those are IoT devices (smart home devices, connected self-driving cars, industrial devices, etc.) and the remaining 42% are non-IoT devices (laptops, smartphones, etc.). This means that nowadays data can come from various sources. Some of these are:</p>
<ol>
<li><strong>User activities:</strong> Any user action produces data and even the data that reflects where and how often we tap on the mobile applications we use is valuable. Every piece of data that contains this kind of information needs to be kept and distributed to the applications’ data centers.</li>
<li><strong>“Internet of Things” devices:</strong> These devices produce a huge amount of information, such as sensor data generated in many different locations. All this data needs to be retrieved and sent to a data pool for further analysis.</li>
<li><strong>Program logs:</strong> Today’s computer applications consist of many components and all those components produce program logs. These logs are sent to a data engineering pipeline for further analysis.</li>
</ol>
<p>All the different structured data that the world produces needs to be delivered to other components in the software structure, or visualized and interpreted by business analysts and data scientists. However, this delivery is not a simple task. There are some crucial requirements on how to deliver the data:</p>
<ol>
<li>Data needs to be delivered in a standardized structure</li>
<li>The data should not present any conflicts</li>
<li>Data duplication may need to be removed</li>
<li>The data needs to be reliable</li>
<li>The data delivery must be done with minimum delay</li>
</ol>
<p>Therefore, the delivery of this information is a complex process and, if it is not carried out properly, it can be very problematic, especially for medium and large-scale companies. This is where data engineers come into play.</p>
<p>In this article you will learn the basics of data engineering, which are:</p>
<ol>
<li>What data engineering is and why to ensure a robust data pipeline</li>
<li>The difference between data science and data engineering</li>
<li>The reason behind the increasing popularity of data engineering</li>
<li>The skills of a good data engineer</li>
<li>How DataCamp can help you become a data engineer</li>
</ol>
<h2 id="what-are-the-responsibilities-of-a-data-engineer-">What are the responsibilities of a data engineer?</h2>
<p>To put it simply, data engineers:</p>
<ol>
<li>Aim to make data easily accessible and available for data scientists and business intelligence engineers (or anyone working with data)</li>
<li>Optimize the big data system architecture of companies</li>
<li>Design and configure databases</li>
<li>Collaborate with teams of business intelligence engineers, data scientists, and data analysts</li>
<li>Explore and transform data
In order to perform all these tasks, data engineers first need to set up the ETL pipelines.</li>
</ol>
<h2 id="etl-pipelines">ETL pipelines</h2>
<p>ETL pipelines receive complex data at regular time intervals and process it with the aim of storing the data in a usable form. It is the responsibility of the data engineers to establish and maintain these pipelines.</p>
<p>There are 3 types of ETL pipelines:</p>
<ol>
<li><strong>Extract</strong>
A vast number of data created by data sources is collected in different formats.</li>
<li><strong>Transform</strong>
Since the data comes in different formats and due to its inconsistent sources, the data is processed to become as standardized as possible. This increases the discoverability and usability of the data for the next steps.</li>
<li><strong>Load</strong>
Once the data is in a discoverable and usable format, it is usually stored in data warehouses. This makes the data available at all times for further analysis.</li>
</ol>
<h2 id="data-warehouses">Data warehouses</h2>
<p>Data warehouses are where standardized data is stored. They are optimized for filtering and reading large volumes of data. In modern data warehouses, both structured and unstructured data can be stored, because it is also important to store the data that cannot be categorized, such as photos and videos.</p>
<h2 id="data-engineering-vs-data-science">Data engineering vs data science</h2>
<p>The differences between the roles of a data engineer and a data scientist are important. On the one hand, data scientists have an important role in companies because they contribute to data-driven decision making. Nevertheless, the success of data scientists is only as good as the data that they have available.</p>
<p>On the other hand, data engineers are the ones who design the systems that provide data to data scientists in an organized and consistent manner. Once the data engineers make the information available for them, data scientists then try to find patterns in the data to reach conclusions, using the format provided by the data engineers.</p>
<p>Data engineers and data scientists clearly work closely together. We can think of it as a football match. In football, it is the coach who develops the strategies that will create goal opportunities, and it is the coach who sets the players in certain positions. In this context the coach is the data engineer, so we can consider that the ball is the data and the player who scores is the data scientist. The rest of the team is the infrastructure.</p>
<h2 id="why-has-data-engineering-increased-in-popularity-">Why has data engineering increased in popularity?</h2>
<p>According to research conducted on the <a href="https://www.burning-glass.com/products/nova/">Burning Glass Nova</a> platform, job postings for &quot;data engineer&quot; increased by 88.3% in 2019. According to this research, demand for data engineers has been on the rise since 2016. Another interesting piece of information is that the number of job openings for data engineers is almost 5 times higher than the job openings for data scientists. The salary of a data engineer is also 20-30% higher than the salary of a data scientist.</p>
<p>As stated above, data scientists are the final link in the data processing chain. They need the data to be processed by a data engineer in order to perform their job. As the number of data sources and data types multiply, the need for a data engineer grows. For this reason, companies have recently started to invest more in the extraction and distribution of data.</p>
<p>In Figure 4.1, a pyramid chart displays the hierarchy of needs of data science. In this hierarchy, the tasks assigned to data engineers are at the base of the pyramid, which explains how crucial data engineers are for companies.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image3_kje7by.png" />Figure 4.1: The data science hierarchy of needs.</center>

