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How to Become a Data Scientist: The Ultimate Guide (2020) In this article, I share a list of online resources that will guide you on how to become a data scientist. Data science is a complex and multidisciplinary field with subjects that range from statistics to software engineering and business. I made this guide including skills that I think are essential to become a data scientist. The resources on this list are broad. Some of them touch on programming and others on statistics and mathematics. This list is especially useful if you are a beginner because many courses don't have knowledge pre-requisites. At the same time, I included advanced programs for those who already have knowledge of data science, so everyone can use this guide. I divided this guide into five parts. The five parts are: Introduction Statistics and Probability Mathematics Programming Full "How to Become a Data Scientist" Programs Let's get started! 💪🚀 How to use the resources on this guide: "How to become a data scientist"? To use the how to become a data scientist guide correctly, you can start with the resource in the introduction section and then choose at least one course from sections two, three and four. Or you can go straight to section five and choose one of the resources over there. You should bookmark this guide to keep access to the resources here. For each part of this guide, I list my favorite courses, you can choose the one that fits your profile the most. Some courses are for beginners and some are for advanced students with more experience. For most of the items on this list, I share a brief description of the resource and I also explain the platform that hosts it. So, for example, if I suggest a course from Khan Academy, you will see an explanation of what Khan Academy is followed by a description of the course I am suggesting. These are the titles of the resources from each section: Introduction on how to become a data scientist Data Science from Scratch: First Principles with Python by Joel Grus Statistics and Probability Statistics and probability by Khan Academy Statistics by CrashCourse on Youtube Introduction to Statistics by Udacity Introduction to Descriptive Analytics by Udacity Introduction to Inferential Statistics by Udacity Introduction to Probability and Statistics by MIT Introduction to Probability by Harvard on EdX Statistics with Python Specialization by the University of Michigan on Coursera Statistics with R Specialization by Duke University on Coursera Statistics for Business Analytics by SuperDataScience on Udemy Probabilistic Graphical Models Specialization by Standford University on Coursera Mathematics Linear Algebra by Khan Academy Multivariable Calculus by Khan Academy Krista King Algebra and Calculus Courses on Udemy: Become an Algebra Master, Become a Calculus Master 1, Become a Calculus Master 2, and Become a Calculus Master 3 Data Science Math Skills by Duke University on Coursera Introduction to Discrete Mathematics for Computer Science on Coursera Mathematics for Machine Learning by Imperial College London on Coursera Multivariable Calculus and Linear Algebra by MIT Programming Introduction to coding Learn how to code by Codeacademy Introduction to Programming Nanodegree by Udacity Python Programming Python tutorial by SoloLearn Python 3 Programming Specialization by the University of Michigan on Coursera Python for Everybody Specialization by the University of Michigan on Coursera Introduction to Scripting in Python Specialization by Rice University on Coursera Applied Data Science with Python Specialization by the University of Michigan on Coursera Python Programmer by DataCamp Programming for Data Science with Python by Udacity The Command-Line, Git & GitHub Learn the Command Line on Codecademy Introduction to Git & GitHub by FreeCodeCamp in Youtube How to Use Git and GitHub by Udacity Full "How to Become a Data Scientist" Programs Coursera Specializations Data Science Specialization by Johns Hopkins University IBM Data Science Professional Certification by IBM Executive Data Science Specialization by John Hopkins University Data Mining Specialization by the University of Illinois at Urbana-Champaign Deep Learning Specialization by deeplearning.ai Machine Learning Specialization by the University of Washington Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Machine Learning and Reinforcement Learning in Finance Specialization by New York University Tandon School of Engineering Advanced Data Science with IBM Specialization by IBM Advanced Machine Learning Specialization by the National Research University Higher School of Economics Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Data Engineering, Big Data, and Machine Learning on Google Cloud Platform Specialization by Google Machine Learning Specialization by Standford University and Andrew Ng Business Analytics Specialization by the University