DataScience is one of the booming technologies used by various organizations to have a competitive edge over others. It basically involves implementing vast amounts of raw data to unearth relations and extract meaningful information or insights.
What is DataScience?
DataScience combines many fields like Statistics, Data Analytics, Machine Learning Algorithms, etc. For DataScience, We use large datasets to get meaningful insights that would help an organization make better decisions.
The following are the steps involved in DataScience:-
- Creating or designing a problem.
- Obtaining or Collecting raw data that is required for the problem.
- Processing the data for analysis.
- Modeling the data.
- Communicating and Visualizing the outputs.
Necessity for DataScience
DataScience is very helpful for organizations to make better decisions to progress their growth. Data is increasing exponentially from various sources like social media, Youtube, Smart devices, and many more. Data is a very important factor for an organization to run its business properly. In fact, The way an organization deal with its data plays a major role in the business competition. If an organization did not utilize the data properly it may cause the organization to step out of the market competition and pay serious damage, as its rival organization may have utilized it properly.
So, To evade this situation we can make use DataScience. With the help of DataScience, An organization can handle massive amounts of data to get meaningful insights with these insights an organization can make optimal decisions. Nowadays, DataScience is being opted by many organizations like Amazon, Microsoft, Google, and many various organizations. Using DataScience, We can also predict things like elections, customer preferences, and many more.
Applications of DataScience
There are a lot of Applications of DataScience, Few of them are:-
- HealthCare (for detecting tumors or diseases.)
- Image Recognition (Photos we upload to our devices can be categorized using image recognition.)
- Transport (Smart Vehicles or Self Driving cars.)
- Speech Recognition (It is used in the voice assistants provided by Google's Voice Assistant, Apple's Siri, and many more.)
- Recommendation System (It helps reduce time and provides a text recommendation that would be probably used.)
Tools Used for DataScience
There are a lot of tools that can be used for implementing DataScience, Few of them are:-
- Big ML (Web-based tool that can be used for processing Machine Learning Algorithms.)
- MS Excel (It is an application provided by Microsoft, that can be used for analysis, and visualization.)
- Tableau (It is a software used for visualization.)
- Jupyter (It is a web-based software that is used for writing codes, presentations, etc. It supports R and Python languages.)
- NLTK (This tool is very important, as computers can understand human language, Can be used for text analysis, summarization, etc.)
- TensorFlow (It is widely used for machine learning problems, It is free and open-source.)
Job Opportunities in DataScience Field
DataScience is a booming field and many organizations need many data scientists for their business. It is considered one of the hottest jobs in the 21st century. The average salary of DataScience is around $90,000-$195,000. There are various types of jobs in the DataScience domain, and below are a few of them.
- Data Analyst (Responsible for analyzing and visualizing the data for decision making )
- Data Scientist (Responsible for providing meaningful insights through various algorithms, tools, etc)
- Data Engineer (Responsible for maintaining and building data architecture)
- Machine Learning Expert (Responsible for using machine learning algorithms like regression, classification, clustering, etc)
- Business Analyst (Responsible for making forecasting, budgeting, and analyzing data models)
DataScience can be learned by anyone of any background i.e people from a non-technical background can also learn DataScience. There are a lot of courses provided on websites like Udemy, Coursera, Great Learning, Upgrad, etc. If a person from a technical background wants to pursue a career in the DataScience field they would have to learn topics such as Machine Learning, Database Management, Statistics, and a few more. DataScience is a great and one of the booming technologies to pursue one's career.