Data science is the result of a merger
Before starting it is necessary to look at where Data Science comes from. Data science is the result of a merger between two fields that have been around for decades: statistics and computer science.
Now as the world entered the era of big data, the need for its storage also grew. It was the biggest challenge for the enterprise industries until 2010. Their main focus was on building a framework and finding solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, now the question arises for processing of this data. Data Science is the secret sauce here. All the ideas and tricks which Hollywood sci-fi movies imagine & we can see can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence.
What is Data Science?
Data Science is a mixture of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.
What is the need of Data Science?
Earlier the data was mostly structured and small in size, whose analysis was simple. But today most of the data is unstructured or semi-structured. The image given below shows that by 2020, more than 80 % of the data will be unstructured.
The above data is generated from different sources like financial logs, text files, multimedia forms, sensors, images, videos, and instruments. Simple BI tools cannot process this huge volume and variety of data. This is the reason we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.
Below infographic shows all the domains where Data Science is creating its impression.
What Do Data Scientists Do?
In day to day work, data scientists are often responsible for everything that happens to data, from collecting it, analyzing it and reporting on the results. Although every data science job is different, here’s one way to visualize the data science workflow, with some examples of typical tasks a data scientist might perform at each step.
It works like this:
- Capture data:
For example: Extracting the data from a company database, scraping it from a website, accessing an API, etc.
- Manage data:
For example: Correctly storing the data, and will almost always involve cleaning the data.
- Exploratory Analysis:
For example: performing different analyses and visualizing the data in various ways to look for patterns, questions, and opportunities for deeper study.
- Final Analysis:
For example: digging deeper into the data to answer specific business questions, and fine-tuning predictive models for the most accurate results.
For example: presenting the results of analysis to management, which might include writing a report, producing visualizations, and making recommendations based on the results of analysis. Reporting might also mean plugging the results of analysis into a data product or dashboard so that the data can be easily accesible by other team members or clients.
All of that said, what data scientists do from day to day can vary tremendously, in no small part because different companies make use of data science in different ways.
Data Science Components
- Data (and Its Various Types)
The raw dataset is the foundation of Data Science, and it can be of various types like structured data, unstructured dataand Semi Structured Data.
- Programming (Python and R)
Data management and analysis is done by computer programming. In Data Science, two main programming languages are : Python and R.
- Statistics and Probability
Data is manipulated to extract information out of it. Statistics and probability is the mathemetical foundation of Data Science. One should have a clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching at incorrect conclusions. That’s the reason why statistics and probability play a very important role in Data Science.
- Machine Learning
As a Data Scientist, every day, you will be using Machine Learning algorithms such as regression and classification methods. It is very important for a Data Scientist to have a knowledge of Machine Learning as a part of their job so that they can predict valuable insights from available data.
- Big Data
In the current world, raw data is compared with crude oil, and the way we extract refined oil from the crude oil, by applying Data Science, we can extract different kinds of information from raw data. Popular tools used by Data Scientists to process big data are Java, Hadoop, R, Pig, Apache Spark etc.
How do top industry players use Data Science?
Top industries like Google, Amazon, and Visa are using Data Science. Here, the deciding factor for an organization is that what value they extract from their data repository using analytics and how well they present it. Below, we list some of the biggest and best companies that are hiring Data Scientists at top-notch salaries.
Google is by far the biggest company that is on a hiring spree for trained Data Scientists. Since Google is mostly driven by Data Science, Artificial Intelligence and Machine Learning these days, it offers one of the best Data Science salaries to its employees.
Amazon is a global e-commerce and cloud computing firm that is hiring Data Scientists on a big scale. They need Data Scientists to find out customer mindset and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.
An online financial gateway for most companies, Visa does transactions worth hundreds and millions in a single day. Due to this, the need for Data Scientists is huge at Visa to generate more revenue, check fraudulent transactions, and customize products and services as per customer requirements, etc.
So give a kick start your career in Data Science with AUV Technology.