of Pennsylvania Udacity Nanodegrees Data Scientist Nanodegree Data Analyst Nanodegree Data Engineer Nanodegree AI Programming with Python Nanodegree Deep Learning Nanodegree Artificial Intelligence Nanodegree Machine Learning Engineer Nanodegree Intro to Machine Learning with PyTorch Nanodegree Datacamp Career Tracks Data Scientist with Python Career Track Data Science for Everyone Career Track Data Analyst with Python Career Track Data Engineer with Python Career Track Machine Learning Scientist with Python Career Track Machine Learning for Everyone Career Track Codecademy Career Path Data Science Career Path Index How to Become a Data Scientist: The Ultimate Guide (2020) Introduction to Data Science 11 Courses on Statistics and Probability 11 Courses on Mathematics Programming for Data Science Full Data Science Programs Introduction on How to Become a Data Scientist Data Science from Scratch by Joel Grus I will start this guide on how to become a data scientist by sharing a book. My first suggestion is Data Science from Scratch: First Principles with Python by Joel Grus. If you never opened a book or watched a course about Data Science, I suggest starting with this one. I think this book is an excellent introduction to key principles in Data Science. You can do most of the work that the book demonstrates using Python libraries, but I find important to know the basic building blocks. The book has a crash course in Python, visualization principles, linear algebra, statistics, probability and lots of examples. In my opinion, a great place to start. Besides, you can choose your own pace with the book and take the time you need to learn. To wrap-up the introduction, I want to mention that there are several types of professionals who work with data. There are data analysts, data scientists, data engineers, machine learning engineers, etc. You can read more about what each of these professionals does here. In the meantime, you can take a career path quiz from Coursera and discover what job profile best matches your skills and interests. Career path quiz Coursera has a 7-question quiz that helps you decide which data career path is more suitable for you. It will take only a few minutes to finish. You can take the quiz for free here. Additional readings: Data engineers vs. data scientists by Jesse Anderson What are machine learning engineers? by Ben Lorica and Mike Loukides How to Become a Data Scientist with Statistics and Probability: 11 Courses In this guide on how to become a data scientist, statistics and probability are subjects that gain highlight. Data Science itself is a combination of three fields, statistics, mathematics and computer science. In this article, I will focus on statistics and math, to read more about computer science and data science take a look here. In this section, I listed eleven fantastic courses. For each recommendation, I briefly introduce the platform that hosts it and a description of the course. 1. Statistics and probability by Khan Academy Khan Academy Logo Khan Academy is an educational platform that provides quality education for anyone around the world. They are a nonprofit organization, so there are no fees with using Khan Academy. Their videos are on YouTube, but it is worth registering on their official website where besides watching the lectures, you're able to build a profile, track your progress, win badges, participate in discussions and projects. Besides that, the platform has a vast collection of courses, not only in the statistics and mathematics field but also in history and arts. The statistics and probability course include 16 topics with more than 70 videos and interactive exercises. You can access the course in this link. 2. Statistics by CrashCourse on Youtube CrashCourse Youtube Logo Crash Course is a YouTube channel with educative purposes. At the moment I am writing, they have more than 10 million subscribers. They create amazing, eye-catching videos over several subjects like physics, philosophy, games, economics, history, computer science, etc. Their statistics series have 45 videos with a lot of real-life examples. You can access the course by clicking in this link. 3. Introduction to Statistics by Udacity Udacity Company Logo Udacity is an online education platform that offers massive open online courses (MOOCs). While they have free classes in their catalog, you have to pay for most of them. They offer programs in several interesting subjects like Artificial Intelligence, Blockchain development, Computer Vision, Virtual Reality, Flying Cars & Autonomous Flight, etc. In the last section, you will see that I share some of Udacity's full programs that they call Nanodegress. Check the Nanodegrees here. Udacity offers a free course called Intro to Statistics with seven lessons. It intends to teach you how to identify relationships in data, probability, estimation, outliers, normal distribution, inference, and regression. Click in this link to access this course. 