Data science has rapidly grown as a successful career option for developers over the past few years all around the world. With the growing opportunities of artificial intelligence, machine learning, deep learning, and other areas, data science has become a suitable field of research and work.
I am writing this blog post on an introduction to data science by focusing on the students and job holders who are interested in data science as a career.
Let us begin with an understandable intro to data science and move on with various implementations of data science.
What is data science?
Data Science is a meticulous study of the flow of information from the enormous data present in an organization’s repository. It is the study of patterns and other phenomena that makes use of algorithms, data analysis, statistics, deep learning, and machine learning techniques.
The core of data science lies in how inferences can be drawn and patterns seen by using raw and unstructured information or data. Mining a lot of structured and unstructured data to distinguish the patterns can enable a corporation or organization to get control over costs, increment efficiencies, perceive new market openings, and outpace the organization’s competition.
Data Science is the study of where data comes from, what it signifies, and how it can be transformed into a worthwhile resource in the formulation of business and IT strategies.
![What is Data Science? A Beginner’s Guide to Data Science]()
Data science can allow various business problems to be solved by developing a lot of applications that influence large data sets. It can also more extensively sway society by having an impact on culture, legislative issues, wellbeing, and prosperity – in this sense, the genuine capability of statistics and mathematics, all things considered, can be unleashed utilizing the tools of data science.
Why develop a career in the data science field?
All the business organizations and software companies are concentrating on data science in a big way because it has the capacity to complete the same work that hundreds of employees do in an organization.
The companies will get highly provable information using data science technology. It reduces the wages of employees, and the bad thing is that data science will kill many jobs in the industry.
Data science is dramatically altering our lives and shaping our future with the world economy.
Recently, the World Economic Forum published a report suggesting that between now and 2022, about 75 million jobs may be lost to machines and algorithms. The report further says that 133 million new roles may emerge due to the rapid evolution of Machines and AI in the workplace. That translates into 58 million net new jobs that will be created in the next few years alone.
Data science represents an excellent opportunity for a career and for those already in the workforce that are rescaling and upscaling with future-oriented job skills such as AI, Machine Learning, Deep Learning.
| JOB ROLE |
AVERAGE ANNUAL SALARY (0-3 YEARS OF EXPERIENCE) |
SALARY RANGE (LPA) |
| Machine Learning Engineer |
7 lakhs |
3.2 to 20 |
| Data Scientist |
6.3 lakhs |
3 to 20 |
| Business Analyst |
5.8 lakhs |
2.5 to 10 |
| Statistical Analyst |
5.8 lakhs |
1.9 to 10 |
| Data Engineer |
5 lakhs |
3.4 to 20 |
| Data Analyst |
4.9 lakhs |
1.9 to 8.2 |
Data science is becoming increasingly skill-based in both prominent companies to smaller startups.
Google is one of the most significant organizations recruiting a large number of trained Data Scientists. Since Google is, for the most part, determined by Data Science, Artificial Intelligence, and Machine Learning nowadays, it offers conceivably the best data Science compensations to its representatives.
Amazon is a global cloud computing and e-commerce titan that is hiring Data Scientists on a massive scale. Amazon needs the Data Scientists to identify the customer mindset and magnify the geographical reach of both e-commerce and cloud domains.
Data Science Components
The Major Key components of Data science are,
Data
Data can be of various types like Structured Data and Unstructured Data, where the structured data will be mostly in a tabular form, and Unstructured data like Images, Videos, Audio, PDF Files, Emails, etc. Data is the primary foundation of Data science.
Programming
Data Analysis and Data Management is done using computer programming. The most popular programming languages are Python and R.
Probability and Statistics
The mathematical foundation of Data Science is probability and statistics. We will surely get incorrect conclusions at the end if we do not have explicit knowledge of Probability and Statistics. That the reason why statistics and probability perform a central part in Data Science.
Machine Learning
The major part of data science has the capacity to complete the same work as hundreds of employees. It can be done using algorithms such as regression and classification methods. A data scientist should be thorough with the machine learning algorithms to predict worthy insights from the available data set.
Big Data
Developers can extract lots of types of information from raw data. Big data can mainly work with three v’s or different working properties such as Volume, Velocity, and Variety. In the current world, trillions of data points are being stored, and data scientists are working on the various types of data using tools like Hadoop, Pig, R, Apache Spark, etc.
I hope you enjoyed reading my blog and understood what Data Science is. I will write more posts to help you understand Data Science and AI, Machine Learning, and Deep Learning topics in depth.