Data science is a field of study where data is analyzed using some specific parameters and a decision is taken based on the pattern and results that are generated by the analysis. It is an interdisciplinary science that is about using scientific methods, algorithms, and processes to study the available data and gain knowledge from it.
Data science is a mixture of concepts that unify data, machine learning and other useful technologies to derive some meaningful results from the sample data.
It is used as a synonym for many related fields like business analytics, business intelligence, predictive modeling, and plain statistics. It has many concepts taken from earlier solutions and rebranded as a part of Data Science. It is a very sensitive field as there is a big catch- without proper utilization of resources and poor management, data science is bound to give results that are spectacular failures.
Role of a data scientist
A data scientist makes use of the data available to get tangible results that can be used to improve the functionings and performance of an organization. They collect information from a variety of sources. Then they analyze this data with various methods and generate patterns and trends.
These results are then used to give the organization a set of recommendations and a plan of action for the future. These once implemented are again monitored, measured, and analyzed to track its efficiency. This ongoing process is continuously carried out by data scientists to optimize the existing system.
When necessary, they also build ML and AI-based tools for utilizing the data. They also store all these data and clean it up before doing any analysis. In addition, data scientists are also expected to train various teams to internally handle some analytics process.
A data scientist is an in-demand job and is a good career path. It also pays really good salary. If you want to become a data scientist without a higher education degree, then there are a few options that can choose. Bachelor degree is now introduced for data science. You can choose to go for it. But if you want to do it faster, then you can do an offline course on data science. If that is not possible, consider doing an online course on data science to get the required certification.
How businesses use data science to their benefit?
There are a whole lot of ways data science usage benefits businesses. A few of them are discussed in this section.
Making better decisions at every level of management
Data scientists are strategic partners to middle and upper management. The data scientists measure, track, and record performance parameters. With these metrics, the data is used to improve the decision-making process at multiple levels of the organization to effectively raise the overall performance of the organization.
Decisions on recruiting
This is a great application of data science. All that time saved in reading resumes can be contributed more effectively to the organization. using data science, it becomes easy to weed out unwanted or unsuitable applications for a job listing. With all the information available on the web, the data scientist can work towards finding the best and (nearly) perfect candidate for any position.
Defining goals and objectives
After analyzing the data and coming up with trends and patterns, the data scientists give recommendations to the team. They also suggest a plan of action based on these trends for better customer interaction, performance, and increase in sales by targeting properly.
Data scientists also go through the existing system and help the teams optimize the existing patterns for better results. They are expected to constantly improve upon the existing analytics and exploring new avenues and methods for data analysis.
With the data in hand and the results of the analysis, the data scientists are in a unique position to suggest best practices that should be followed by each team to improve on their performance and increase their contribution to the company. In this context, the data scientist takes an effort to introduce the key players to using analytics tools effectively on their end. This can be used for gaining insights on, let’s say, a marketing campaign.
Identifying changing targets
Most companies have a minimum of one channel through which they collect data. Usually, many channels from the analytics tool to survey are used to collect data from the consumers. The demographics and other aspects that are not game-changing on their own are linked and their relationship often provides the key to identifying new targets. The data scientist must be capable of establishing a relationship between seemingly obscure
Testing the decisions taken
It is the most imperative result oriented task of the data scientist. Impact of every decision must be recorded and analyzed. Not every decision taken will be the best decision despite the maximum effort. Such errors must be rectified and effort will be taken to perfect the solution to every issue faced. In a way, it quantifies success and failures.
What tools are available for data science?
A lot of tools are available for a data scientist. The users are expected to find the best fitting stack of data science tools that satisfy the requirements of the company. Check out our article on Data Science tools.
Tools to collect data
Collecting data is the first step to analyzing the data. The data has to be of good quality to get the best results. More importantly, the integrity of the data, accuracy are important. Additionally, the data should have the least possible errors. The tool that helps us to achieve these goals are used to collect data.
Ex: IBM DataCap, Octoparse, OnBase
Tools to analyze data
The next step to data collection is to process and analyze the data. The results derived from the analysis is then used to make decisions for the betterment of the company. The data analysis is used to determine the performance levels of the company.
Ex: Domino data lab, Alteryx, Rapidminer
Tools for data warehousing
Data warehousing is collecting corporate data and data from operating systems and other external sources. These data are consolidated in a warehouse and made easy to use.
Ex: Amazon Redshift , Google Bigquery, Microsoft Azure, SQL
Tools for machine learning
Machine learning is an important aspect of data science. Machine learning is used in prediction analysis. It is used to evaluate and optimize the data to accurately interpret it to obtain the desired results.
