How to Start Journey of Data Science?

How to Start Journey of Data Science?

Introduction

What is data science?

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: “use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems”.

How data science is helpful to mankind?

Data can be used for many purposes:

– quality assurance

– to find actionable patterns (stock trading, fraud detection)

– for resale to your business clients

– to optimize decisions and processes (operations research)

– for investigation and discovery (IRS, litigation, fraud detection, root cause analysis)

– machine-to-machine communication (automated bidding systems, automated driving)

– predictions (sales forecasts, growth, and financial predictions, weather)

Fast delivery is better than extreme accuracy. All data sets are dirty anyway. Find the perfect compromise between perfection and fast return.

Why we should learn data science?

Here are some reasons for learning data science:

1. We live in a digital world, everything is data-driven. There is data science in business, accounting, education, science, engineering, healthcare, technology, energy sector, government, and so on. So having the ability to work with data is an essential skill.

2. Data science is also a very promising field with lots of high paying job opportunities.

3. Basic data science skills are important for personal use, for example managing finance, data-driven decision making, etc.

How can you learn data science at an university?

This are the subject we get to learn in the university

Subject to be covered under data science

Java: — Java applications are typically compiled to bytecode that can run on any Java virtual machine regardless of the underlying computer architecture. The syntax of Java is similar to C and C++, but it has fewer low-level facilities than either of them.

Linux: — Linux is the best-known and most-used open source operating system. As an operating system, Linux is software that sits underneath all of the other software on a computer, receiving requests from those programs and relaying these requests to the computer’s hardware.

R: — R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in popularity; as of September 2019, R ranks 19th in the TIOBE index, a measure of popularity of programming language

Python: — python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

SQL: -SQL which stands for Structured Query Language, is a programming language that is used to communicate with and manipulate databases. In order to get the most of the mounds of data they collect, many businesses must become versed in SQL.

NoSQL: -A NoSQL (originally referring to non-SQL, non-relational or not only SQL) database provides a mechanism for storage and retrieval of data which is modelled in means other than the tabular relations used in relational databases.

Conclusion:

With each passing year, the data will only continue to increase and add to the already massive pile of data. It is not possible for traditional BI tools to analyse such a vast volume of unstructured datasets — they demand more advanced and intelligent analytical tools for storing, processing, and analysing data.

Thanks to Data Science, new and exciting possibilities are opening up, continually changing the way we see the world around us. Data Science’s contribution to changing human lives for the better has been immense.

Not just these, Data Science has also contributed immensely to the healthcare sector. Data Science algorithms and applications can be found in Genomics, Drug Development, Medical Image Analysis, Remote Monitoring, to name a few.