4. Introduction to Descriptive Analytics by Udacity Intro to Descriptive Statistics is also a free course in the Udacity platform. It is a beginner level course that proposes to teach the basics concepts of describing data. It is excellent for those who want to begin a career in data science, data analysis, or in the field of psychology and economics. This course, as the title says, will introduce you to descriptive analytics. It contains seven lessons with the following subjects: research methods, data visualization, central tendency measures, variability measures, standardizing methods, the normal and sampling distributions. You can watch the course for free via this link. 5. Introduction to Inferential Statistics by Udacity Intro to Inferential Statistics is the second part of the previous course. It is a free course, beginner level that covers the inferential part of statistics. This course's program has seven lessons that will show how to perform estimation calculations, hypothesis testing, t-tests, ANOVA, correlation measures, regression, and Chi-squared tests. You can watch the course for free via this link. 6. Introduction to Probability and Statistics by MIT MIT OpenCourseWare Logo MIT OpenCourseWare is an online platform that publishes the Massachusetts Institute of Technology (MIT) lectures and online textbooks. Their mission is to make quality education available worldwide. The release date was 2001, and it has inspired more than 250 other institutions to make their course material available online for free. There is no need to signup, enroll or pay to watch the courses! Everything is open and free. Don't miss the chance to browse all their classes on their website. The course does not provide a certificate of completion. Introduction to Probability and Statistics is a highly rated introduction course that covers topics like basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression. Besides the lectures, the course includes readings, class slides, assignments, and exams. It is also possible to download all the content of the course offline. You can access the course via this link. 7. Introduction to Probability by Harvard on EdX edX Company Logo edX is an online platform that offers massive open online courses (MOOCs). Top-rated Universities provide the classes over there. Most of the courses are free to watch, and it is possible to receive certification after completing a course by paying a fee. Some courses are credit-eligible. The platform also offers complete Master Degrees. It was created by The Massachusetts Institute of Technology and Harvard University in May 2012. Besides its teaching purposes, all the activity on the website is being used to research student's behavior. So, the site also aims to collect data and research to improve retention, course completion and learning outcomes in education. Introduction to Probability is provided by Harvard University (HarvardX) via the edX online platform. The course is for beginners and includes seven units that go from the introduction to probability to Markov chains. HarvardX Logo The course is free to follow, and it costs $99 to receive a verified certificate. You can enroll and access the course via this link. 8. Statistics with Python Specialization by the University of Michigan on Coursera Coursera Company logo Coursera is an online platform founded in 2012 that offers MOOCs. Top universities and big companies create the courses over there. Like edX, Coursera also provides full Master Degrees. The content on the platform is sometimes free to watch, and the students can receive a verified certification that authenticates successful course completion after paying a fee. Statistics with Python Specialization by University of Michigan Statistics with Python is a Coursera specialization. A specialization is a collection of courses created to provide a complete overview of the subject. I list more specializations from Coursera in the last section of this guide. The University of Michigan (USA) offers this specialization on Coursera. It includes three courses: Understanding and Visualizing Data with Python Inferential Statistical Analysis with Python Fitting Statistical Models to Data with Python This specialization teaches statistics using Python. So, I recommend taking this course if you have already some programming knowledge in Python. You can access the course via this link. 9. Statistics with R by Duke University on Coursera Statistics with R Specialization by Duke University Duke University certifies this specialization on Coursera. It has a beginner level and it includes the five following courses: Introduction to Probability and Data Inferential Statistics Linear Regression and Modeling Bayesian Statistics Statistics with R Capstone You can access the course via this link. 10. Statistics for Business Analytics by SuperDataScience on Udemy Udemy Company