A Beginner's Guide to Data Science
It is very likely that you have heard about the potential and promise of data science in several quarters. If you have had some exposure to programming languages and latest development technologies, it is quite probable that you have heard about how data science has emerged as one of the most promising technology fields in the recent times. The so called rubrics and expressions like ?data is the new crude oil? are not bogus and baseless. There is enough basis for them.
If you want to make a career on this promising technology field as an expert, this is the right time to begin with. As the data science has not yet come of age and has just started to unleash its power for the real-life utilisations and enterprise applications of all types, the career opportunities for expert data scientists is huge.
These days Machine Learning and Data Science are trending topics in the industry with many new jobs being posted on the job portals. Programmers and other professionals can learn Data Science to grab these highly paying jobs in the industry. You can become Data Scientist without enrolling into regular college course just by reading and practicing with the various examples available on the internet. There are many resources on the Internet through which you can prepare yourself for any Data Science project. This tutorial will guide you in learning the right topics in Data Science. We have spent lot of time in researching on the topics and arranging them in the order to help in learning Data Science.
Let's help the aspirants of this field with the basic understandings starting from the streams of data science and the expertise areas they need to focus upon. In this article we are going to give you the topics you must learn to master Data Science.
What a Data Scientist Need to Learn?
Data science is basically about making sense of data and using the data to draw relevant insights and patterns for decision making, predictions and smarter algorithms that replace human intervention across crucial in-app processes.
Now, when it is about telling you about the specific things you need to learn, there are grossly 2 approaches. Firstly, from a technical standpoint it is important to have an in-depth understanding of the underlying technologies and the foundation. On the other hand, from a practical standpoint it is important to know about all the helpful programming libraries required for handling the actual data projects.
Data Science Foundations
Here is the summary of topics you must learn to become a data scientist.
Programming: At first make a choice of your programming language. Choose Python, TensorFlow or R and get ready to start coding.
Linear Algebra: Now you need to understand about representing data sets as matrices. For this you need command over concepts such as vectorization and orthogonality.
Calculus: As a foundation you need some command on calculus since several models written by you will make use of tools such as computing optimisation, derivatives, integrals for finding quicker solution to data problems.
Probability: A command over the probability will be further beneficial as data science mainly deals with tasks related to prediction of events based on the context and relation of events.
Statistics: Finally, you need to have a solid command over the elementary concepts of statistics to analyse information and come out with hidden insights. Testing hypothesis to understanding percentiles, you need elementary knowledge of various statistical concepts and tools.
Machine Learning: Machine Learning happens to be the core technology pertaining to the field of data science, which is mostly about training the computing machine with relevant user data, use patterns and user behaviour elements for delivering prompt and user-centric outputs.
Data Science Libraries
Finally, you need to be versed with variety of programming language libraries for using in the context of data science. You need to learn these libraries as they provide the methods and functions to handle easily a variety of programming tasks corresponding to the data science needs. You particularly have to learn the Python libraries for data science such as Numpy, Pandas, Matplotlib and Scikit-learn.
In this tutorial we have listed down the topics you must learn in Data Science. Check our following Data Science tutorials:
