Why and how to become a Data Scientist?
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.
Benefits for you
A structured course tree
A carefully tailored list of courses for best experience developing your skills, including only the essentials and skipping the usual college surpluses.
Learn from experienced teachers
Improve your skill set with proven tools, and take opportunities to practice with realistic tasks.
Get a dream job
Make additions to your résumé to secure your dream job with high pay. Send applications anywhere in the world!
Get skills for life
Even if you choose to stop midway, you’ll have acquired skills that you’ll be able to use in many other fields.
Data science graduates work at:
Data Scientist salary figures in global markets
Average yearly pay
$74,400
- USA $117,000
- Australia $102,000
- Japan $83,000
- Canada $78,000
- Norway $70,000
- Switzerland $70,000
- Germany $59,000
- Netherlands $57,000
- UK $55,000
- France $52,000
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.
Get the job you dream of.
The demand is Huge!
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.
Your Learning Path
1
Business analytics
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.
10 lectures
Data Science for Business
Learn the fundamentals of using data science for business, create data analytics strategy and back up your problem-solving practices with data analysis.
Start Learning Now2
Statistics
In Statistics, you study methodologies for data gathering, reviewing, analysis and drawing insights to make better-informed business decisions.
44 lectures
Master Elementary Statistics
The course is designed to cover all topics needed to ace the AP Statistics exam, very suitable for a junior data scientists.
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4 lectures
Intro to Statistics with R
Learn skills for data scientist by studying key statistical concepts and techniques like exploratory data analysis, correlation, regression, and inference.
Start Learning Now3
Linear algebra
Acquiring linear algebra skills will boost your understanding of how to apply various data science algorithms and how they really work under the hood.
14 lectures
Beginning Algebra
Build a better understanding of variables, grouping symbols, equations, how to turn words into symbols and sentences into equations.
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15 lectures
Linear Algebra - Foundations to Frontiers
An in-depth course where you’ll learn to link linear algebra to matrix software development
Start Learning Now4
Data visualization and soft skills
Master the tools that will help you convert complex data from your projects to a form that will be easy to understand for others.
19 lectures
Data Visualization
Learn Tableau Prep and Tableau Desktop to prepare, analyze, and show your data so that others can comprehend.
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4 lectures
Business Data visualization with Tableau
Follow the best practices to combine, assess the data and learn to represent them for your intended audience with Tableau.e
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18 lectures
Integrated Marketing Communication
An opportunity to learn soft skills for presenting your ideas and projects in a manner that will be compelling and clear to your audience.
Start Learning Now5
General development/coding skills
Employers are looking for data scientists who have at least basic experience with general development and coding.
23 lectures
How to use GIT commands
Learn the essentials of GIT commands for DevOps and get the skills using state of the art version control system.
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4 lectures
Version control with Git
Build a strong conceptual understanding of the Git version control system to manage team files for small and large projects.
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120 lectures
Data science course on R
A comprehensive data science course that will help you tackle a must-have skill for any data scientist today.
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97 lectures
Python Dictionary, Python For Loop & Much More
This Python tutorial will take you from the basics until you can use the unique Python syntax on your own.
Start Learning Now6
Query database
Learning to extract data from a database and presenting insights in an intelligible form is at the root of the data scientist’s job.
20 lectures
Querying and managing data with SQL
SQL is the number one programming language with a particular purpose for managing data. Learn SQL for storing, querying and manipulating data.
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26 lectures
SQL and Database Core Concepts
Cover all major SQL concepts and learn to write a query from scratch in a short time as part of a data scientist training.
Start Learning Now7
Engineering: Database Design
It’s easy for users to access the essential information in a database that performs well and adapts to future needs.
15 lectures
Database design using ER modelling and Normalization technique
Learn the basic concepts and definitions and then practice building an ER model and turn it into a physical database design for any application.
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15 lectures
Database Systems, Concepts and Design
Learn the database concepts, techniques, and tools to develop a database application to be used in a real-world environment.
Start Learning Now8
Engineering: Building data pipelines
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.
15 lectures
Build Big Data Pipelines w/ Hadoop, Flume, Pig, MangoDB
Learn to build big data pipelines using multiple technologies to solve real business problems.
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32 lectures
Building Data Pipelines with Python
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.
Start Learning Now9
Engineering: Distributed data systems
Having the ability to distribute logically interrelated databases on a decentralized network brings more capabilities for scalable data processing.
4 lectures
Distributed Database Systems
Address the components of distributed database systems, and get skills working with their architectures, storage & indexing, query processing, and other vital topics.
Start Learning Now10
Machine Learning: Data cleaning & manipulation
Data cleaning & manipulation processes leave you with complete, correct, accurate and relevant parts of the data that you can effectively work with.