Logo Udemy is an online learning platform that offers MOOCs. However, different from platforms like Coursera and edX, the courses are not produced by Universities or big companies. Anyone can produce and submit a course to the platform. If the course follows Udemy's standards, Udemy will sell it, and both the creator and Udemy will get a cut from it. Statistics for Business Analytics and Data Science Statistics for Business Analytics and Data Science has 7 hours and 45 lectures. I like this course because it shows how to apply statistics for Business. So, the course is not only suitable for those people who are interested in scoring a job in Data Science or Data Analytics, but also for business owners who want to understand better what taking data-driven decisions means. The course covers topics as variable types, normal distribution, central limit theorem, hypothesis testing, Z-score, confidence intervals, standard deviation, statistical significance, p-value, etc. You can access and enroll for this course via this link. 11. Probabilistic Graphical Models by Standford University on Coursera Probabilistic Graphical Models by Stanford University Probabilistic Graphical Models is an advanced level Specialization. If you watched any of the courses above in this list or if you already have a sound knowledge of statistics and probability you can follow this program without big problems. This specialization includes the following three courses: Probabilistic Graphical Models 1: Representation Probabilistic Graphical Models 2: Inference Probabilistic Graphical Models 3: Learning The main content of the courses is: Introduction to probabilistic graphical models Bayesian network representation and its semantics Dynamic Bayesian Networks Structured CPDs for Bayesian Networks Markov Networks Belief Propagation Algorithms MAP Algorithms Inference in Temporal Models Parameter Estimation in Bayesian Networks You can access this course via this link. How to Become a Data Scientist with Math: 11 Courses The mathematics topics I chose for this guide on how to become a data scientist focus on are linear algebra and multivariate calculus. The majority of the courses are free to watch, and for some of them, it is possible to receive a verified certificate upon the payment of a fee. I introduced the platforms that host these courses in the previous section. Under every course title, I will provide a brief explanation of the course content. Khan Academy 1. Linear Algebra The Linear Algebra course covers topics as vectors and spaces, matrix transformations and alternate coordinate systems. Linear algebra is essential for Data Science and Machine Learning. You can access this program via this link. 2. Multivariable Calculus The Multivariable Calculus course covers multivariable functions, derivatives, applications, and integration of the multivariable functions. Besides that, it teaches Green's, Stoke's, and the divergence theorems. You can access this program with this link. Krista King Algebra and Calculus Courses on Udemy Krista King Algebra and Calculus Courses 3. Become an Algebra Master This course has 346 lectures and several practical exercises. You can watch the course via this link. Krista King has three courses on Calculus, you will probably not need all of this content for Data Science, but it is never wrong to learn. The calculus courses are the following: 4. Become a Calculus 1 Master This course has 18.5 hours of videos. Among the subjects, you will find precalculus, derivatives, and limits & continuity. You can access the course via this link. 5. Become a Calculus 2 Master This course has 32 hours of videos. You will learn Integrals, Polar & Parametric, and Sequences & Series. You can access the course via this link. 6. Become a Calculus 3 Master This course has 32,5 hours of video, and it covers partial derivatives, vectors, multiple integrals, and Differential Equations. You can access the course via this link. 7. Data Science Math Skills by Duke University on Coursera Data Science Math Skills by Duke University Data Science Math Skills is a single course, not a specialization. It is designed to teach the mathematics that you need to succeed in Data Science. Topics in the course: Set theory Properties of the real number line Interval notation and algebra with inequalities Uses for summation and Sigma notation Math on the Cartesian plane Graphing and describing functions and their inverses The concept of instantaneous rate of change and tangent lines to a curve Exponents, logarithms, and the natural log function. Probability theory The course does not dive deep into the subjects, so it is useful if you want to refresh your mathematics knowledge or if you're beginning to learn it. You can access it via this link. 8. Introduction to Discrete Mathematics for Computer Science on Coursera Introduction to Discrete Mathematics for Computer Science Specialization The National Research