https://www.datacamp.com/community/podcast/delivering-smarter-cities-and-better-public-policy-w-data

<p>Adel speaks with Kaveh Vessali about the intersection between data and public policy and the many exciting insights he’s gained from his role delivering smart cities and data transformation projects within the public sector in the middle east.</p>

Adel speaks with Kaveh Vessali about the intersection between data and public policy and the many exciting insights he’s gained from his role delivering smart cities and data transformation projects within the public sector in the middle east.

Delivering Smarter Cities & Better Public Policy w/ Data

https://www.datacamp.com/blog/delivering-smarter-cities-and-better-public-policy-w-data

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

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

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

Harnessing the Power of Collaboration

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

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

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

DataCamp Announces New Funding

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

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

Request for Proposal: Topical Projects for January 2019

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

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

Data science at DataCamp

<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

<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 

https://www.datacamp.com/community/tutorials/tutorial-deep-learning-tutorial

The tutorial answers the most frequently asked questions about deep learning and explores various aspects of deep learning with real-life examples.

Deep Learning Tutorial

Deep Learning - A Tutorial for Data Scientists

https://www.datacamp.com/community/tutorials/tutorial-mastering-pycharm-tutorial

The data scientist's comprehensive guide to mastering PyCharm

Explore how to install PyCharm and create new projects. How to debug, test and commit to git. Find frequently asked questions.   

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How to Become a Data Scientist

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