18 lectures
Master course preparing and cleaning data with Tableau Prep 2018
Learn the basic concepts and create data flows with Tableau Prep to practice with its functionality in your pilot project.
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4 lectures
Getting and Cleaning data
You’ll find out how to obtain data from various sources and in various formats, and then how to make them tidy for further processing.
Start Learning Now11
Machine Learning: Use-case models
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.
21 lectures
Business Analysis: Working with Use Cases
A course for business analysts to learn the methodology with techniques for system analysis and modeling for business purposes.
Start Learning Now12
Machine Learning: Algorithms
Machine learning algorithms – without human intervention – can learn from data and experience, therefore, a must-have on a data scientist’s toolbelt.
10 lectures
Master Machine Learning Algorithms
A course on machine learning algorithms showing the benefits to data scientists.
Start Learning Now13
Machine Learning: Feature engineering
Feature engineering is the art of creating new input features from a raw dataset so that machine learning algorithms do their job.
6 lectures
Feature Engineering
Learn to transform features to use them optimally in your machine learning models with greater accuracy.
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85 lectures
Feature engineering for Machine Learning
Make use of a rich compilation of various techniques used for feature transformation to extract the most predictive power out of raw datasets.
Start Learning Now14
Machine Learning: Model evaluation
As part of the model development process, model evaluation employs test sets to check model performance.
4 lectures
Practical Machine Learning
A good portion of this course will be dealing with cross-validation to learn evaluate models.
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43 lectures
Machine Learning Masterclass
If you are super keen on building more intelligent apps using Machine Learning, this new foundational framework is one to take advantage of!
Start Learning Now15
Machine Learning in trading
Machine learning for trading is commonly used at predicting the range for short-term price movements providing a certain level of confidence.
8 lectures
Machine learning for trading
Learn about the challenges of using machine learning in trading and how to use it in realistic situations.
Start Learning Now16
Model improvement: Ensemble method
At data science, it’s essential to combine several machine learning models into one predictive model with meta-algorithms – ensemble methods.
4 lectures
Introduction to Ensemble Methods
Learn about the main ensembling techniques and get practical experience with data modeling in various domains – in a competitive environment.
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42 lectures
Ensemble Machine Learning in Python: Random Forest, AdaBoost
Gain a deeper understanding of what happens under the hood with machine learning models.
Start Learning Now17
Model improvement: Dimensionality reduction
Data scientists use dimensionality reduction algorithms to reduce the number of random variables under consideration to reduce the complexity of extracted data features.
18 lectures
Principal Component Analysis (PCA) and Factor Analysis
Learn to take advantage of Principal Component Analysis at dimensionality reduction and reduce the complexity of variables.
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4 lectures
Advanced Machine Learning and Signal Processing
This course includes a section on unsupervised machine learning and gives you data scientist skills and understanding to reduce dimensionality.
Start Learning Now18
Natural language processing (NLP)
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.
108 lectures
Data Science: Data Mining and Natural Language Processing in R
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.
Start Learning Now19
Deep learning
Mastering deep learning will add one of the latest data scientist qualifications to your skillset to build AI systems.
4 lectures
Neutral Networks and Deep Learning
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.
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Mission Accomplished
You will learn from these experts
Caleb Stultz
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 Akram
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 Rakesh Chinta
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 Mandachi
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
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.
University of Texas at Austin teachers
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.
PhDs in Biostatistics
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.
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.
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Featured Review
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.
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Learn from real experts in their fields who share their knowledge and practical know-how.
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Prove what you’ve learned to anyone who asks, and hang sweet additions to your wall of achievements.
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Find opportunities to practice with code examples, practical tasks, learning missions, etc.
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Feel even more motivated to get skills for your career with a number of gaming elements!
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Get your dream job
Increase your chances to secure a job that you dream of by focusing on the skills that you actually need.
Don’t miss the chance to develop into a Data Scientist and be in high demand anywhere on Earth!
Start Learning NowFAQ
What is a Data Scientist?
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.
What does a Data Scientist do?
The specific roles and duties vary in each organization, but typically, the main things Data Scientists do are these:
- Collecting large amounts of unstructured data and turning it to be more usable.
- Solving business problems with data-driven techniques.
- Developing data analysis algorithms with R, Python, SAS languages, for example.
- Working on statistical tests, distributions, and drawing insights from big data.
- Communicating and working together with colleagues from business and IT sides.
- Visualizing insights so that non-IT business people could understand what data presents.
How to become a Data Scientist?
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.
How long does it take to become a data scientist?
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.
How much does a Data Scientist make?
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.
How to get a job as a Data Scientist?
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!
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