University Higher School of Economics certifies the Introduction to Discrete Mathematics for Computer Science specialization. It has five courses which are: Mathematical Thinking in Computer Science Combinatorics and Probability Introduction to Graph Theory Number Theory and Cryptography Delivery Problem The courses use Python programming language, so make sure you have some python programming knowledge before you start it. I like this specialization because it includes a section of mathematical thinking, which helps you with developing reasoning in mathematical terms. I don't consider it a beginning course though, so make sure you feel comfortable with the mathematics subjects that you learned in the previous classes of this list. You can access this specialization via this link. 9. Mathematics for Machine Learning by Imperial College London on Coursera Mathematics for Machine Learning Specialization by Imperial College London The Imperial College in London certifies the Mathematics for Machine Learning specialization. It has three courses which are: Mathematics for Machine Learning: Linear Algebra Mathematics for Machine Learning: Multivariate Calculus Mathematics for Machine Learning: PCA This specialization focuses on the math subjects for Machine Learning. Topics that you have already seen in the Khan Academy's courses. I consider it an intermediate level course. It also requires basic Python knowledge. You can access this specialization via this link. Multivariable Calculus and Linear Algebra by MIT 10. Multivariable Calculus This course is made of recorded lectures from MIT. The course syllabus is: Vector and Matrices Partial Derivatives Double Integrals and Line Integrals in the plane Triple Integrals and Surface Integrals in 3-space Besides the lectures, you can download lecture notes, take the exam (and check the solution later) and take assignments. The course is free, so you won't receive a certificate for it. You can access the course via this link. 11. Linear Algebra This course is also made from recorded MIT lectures. It is extensive, with more than 30 lectures. Similar to the Multivariate Calculus course, you can download the course materials and assignments and related resources for the subject. The course is free to watch, so you can't receive a certificate of completion. You can access the course via this link. Programming for Data Science Which programming languages should I learn to become a data scientist? That is a good question. The truth is, there is no right answer. I want to say that different tasks demand different tools, so there will be a language that performs better for every data science task. In this guide on how to become a data scientist, I will list resources to learn Python because I believe Python has many essential libraries and a big community around it which makes it an excellent language to learn for Data science. Introduction to coding If you are entirely new to coding, I recommend the following course that is an introduction to programming in general. 1. Learn how to code by Codecademy Codecademy Logo Codecademy is an online platform that offers free and paid coding lessons. For the pro (paid) version you can have access to learning plans called Paths that are a collection of courses, similar to Coursera's specializations and Udacity's Nanodegress. Check the last section to see my list of Codecademy's Paths. Learn how to code is a free course. It has six hours of lessons and zero prerequisites! Perfect for someone who wants to start now and don't know where to begin. You can find the course via this link. Codecademy also offers a full career path called Code Foundations in their Pro version, not necessarily needed for Data Science, but really cool if you are interested in coding! 2. Introduction to Programming Nanodegree by Udacity Introduction to Programming Nanodegree by Udacity This is a more general Nanodegree that gives a full overview of Programming. From Web and App development to Artificial Intelligence. You can find this Nanodegree via this link. 07 Courses to learn Python Python is the number one language when people talk about data science. I am a personal fan of Python too, I couldn't leave it out of this guide on how to become a data scientist 😊. Besides that, Python can be easy to pick up whether you're a first time programmer or experienced with other languages. 