**Data science** is becoming a hot topic of discussion around the world. Data science is a technology that uses mathematics, probabilities, statistical analysis, algorithms to process data into various buckets to provide insights to solve some complex problems. The data comes in various like structured data, semi-structured data, and unstructured data. There are other forms of the data, however, we will limit the analysis of data in these three forms provided earlier. The **applications of data science** are solving many complex problems across the world

**Data scientist jobs** are constantly rising by creating demand across the globe. It is predicted that by 2026 the demand for data scientist jobs will be as high as 40 million globally. There is a gap for data science professionals in today’s world due to fewer skills. To become a data scientist one must learn how the data is identified, classified, processed, stored, and used to get the many insights into the data.

The salary for **data science jobs** is approximately 26% higher when compared to other categories.

**Look at the Salary Trends:**

**Who requires data scientist training – suitability check**

Anyone with passion can learn programming, **data science tools**, math, statistics, and algorithms to become a data scientist.

Data Science training is required for those who are in the industry as well as for fresh aspirants. Personally, I would recommend my readers to enhance their data science skills with ‘Great Learning’ in association with the University of Texas with various modes of delivery and certification from the University of Austin!

If you are having 2+ years of programming experience and want to Shift your domain to data science, then acquire data science certification in M.Tech and get Hands-on experience in Python, SQL, and other Data Science techniques.

**Suitability Check **

One needs to have a keen interest in understanding problems and solving them using data sets with knowledge in mathematics, statistics, algorithms, and programming.

- Tools such as Tableau, Big data tools, helps you to experience and see whether this subject is suitable for you.
- Programming Intrest is a must.
- Knowledge of store data, process data, using data science algorithms, statistics, and tools can help you stand out from the crowd.
- You need to learn concepts like data wrangling techniques, data visualization techniques.
- Knowledge of multivariable calculus and linear algebra is an added advantage.
- Knowledge of Python or SAS programming language is a must.
- One needs to be good at communication, working in a team, and collaboration skills.

**Follow these steps to learn data science**

Data science can be learned by following ways

**Prepare for the Data science career –** Once you decide to take up a **data science career** you need to look for learning some mathematics, computer science, computer engineering, or algorithms. Programming languages such as R, SAS, Python, and Java are very important to learn. Start mastering these languages. Even when you are in secondary school learn linear algebra, differential calculus, and statistics. Show interest and passion for these subjects.

**Earn an undergraduate degree – **To pursue your career as a data science one must complete a degree preferably inmathematics, statistics, computer science, data science, or computer engineering degrees. This helps you to build a strong foundation.

**Get into the IT industry – **After earning the undergraduate degree go for the entry-level job in data science or programming to get expertise in **data science applications**.

**Earn an online master’s degree –** Once you land a job, that doesn’t mean you have to stop studying, to sustain yourself in the industry one can take up a master’s in data science courses online from reputed universities like Northwestern University, Great Lakes Institutes, etc and enhance your skills.

**Top 2 Learning sources for mastering data science**

**Books**

Reading books in detail can help you to build a strong foundation in data science concepts.

Here I have tried to consolidate an important book list to understand the data science concepts, they are :

- Big Data – A revolution
- Data mining techniques
- Data science and big data analytics
- Data science for business
- Designing data-driven applications
- Headfirst statistics
- Data Scientist Guide: Introduction to Machine Learning with Python
- Intro to Probability
- Pattern recognition and machine learning
- Practical data science with R
- Practical statistics for a data scientist
- Python for data analysis
- Python Machine Learning by example
- R for data science
- Storytelling by data
- The data science handbook
- Thinking with data

**Free Courses and Blogs**

There are a lot of educational platforms/websites which offers free courses in data science Some of them are listed below:

- Codementor | Data Science Tutorials
- Great Learning Academy | Free Data Science course
- Data plus science
- Data science | Google News
- Data science | Reddit
- Data science 101
- Data science central
- Data Science Dojo
- Data Science for social good
- DataRobot | Machine learning software
- Datascience@Berkeley Online learning blog
- DATAVERSITY
- Domino Data Science blog
- Kaggle | Data science news
- KDnuggets | Big Data, and Data Mining
- NYC Data Science Academy Blog
- Revolution Analytics

**Wrapping up**

The certified data science professionals are in constant demand as per the report by one of the leading survey companies. The Bureau of Labor Statistics (USA) found the demand for data science professionals will go up to a 28 percent job rise by 2026 in this field. The industry demands certified data science professionals over professionals who do not have active data science certifications.