The field of data science is strong, expanding quickly, and has many unrealized potentials. The market is predicted to grow significantly over the next seven years, from $37.9 billion in 2019 to $230.80 billion in 2026, according to LinkedIn’s Emerging Jobs Report.
As a result, data science should be the entry point for aspiring IT professionals interested in a long career. But picking up a new discipline can be difficult. By developing and implementing a sound educational plan, or roadmap, difficulty can be reduced.
The information required to develop a data science road map for 2022 is provided in this article. We will describe the data science roadmap, its various elements and milestones, how to track your progress on the data science roadmap, and other relevant resources.

We begin with the basics. What is a roadmap for data science?

A Data Science Roadmap: What Is It?

To answer this question simply, let’s first define what a “roadmap” is. Maps are strategic plans that identify a goal or desired result and list the key steps or milestones needed to get there.

In contrast, data science is described in this article as:

A discipline that works with semi-structured, structured, and unstructured data. It involves procedures like data preparation, analysis, and cleaning, among other things.

Combining statistics, math, programming, and problem-solving skills, data science is the activity of cleaning, preparing, and aligning data. It also involves the ability to look at things differently.

A strategic plan intended to assist the aspiring IT professional in learning about and succeeding in the field of data science is thus visualised in a data science roadmap.

Let’s examine this data science road map in more detail.

Understanding programming or software engineering

You need to have a strong foundation before you start your data science journey. Software engineering or programming expertise and experience are needed in the data science field. A minimum of one programming language, such as Python, SQL, Scala, Java, or R, should be learned.

Included Programming Topics

Common data structures (such as dictionaries, data types, lists, sets, and tuples), searching and sorting algorithms, logic, control flow, writing functions, object-oriented programming, and how to use third-party libraries are all things that data scientists should become familiar with.

Studying Data Cleaning and Collection

Finding useful data that solves problems is a common task for data scientists. They gather this information from a wide range of resources, such as databases, APIs, public data repositories, and even scraping if the site permits it.

How to Study Storytelling, Exploratory Data Analysis, and Business Acumen

It’s time to advance to the data analysis and storytelling stages of your data science roadmap. Data analysts, who have a close relationship with data scientists, analyse data to draw conclusions, then present their findings to management in simple language and visual representations.

Why You Should Study Data Engineering

At large data-driven organisations, data engineering supports the Research and Development teams by ensuring that clean data is easily accessible for research engineers and scientists. If you want to concentrate primarily on the statistical side of things, you can skip this section even though data engineering is a completely different field.

How to Learn Applied Mathematics and Statistics

The majority of data science interviews concentrate on inferential and descriptive statistics, which are fundamental to the field of data science. The path to a deeper comprehension of how algorithms function is paved by mathematics and statistics.

Finally, learn about machine learning and artificial intelligence (AI).

It’s time to wrap up your journey by learning about two industries that heavily rely on data science: artificial intelligence and machine learning, as you near the end of your data science roadmap. These subjects can be divided into three groups:

Reinforcement Learning is a discipline that assists you in developing self-rewarding systems. Use the TF-Agents library, build Deep Q-networks, learn how to optimise rewards, and more if you want to comprehend reinforcement learning.

Supervised Learning is concerned with regression and classification problems. It would be beneficial if you studied naive Bayes, tree models, ensemble models, KNNs, multiple regression, logistic regression, polynomial regression, simple linear regression, and multiple regression with logistic regression. Study evaluation metrics to complete your education.

Unsupervised Learning: Applications of unsupervised learning include dimensionality reduction and clustering. Go in-depth with gaussian mixtures, PCA, K-means clustering, and hierarchical clustering.