1. Python tutorial by SoloLearn SoloLearn Logo SoloLearn is is an online and mobile learning platform that offers free coding classes. It is very dynamic and interactive as you can also complete the lectures on your mobile phone. Their Python Tutorial is a great way to start learning Python. It has 92 lessons and 275 quizzes. More than 4 million people have already done this tutorial! That's really impressive. You can access this tutorial via this link. 2. Python 3 Programming Specialization by the University of Michigan on Coursera Python 3 Programming Specialization by University of Michigan This specialization has five courses, starting from the basics until a final capstone project. You can access and enroll for this specialization via this link. 3. Python for Everybody Specialization by the University of Michigan on Coursera Python for Everybody Specialization by University of Michigan This specialization has five courses including data structures, web scraping, databases, and a final capstone project. You can access and enroll for this specialization via this link. 4. Introduction to Scripting in Python Specialization by Rice University on Coursera Introduction to scripting in Python by Rice University This specialization has four courses which are: Python Programming Essentials Python Data Representations Python Data Analysis Python Data Visualization You can access and enroll for this specialization via this link. 5. Applied Data Science with Python by the University of Michigan on Coursera Applied Data Science with Python specialization by University of Michigan This specialization will not only improve your Python coding skills but it will explain how to use Python for Data Science. There are five courses in this specialization: Introduction to Data Science Applied Plotting, Charting & Data Representation in Python Applied Machine Learning in Python Applied Text Mining in Python Applied Social Network Analysis in Python You can enroll in this specialization via this link. 6. Python Programmer by DataCamp Python Programmer from DataCamp Click this link to access this fantastic course. 7. Programmer for Data Science with Python by Udacity Programmer for Data Science with Python by Udacity This is a new Nanodegree from Udacity to learn programming for data science with Python. Use this link to access this program. Learning how to use the Command Line, Git & GitHub and Stack Overflow Independently of which career path you’re going to take, you will need to know how to use the command line, git and GitHub. 1. Learn the Command Line on Codecademy Learn the Command Line Course on Codecademy The command line is an interface in which you can interact with your computer using text (commands). By using the command line you can perform tasks more efficiently and access some programs or functionalities that are not available for common user interfaces. Learn the Command Line is a free, 10-hour course with no prerequisites, offered by Codecademy. You can access the course and enroll via this link. 2. Introduction to Git & GitHub by FreeCodeCamp in Youtube freeCodeCamp logo freeCodeCamp is a non-profit organization that intends to make learning web development accessible to everyone. It is useful for Data Science because you can learn Databases (good for getting Data Engineering skills), Git & Github and D3.js (for Data Visualization). You can access the lectures on their online platform or from their Youtube channel. Git & GitHub is a series of 11 videos that can be found in the freeCodeCamp's Youtube channel which has more than 800k subscribers and awesome content! You can access this playlist via this link. 3. How to Use Git and GitHub by Udacity If you still have doubts about how to use Git and GitHub you can watch Udacity's How to Use Git and GitHub free course. You can access the course via this link. StackOverflow Stack Overflow logo This is not a course, but a platform where developers from the whole world post their questions and struggles with coding. Other developers will answer these questions with their own solutions. Stack Overflow goes way beyond that, as users can build a reputation within the platform, so it is worth registering on the website and get familiar with how things work over there because you have a 100% chance of using it in the future. By the way, don't expect to get answers over there without showing that you have done active effort to find a solution. You can access Stack Overflow via this link. Full "How to Become a Data Scientist" Programs A Full program, in my perspective, is a program that intends to teach you how to become a data scientist. So, it does not teach you only one subject like statistics or mathematics or programming. These programs aim to teach all of the subjects that you need to master in order to become a Data Scientist. I divide this section per platform that offers this kind of program. Every platform calls the program with a different name. The platforms and the name of the programs I included are: Coursera: Specializations Udacity: Nanodegrees Codecademy: Paths Datacamp: Career Tracks Coursera Specializations Beginner Specializations 1. Data Science Specialization by Johns Hopkins University Beginner Data Science Specialization by John Hopkins University Access the specialization via this link. 2. IBM Data Science Professional Certificate Specialization IBM Data Science Professional Certificate Specialization from Coursera Access the specialization via this link. IBM has also