<center><a target="_blank" href="https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007">Source: hackernoon</a></center>

<h2 id="what-are-the-skills-of-a-data-engineer-">What are the skills of a data engineer?</h2>
<p>The skills of data engineers are generally intertwined with the skills of software engineers and data scientists, as shown in Figure 5.1.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image2_rqg5yb.png" />
Figure 5.1: Data roles and skill sets</center>
<center><a target="_blank" href="https://ryanswanstrom.com/2016/11/28/data-scientists-data-engineers-software-engineers-the-difference-according-to-linkedin/">Source: RyanSwanstrom</a></center>

<h2 id="is-the-role-of-a-data-engineer-closer-to-the-one-of-a-software-engineer-or-to-the-one-of-a-data-scientist-">Is the role of a data engineer closer to the one of a software engineer or to the one of a data scientist?</h2>
<p>The previous occupations of a group of data engineers are displayed in Figure 5.2.  According to this figure, over 40% of them have a software engineering background. Not only does this mean that data engineering and software engineering are significantly closer than we may have first thought, but also that software engineers nowadays are much more likely to switch to data engineering than any other field.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image5_d4vwny.png" />
Figure 5.2: Data engineers by prior role (top 10).</center>
<center><a href="https://www.stitchdata.com/resources/the-state-of-data-engineering/">Source: stitchdata</a></center><br>

<p>At DataCamp we believe the following to be the skills of a good data engineer:</p>
<ol>
<li><strong>Software engineering background</strong>
Data engineers need to have a software engineering background, since they need to approach data engineering challenges with their knowledge of object-oriented programming, data structures and algorithms. Data engineers must be experienced in Python, Scala or Java.</li>
<li><strong>Tools</strong>
Data engineers need to be comfortable with data engineering tools like Airflow, Apache or Kafka.</li>
<li><strong>Database Technologies</strong>
A data engineer should have knowledge of multiple kinds of databases.</li>
<li><strong>Cloud Technologies</strong>
Data engineers should be comfortable using multiple cloud platforms like Amazon Web Services, Google Cloud Platform and Microsoft Azure. They need to have experience in cloud architecture and DevOps tools as well, since they are responsible for the automation of the data flow part of the system.</li>
</ol>
<h2 id="how-to-learn-data-engineering">How to learn data engineering</h2>
<p>If you want to improve in the data engineering field, you can start by completing the following recommended DataCamp courses. By completing these courses, you will likely become a more suitable candidate for internships or job positions in the field of data engineering.</p>
<ol>
<li><a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/intermediate-python">Intermediate Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-relational-databases-in-sql">Introduction to Relational Databases in SQL</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-scala">Introduction to Scala</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-data-engineering">Introduction to Data Engineering</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-airflow-in-python">Introduction to Airflow in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/aws-cloud-concepts">AWS Cloud Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/building-data-engineering-pipelines-in-python">Building Data Engineering Pipelines in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/nosql-concepts">NoSQL Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/etl-in-python">ETL in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/streaming-concepts">Streaming Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda">Streaming Data with AWS Kinesis and Lambda</a></li>
</ol>


https://www.datacamp.com/community/blog/what-is-data-engineering

Learn what data engineering is, what is the difference between data science and data engineering, the scope in the field, and how to become one.

Learn what data engineering is, compare it with data science, and find out the skills required for the role and how to acquire them. 

What is Data Engineering?

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

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