Harvard Business school has named Data Scientist’s profession the ‘sexiest job of the 21st century’. The ability to manipulate big data and make insights from them earns a lot of money today. The demand for qualified data scientists has been exceeding supply in recent years. Why? Because this complex role demands multidisciplinary skills and experience. How to become a data scientist? Take and keep our data scientist roadmap in front of your eyes, and learn what it takes to secure a high paying position!
A data scientist must be capable of using advanced analytics technologies, machine learning and predictive modeling to go beyond statistical analysis and to identify patterns, trends, and relationships in sets of data. BitDegree encourages you to improve your skill-set immediately! Use our roadmap with plenty of data science related courses, and raise your value to hit your dream career.
A carefully tailored list of courses for best experience developing your skills, including only the essentials and skipping the usual college surpluses.
Improve your skill set with proven tools, and take opportunities to practice with realistic tasks.
Make additions to your résumé to secure your dream job with high pay. Send applications anywhere in the world!
Even if you choose to stop midway, you’ll have acquired skills that you’ll be able to use in many other fields.
The graph shows the average data scientist annual salaries in different markets. You need a bunch of skills to succeed, but once you have them, the money will come. And rightfully so! Although we’ve combined the data provided by Glassdoor, Indeed, Ziprecruiter and other trusted sources, these figures may vary significantly depending on changing trends and your experience.
There are thousands of Data scientist openings for qualified specialists. Build your expertise in the core fields that will add to your resume and help you become a data scientist. Get a solid foundation by learning statistics, linear algebra, general development and coding languages, data manipulation, machine learning and other important skills.
As a modern data scientist, you need to shift focus on a strategic perspective on big data and analytics to help businesses utilize and allocate resources in these areas.
Learn the fundamentals of using data science for business, create data analytics strategy and back up your problem-solving practices with data analysis.See Learning Paths
In Statistics, you study methodologies for data gathering, reviewing, analysis and drawing insights to make better-informed business decisions.
The course is designed to cover all topics needed to ace the AP Statistics exam, very suitable for a junior data scientists.See Learning Paths
Learn skills for data scientist by studying key statistical concepts and techniques like exploratory data analysis, correlation, regression, and inference.See Learning Paths
Acquiring linear algebra skills will boost your understanding of how to apply various data science algorithms and how they really work under the hood.
Build a better understanding of variables, grouping symbols, equations, how to turn words into symbols and sentences into equations.See Learning Paths
Master the tools that will help you convert complex data from your projects to a form that will be easy to understand for others.
Learn Tableau Prep and Tableau Desktop to prepare, analyze, and show your data so that others can comprehend.See Learning Paths
Follow the best practices to combine, assess the data and learn to represent them for your intended audience with Tableau.eSee Learning Paths
An opportunity to learn soft skills for presenting your ideas and projects in a manner that will be compelling and clear to your audience.See Learning Paths
Employers are looking for data scientists who have at least basic experience with general development and coding.
Learn the essentials of GIT commands for DevOps and get the skills using state of the art version control system.See Learning Paths
Build a strong conceptual understanding of the Git version control system to manage team files for small and large projects.See Learning Paths
A comprehensive data science course that will help you tackle a must-have skill for any data scientist today.See Learning Paths
Learning to extract data from a database and presenting insights in an intelligible form is at the root of the data scientist’s job.
SQL is the number one programming language with a particular purpose for managing data. Learn SQL for storing, querying and manipulating data.See Learning Paths
It’s easy for users to access the essential information in a database that performs well and adapts to future needs.
Learn the basic concepts and definitions and then practice building an ER model and turn it into a physical database design for any application.See Learning Paths
Data scientists need building data pipelines to perform many automated jobs to extract the necessary data and have it in one place and the same format.
Learn to build big data pipelines using multiple technologies to solve real business problems.See Learning Paths
Learn the architecture basics and variety of the most popular frameworks and tools to build data pipelines and automate workflows with Python 3 in your data scientist’s daily practice.See Learning Paths
Having the ability to distribute logically interrelated databases on a decentralized network brings more capabilities for scalable data processing.
Address the components of distributed database systems, and get skills working with their architectures, storage & indexing, query processing, and other vital topics.See Learning Paths
Data cleaning & manipulation processes leave you with complete, correct, accurate and relevant parts of the data that you can effectively work with.
Learn the basic concepts and create data flows with Tableau Prep to practice with its functionality in your pilot project.See Learning Paths
Use-case models are intended to communicate how the system behaves to the customer or user, so the system is what users expect it to be.
A course for business analysts to learn the methodology with techniques for system analysis and modeling for business purposes.See Learning Paths
Machine learning algorithms – without human intervention – can learn from data and experience, therefore, a must-have on a data scientist’s toolbelt.
Feature engineering is the art of creating new input features from a raw dataset so that machine learning algorithms do their job.
Learn to transform features to use them optimally in your machine learning models with greater accuracy.See Learning Paths
Make use of a rich compilation of various techniques used for feature transformation to extract the most predictive power out of raw datasets.See Learning Paths
As part of the model development process, model evaluation employs test sets to check model performance.
A good portion of this course will be dealing with cross-validation to learn evaluate models.See Learning Paths
If you are super keen on building more intelligent apps using Machine Learning, this new foundational framework is one to take advantage of!See Learning Paths
Machine learning for trading is commonly used at predicting the range for short-term price movements providing a certain level of confidence.
At data science, it’s essential to combine several machine learning models into one predictive model with meta-algorithms – ensemble methods.