two other specializations offered by Coursera that have a shorter duration because they hold just a part of the courses from the Data Science Professional Certificate specialization that I mentioned above. Those specializations are: 1. IBM Introduction to Data Science Specialization Introduction to Data Science specialization by IBM Access the specialization via this link. 2. IBM Applied Data Science Specialization Applied Data Science Specialization by IBM Access the specialization via this link. 3. Executive Data Science Specialization by John Hopkins University Executive Data Science Specialization by John Hopkins University Access the specialization via this link. 4. Business Analytics Specialization by the University of Pennsylvania on Coursera Business Analytics Specialization by the University of Pennsylvania on Coursera Access the specialization via this link. Intermediate Specializations 1. Machine Learning by Stanford University and Andrew Ng on Coursera Machine Learning by Stanford University and Andrew Ng on Coursera Access the specialization via this link. 2. Data Mining Specialization by the University of Illinois at Urbana-Champaign on Coursera Data Mining by the University of Illinois at Urbana-Champaign on Coursera Access the specialization via this link. 3. Deep Learning Specialization by deeplearning.ai on Coursera Deep Learning specialization by deeplearning.ai on Coursera Access the specialization via this link. 4. Machine Learning specialization by the University of Washington Machine Learning specialization by the University of Washington Access the specialization via this link. 5. Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud on Coursera Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud Access the specialization via this link. 6. Machine Learning and Reinforcement Learning in Finance Specialization by New York University Tandon School of Engineering on Coursera Machine Learning and Reinforcement Learning in Finance Specialization by New York University Tandon School of Engineering Access the specialization via this link. 7. Data Engineering, Big Data, and Machine Learning on GCP Specialization Data Engineering, Big Data, and Machine Learning on GCP Specialization Access the specialization via this link. Advanced Specializations 1. Advanced Data Science with IBM Specialization by IBM on Coursera Advanced Data Science with IBM Specialization by IBM Access the specialization via this link. 2. Advanced Machine Learning Specialization by the National Research University Higher School of Economics on Coursera Advanced Machine Learning Specialization by the National Research University Higher School of Economics Access the specialization via this link. 3. Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud on Coursera Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization by Google Cloud Access the specialization via this link. Udacity Nanodegrees Nanodegrees are fantastic programs! They are the answer to the question of how to become a data scientist because they are all-in-one programs. 01. Become a Data Scientist Become a Data Scientist Nanodegree Access the Nanodegree via this link. 02. Data Analyst Data Analyst Nanodegree Access the Nanodegree via this link. 03. Data Engineer Data Engineer Nanodegree Access the Nanodegree via this link. 04. AI Programming with Python AI Programming with Python Nanodegree Access the Nanodegree via this link. 05. Deep Learning Deep Learning Nanodegree Access the Nanodegree via this link. 06. Artificial Intelligence Artificial Intelligence Nanodegree Access the Nanodegree via this link. 07. Machine Learning Engineer Machine Learning Engineer Nanodegree Access the Nanodegree via this link. 08. Intro to Machine Learning with PyTorch Intro to Machine Learning with PyTorch Nanodegree Access the Nanodegree via this link. Codecademy Paths 01. Data Science Path Data Science Path Codecademy Access the path via this link. Datacamp Career Tracks 01. Data Scientist with Python Data Scientist with Python DataCamp Access the career track via this link. 02. Data Science for Everyone Data Science for Everyone DataCamp Access the career track via this link. 03. Data Analyst with Python Data Analyst with Python DataCamp Access the career track via this link. 04. Data Engineer with Python Data Engineer with Python DataCamp Access the career track via this link. 05. Machine Learning Scientist with Python Machine Learning Scientist with Python DataCamp Access the career track via this link. 05. Machine Learning for Everyone Machine Learning for Everyone DataCamp Access the career track via this link. I hope you enjoyed this guide on how to become a data scientist! Don't hesitate to contact me if you have a constructive comment or suggestion on how to improve this guide 🤝 If you want more content like this one, follow me on Instagram or subscribe to my newsletter!👩🏽💻

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