Data science is a rapidly growing field essentially offering a lot of potentials. Experts predict the data science market will grow in excess of 500% between 2019 and 2026. The main reason driving this growth is the increasing importance of data analysis in guiding business decision-making.
Data science basically uses analytics and computational methods to extract insights from data, build predictive models and develop new algorithms. Though learning a new discipline can be challenging, working with a well-crafted plan makes it much less cumbersome.
Whether you are transitioning from another career path or fresh from school, this roadmap is designed to provide you a structured, step-by-step approach that will help you build a strong foundation in data science.

Data Science Skills Framework -

Programming
Machine Learning
Statistics & Mathematics

Data Extraction & Wrangling

Exploratory Data Analysis & Storytelling

Programming
Most data science jobs require some level of programming expertise in a programming language (e.g. Python ) or a query language (e.g. SQL)

Machine Learning
Machine learning is a subset of artificial intelligence. With ML you leverage data to train a model to perform some set of tasks (e.g. classification, prediction).

Data Extraction & Wrangling
A big part of data science work is to find data that can help you solve your problem. Data is rarely clean and formatted for use in the “real world”. It’s vital that you spot any error in your data before investing too much time in analysis.
Exploratory Data Analysis & Storytelling
Drawing insights from data and communicating it to stakeholders – often visually and in simple terms – is a core competency for any data scientist.

Statistics & Mathematics
Statistical and mathematical methods are a central part of data science. Why? Because it will be difficult to build algorithms, perform analysis, and uncover insights, unless you have a sold grasp of things like linear algebra, calculus, and probability.
Machine Learning
Machine learning is a subset of artificial intelligence. With ML you leverage data to train a model to perform some set of tasks (e.g. classification, prediction).

Exploratory Data Analysis & Storytelling
Drawing insights from data and communicating it to stakeholders – often visually and in simple terms – is a core competency for any data scientist.

Key Tools for Data Science

• Programming Languages – Python, SQL
• Machine Learning Libraries – Tensorflow, Keras, Scikit-learn
• Data Storage and Management Systems – MySQL, PostgreSQL, SSMS
• Data Visualization Tools – Tableau, Power BI, Matplotlib
• Cloud computing Platforms – AWS, Azure, Google cloud platform

Data science is a multidisciplinary field hence has several career paths. Each career path requires some specific skillset resulting to different roadmaps. Below, we will look at the different career paths in data science and the main skillset required.
Data Analyst
Data analysts are responsible for collecting, processing, interpreting, and performing statistical data. They primarily use programming languages and frameworks to review data and make inferences. Then, they present results for management teams to make better decisions.

Main Skillset Required -
• Software engineering
• Data extraction and wrangling
• Exploratory data analysis and storytelling
• Statistics and Mathematics

Data Engineer
Data engineers develop and maintain the data infrastructure, which determines how a company collects and stores data. They build data pipelines that transform raw, unstructured data into usable formats that data scientists and analysts can use.

Main Skillset Required –
• Software engineering
• Data extraction and wrangling

Data Scientist
Data scientists perform analytics and build machine learning models. Their tasks help companies develop new business strategies and determine long-term goals. Data scientists also build internal data products, which can help a company better understand its workforce, processes, and customers.

Main Skillset Required –
• Software engineering
• Data extraction and wrangling
• Exploratory data analysis and storytelling
• Statistics and mathematics
• Machine learning.

Machine Learning Engineer
Machine learning engineers work with vast quantities of data and perform complex data modelling. They design self-running software that uses previous data to improve the program’s functionality. Machine learning engineers also perform machine learning tests, check data quality, and collaborate with other members of a data science team, such as data scientists, data analysts and administrators.

Main Skillset Required –
• Software engineering
• Data extraction and wrangling
• Machine learning.

Research Scientist
A newly formalized role, a research scientist in machine learning is responsible for researching and developing new methods, algorithms, and approaches to data science. ML scientists are generally a part of the Research and Development (R&D) division in any organization. They are in charge of finding innovative data processing and analysis approaches, often leading to published work.

Main Skillset Required –
• Data extraction and wrangling
• Exploratory data analysis and storytelling
• Statistics and mathematics
• Machine learning.

Track Your Learning Process
Irrespective of your choice of career path in data science, you must have a means of tracking your progress with focus on the required skillset. This way, you know what you've already covered, and you can better visualize what you need to do next.