Learn about the main ensembling techniques and get practical experience with data modeling in various domains – in a competitive environment.See Learning Paths
Data scientists use dimensionality reduction algorithms to reduce the number of random variables under consideration to reduce the complexity of extracted data features.
Learn to take advantage of Principal Component Analysis at dimensionality reduction and reduce the complexity of variables.See Learning Paths
This course includes a section on unsupervised machine learning and gives you data scientist skills and understanding to reduce dimensionality.See Learning Paths
NLP is a sub-field of artificial intelligence that enables data scientists to process natural human language, i.e., unstructured data, with computers to perform computations on them.
Learn to carry out pre-processing, visualization and machine learning tasks such as clustering, classification, and regression in R. You will be able to mine insights from text data to give yourself & your company a competitive edge.See Learning Paths
Mastering deep learning will add one of the latest data scientist qualifications to your skillset to build AI systems.
Get an understanding of the major trends driving deep learning and be ready not only to build but also train and apply deep neural networks.See Learning Paths
A developer and certified teacher who’s committed to excellence. Caleb has produced over 70 hours of content on iOS development, sharing his knowledge extensively!
Jazeb is a Computer Scientist, a freelancer himself, so he knows what skills are needed for daily work. He assists others in boosting careers in the field of programming.
Naga, a multi skilled professional, with a blend of coding and marketing experiences. He organizes his courses in an organic flow and in a form of real-life examples to make his content very practical.
Daniel focuses on creating quality courses that will ensure enjoyable learning. Having personal experience, he shares what it takes to become a good expert in the fields of business and finances.
Google Cloud Training instructors team will walk you through solutions and practices that you’ll find easily applicable. Working on your projects, you’ll be contributing to public learning resources.
Maggie Myers and Robert van de Geijn – people from the world of science who have an enormous amount of experience in real projects and academic environment.
Prof. Brian Caffo, Assoc. Prof. Jeff Leek, and Assoc. Prof. Roger D. Peng formed a team to guide students’ effective learning professionally so you get tangible career benefits.
We’ve selected only the experts with proven expertise that is worth your trust.
Using our Data Scientist roadmap, you should gain the essential skills and raise your value a great deal in the job market. However, the possibilities of learning are endless. Feel free to deepen your data scientist qualification even more choosing among a vast amount of courses on our platform that will suit your chosen craft.Keep Learning
I wanted to learn the skills to become a data scientist, but when I started to look for courses and tutorials, I got overwhelmed by the available supply! That’s the blessing and the curse of the age we live in. I found this roadmap to be very structured and giving clear guidelines to what I actually need to learn and focus only on the essentials. I can skip poor lecturers and content that I don’t need, because I prefer actual studying to looking for what to study.
Learn from real experts in their fields who share their knowledge and practical know-how.
Prove what you’ve learned to anyone who asks, and hang sweet additions to your wall of achievements.
Find opportunities to practice with code examples, practical tasks, learning missions, etc.
Feel even more motivated to get skills for your career with a number of gaming elements!
Coming soon – on BitDegree you’ll be able to receive a Blockchain certificate that is immune to falsification.
Increase your chances to secure a job that you dream of by focusing on the skills that you actually need.
Companies employ Data Scientists to analyze and interpret complex digital data to get insights that assist in making better-informed business decisions. It’s a multiskill job where a statistician, a computer scientist, and a trend-spotter are combined into one human being. This human being stands one foot in the business and the other foot in the IT world. Regarding the intellectual and educational capacity that the Data Scientist position requires, it’s quite a heavy lift. However, even if you stop in the midway of learning the craft, you’ll have a bunch of skills that you can use in many different contexts — no loss in any way.
The specific roles and duties vary in each organization, but typically, the main things Data Scientists do are these:
In today’s world, it’s all about the skills rather than university diplomas. We agree that having a Bachelor’s and Master’s degree will set you up for a start, but how many years will it take to graduate? 4-5-6 in total, depending on the country? That seems like a lot of years! These days, you can take shortcuts more effectively than ever before. Make a structured learning path and get skills in 9 major areas that are essential to an aspiring data scientist: statistics, linear algebra, general development/coding, query database, business analytics, data visualization, soft skills, data engineering, machine learning. Online courses are flexible in time and your ability to choose only what you need to learn, so you’ll end up saving much time that you can use for practice. Employers are happy with talents who have had hands-on experience, so choose your priorities wisely.
Like mentioned earlier, if you go down the traditional way – and there are reasons to do that – you’ll get the degree in 3 to 6 years, hopefully, getting enough opportunities to practice. Or you can choose only selected online courses, spend more time practicing, and you should be able to get the basic skills in under 18 months to be able to apply for junior positions. To become an expert, having the multidisciplinary nature of a data scientist’s work, it’s pretty much a lifelong learning case. Experienced data scientists in the US, Europe, and Asia report that 5 years is the average time it takes to become a good data scientist with knowledge and practical skills.
Typically, Data Scientist’s salary will depend on your experience and where your employer is located. It varies from an average annual salary of around $46,000 in the Netherlands to as much as $120,000 in the US. In the US market, the difference between an entry level Data Scientist and a senior specialist can be quite significant, $69,000 and $162,000 respectively. In Europe, those differences are smaller, and the average European Data Scientist earns around $53,500 per year.
With a healthy amount of patience, you need to prepare very well for the job. Not just the technical stuff, but also get ready to be a part of an organization where you’ll deal with colleagues. Before even applying for a position, have your portfolio ready (you might want to post your projects on GitHub). Develop a genuine interest in what Data Science professionals really do on a daily basis to get a better idea of what the job involves. Also, don’t forget that when somebody invites you for an interview, it means they need you as much as you need a job, so relax a bit and bring your best!