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.

Daniel Mandachi 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.

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2

Statistics

In Statistics, you study methodologies for data gathering, reviewing, analysis and drawing insights to make better-informed business decisions.

Jerry Linch 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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Duke University 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.

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3

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.

Math Fortress 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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UTAustinX 15 lectures
Linear Algebra - Foundations to Frontiers

An in-depth course where you’ll learn to link linear algebra to matrix software development

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4

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.

Tableau 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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University of California 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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Jerome Juska, Ph.D. 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.

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5

General development/coding skills

Employers are looking for data scientists who have at least basic experience with general development and coding.

Aravindhan Dhayalan 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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Steve Byrnes 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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Up Degree 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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BitDegree Foundation VSI © 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.

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6

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.

Khan Academy 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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Jazeb Akram 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.

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7

Engineering: Database Design

It’s easy for users to access the essential information in a database that performs well and adapts to future needs.

Een jeen 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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GTx 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.

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8

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.

V2 Maestros, LLC 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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Katharine Jarmul 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.

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9

Engineering: Distributed data systems

Having the ability to distribute logically interrelated databases on a decentralized network brings more capabilities for scalable data processing.

Arizona State University 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.

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10

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.

Jamie Fry 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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Johns Hopkins University 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.

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11

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.

Don Hussey 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.

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12

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.

Naga Rakesh Chinta 10 lectures
Master Machine Learning Algorithms

A course on machine learning algorithms showing the benefits to data scientists.

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13

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.

Google Cloud Training 6 lectures
Feature Engineering

Learn to transform features to use them optimally in your machine learning models with greater accuracy.

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Soledad Galli 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.

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14

Machine Learning: Model evaluation

As part of the model development process, model evaluation employs test sets to check model performance.

Johns Hopkins University 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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Caleb Stultz 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!

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15

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.

Georgia Tech 8 lectures
Machine learning for trading

Learn about the challenges of using machine learning in trading and how to use it in realistic situations.

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16

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.

National Research University Higher School of Economics 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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Lazy Programmer Inc. 42 lectures
Ensemble Machine Learning in Python: Random Forest, AdaBoost

Gain a deeper understanding of what happens under the hood with machine learning models.

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17

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.

Gopal Prasad Malakar 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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IBM 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.

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18

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.

Minerva Singh 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.

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19

Deep learning

Mastering deep learning will add one of the latest data scientist qualifications to your skillset to build AI systems.

deeplearning.ai 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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Learning path complete

Mission Accomplished

You will learn from these experts

instructor
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!

instructor
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.

instructor
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.

instructor
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.

instructor
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.

instructor
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.

instructor
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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Don’t miss the chance to develop into a Data Scientist and be in high demand anywhere on Earth!

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