How difficult is it to be a data scientist?

Question

Is Data Science career difficult to pursue? What challenges will I face while learning Data Science and becoming a Data Scientist?

Answer 1

Data Science requires years of dedication and practice. The Data Scientist title can only be earned through experience. This multidisciplinary domain requires skills in: 

• Programming

• Big Data Hadoop

• Machine Learning

• AI

• Statistics

• Linear Algebra 

• Databases

• Data processing

• Multivariable Calculus 

• Data Visualization

• Communication  

Of course, mastering all the above would be a very ideal scenario but one could start off by trying to master a few of them and two or three languages from the following list:  

• Python

• SQL

• R

• Julia

• Matlab

• Mathematica

• C++

• Java

Apart from this, the most crucial part of becoming a Data Scientist is to extend your practice to as many real-world projects as you can. The more you are hands-on, the more skilled you will be with the added experience. Nowadays, organizations hire based on specific skill sets rather than qualifications. So, projects are a great way to showcase your skills.

 There is no shortage of studying resources online when it comes to learning about Data Science and the best part is that you can do it at your own pace. Video tutorials, informative blogs, e-books, journals, etc. are all readily available.

 This video tutorial by Intellipaat is designed specifically for Data Science aspirants and beginners:

As much as self-studying can be helpful, it is not sufficient. At one point, you have to make the smart decision of training under someone with more experience in the field. This way you are less likely to miss out on crucial topics and receive expert help whenever you are in need.  

Intellipaat offers a Data Science online course, which is in collaboration with IBM. The course includes:  

• Data Exploration

• Data Manipulation

• Data Visualization

• Statistics

• Machine Learning

• Logistic Regression

• Decision Trees and Random Forest

• Unsupervised Learning

• Association Rule Mining and Recommendation Engines

 The course also ensures work on real-time Data Science projects and offers placement assistance after the course completion.