Tools for data visualization
There is a need for data analysis results to be presented in a visual format to be easily understood by all people. Data visualization helps with identifying trends and patterns that will help us make educated decisions for any business. Data visualization is achieved through graphs, charts, maps, and others.
Google Fusion Table, Microsoft Power BI, Qlik, SAS, D3.js
What is the salary earned by a data scientist abroad?
In the US, a data scientist earns an average salary of $128,700/- per annum. The highest recorded salary of a data scientist, in general, is $249,000/- per annum.
In Australia, a data scientist earns a salary of 120,000/- er annum. The highest recorded salary is 215,000/- per year.
In the UK, a data scientist earns as much as a salary of 54,000/- per year. The highest recorded salary is 126,000/- per annum.
The salary is subject to education, experience, certification, employer, and the location of the employer.
* Source: Indeed. All the salaries average and highest are taken from the set of salaries that were reported to and collected by Indeed .
What is the salary earned by a data scientist in India?
An entry-level data scientist makes on an average Rs. 7Lakhs per annum. According to payscale, the highest recorded salary is in 1.7M per year. With the right education, experience, job location, and employer, you can earn more salary per annum.
What are the general responsibilities of a data scientist expected by companies?
- Collect and store data.
- Process, Clean up and check the integrity of the data.
- Make data secure and in an easily readable format.
- Use machine learning technique for optimization and selecting features.
- Expansion of data collection by including third party data from surveys and other methods.
- Analyze the data and present visual results that are easily understandable.
- Generate patterns and trends based on the analyzed information.
- Chart out a course of action and give recommendations for improvement.
- Build tools if needed. Detect any anomalies with a detection system and monitor its performance.
- Keep optimizing and improving on the existing system and results.
What are the popular applications of data science in the real world?
There are quite a wide set of applications for data science. Some of the industries that use data science are explored here.
One of the top users of data science technology is the gaming industry. The top players in the field like EA sports, Nintendo and Sony all use data science to get maximum profit from their games. Gaming now uses machine learning algorithms that help upgrade the play level along with the players’ progress.
This is a vast topic and includes everything that we search on the internet. Searching the web is facilitated by various search engines like Google, Yahoo, Bing to name a few. These search engines optimize their results using data science algorithms to deliver the results of the search in the fraction of a second.
This is a sector where the applications are endless. Data science is used as a part of research. It is used in practical applications like using genetic and genome data to personalize treatments, used in image analysis to catch tumors and growth. Predictive analysis is a boon to drug testing and development process. It is even used for customer support in hospitals and give virtual assistants as chatbots and apps.
Speech and Image Recognition
Tagging features that are available in Google Photos, Facebook, and other websites are also applications of data science. The auto-tagging feature that gives you a recommendation of the person whose photo you have uploaded is done using a face recognition software.
In addition, there also exits voice recognition related use of data science. All the virtual assistants like Siri, Alexa, Cortana, and Google Assistant are all applications using data science. The speech recognition helps convert voice into text. There is an inherent problem here which is slowly being solved. It is that there is any number of accents for a language and you need that much sample data to teach the speech recognition software to transcribe correctly.
Marketing and Advertising
Marketing a product to a selected pool of targets by digital advertising is one of the more dynamic applications of data science. Digital marketing survives on data science. The digital footprints are recorded and based on your search and preferences, you are shown advertisements that were chosen for you. It uses the user’s internet behavior to analyze and target them.
Logistics is yet another field that has used data science to improve its efficiency. Best routes, traffic, selecting the mode of transport, delivery time are some of the factors that are managed with data science.DHL, FedEx, and UPS have used data science with good effect to shake up things and improve on their performances.
Comparison websites for products
There is a huge market for comparison website due to the cut-throat competition for selling any and every kind of product. Pricegrabber, Junglee, Trivago are some of these popular comparison websites. Every website generates a lot of data and analyzing this could generate a lot of ideas to further the market.
The finance industry is one of the first one to get aboard the data science ship. They use all the data they generated while sanctioning loans to come up with a system in an effort to reduce losses due to bad debts.
Now the financial institutions use data science to analyze the data they collect through customer forms, credit ratings, past history, and other variables to get the probability of defaulting on the loan payments. Based on the results, the financial institution may give out loans to certain persons while denying others.
What does the future hold for data science?
Virtual Reality and Augmented Reality are techs that seem promising for using data science at an exponential level in the future. VR and AR are fields that require extensive computer science knowledge, algorithms and a lot of data to create and provide the user experience that is expected. Pokemon Go is the first step into this technology being used for commercial gaming purpose.
Driverless automobiles like the ones Uber recently tested are a great premise for data science application. From navigations, routes, traffic to crash reports, there are various types of data which can help optimize the cars to give the best performance.