What is Data Science?
Data science is a combination of multiple streams, including statistics, machine learning, and artificial intelligence (AI), and data analysis, to extract insights from data. Data science includes the collection of data from various resources like websites, databases, live streaming or IoT resources etc. To processes, this data using various techniques is known as data processing. To identify insights of data using visualization techniques and select the best fit model using machine learning modules. Using these processes we get the results that we can use for further product development.
Data science involves preparing the data for analysis, including cleaning, aggregating, and processing data to perform advanced data analysis. In analytical applications, such as data scientists analyze the results, identify patterns, and enable business leaders to draw informed insights.
Data science, identify trends and provide information that a company can use to make better decisions and create more innovative products and services. Perhaps the most important thing of all is that it is possible to make the machine learning model (ML), to learn from the vast amounts of data that will be generated from them, rather than relying primarily on the business analysts to see what can be learned from the data.
Future of Data Science
Data Science used in many fields and various sectors. Data science scope is very wide in different sectors as follows:
1. Information Technology
Data science is heavily used in information technology for collecting, processing, analysing data and finding insights. It reduces the development time and resources workload. It also improves the quality of products and services.
2. Healthcare
Data science is used in healthcare for detecting diseases in the early-stage using huge patient dataset. It plays a very important role in the covid-19 pandemic for tracking and supply management.
3. Automobile Industry
In this industry data science used to developed Self Driving cars, Autopilot flying cars, Fixed Destination Cabs, Automatic Public Transport, and various other applications.
4. Banking and Finance
Data science used for detecting frauds, recommend eligible a borrower and much more in banking and finance sector.
Career in Data Science
Various types of data scientist career paths are available. Following are some data science jobs employ the fresher’s and experienced candidate. InsideAIML is one of the best institutes for a data science career.
1. Data scientist
Data collection, pre-processing, analysing and building models are the responsibilities of a data scientist. They can be combined with a wide range of skills to analyze the data collected from the Internet on smartphones, customers, and the integration of sensors and other sources, to get useful information
2. Machine Learning Engineer
Machine learning engineer responsible for creating models and data analysis algorithms. They plays important role in statistical analysis and tune their operations as per results.
3. Data Engineer
Data engineers are responsible for creating a good data ecosystem for their company where the data pipelines are maintained. They were responsible for choosing the best data analysis tool for real-time processing. They provide data to data scientists.
4. Business Analyst
A business analyst is a special person in that field. They have knowledge related to banking, healthcare, management, eCommerce, power, telecom, etc. They help to identify why products fail, what you need for product development etc.
5. Data Analyst
The data analyst is responsible for keeping track of statistical analysis, and the analysis of the A/B-testing, and manipulation, and transformation of large data sets, it is in line with the expectations of the business analysis. With the help of statistical methods to analyze data, they generate insightful business reports and advise you on new ways to reduce costs, improve the efficiency of their business processes. They will also work closely with the management to create a list of priority needs of the business, and the details of each project. With the help of the data available to them, they will make models to show that the trends in the consumer and the consumer, and the general public.
6. Data Architect
The Data Architect is responsible for the development of data-based solutions for cross-platform performance analysis and the design of an analytic app. Through a careful analysis of the databases that can be used to ensure compliance with the company policies and external regulations, while maintaining the security and integrity of the company database. They also provide an insight into the ever-changing demands concerning the database, storage, and use of, as well as to suggest possible solutions, and suggestions on how to optimize them.
7. Applications Architect
Businesses need to have good application and user interfaces associated with the normal operation of the business. Applications architect choose the right application for their business. Due to the increasing complexity of data, companies need to be more advanced applications.
8. Statistics Analyst
A statistical analyst or a statistician, that is required for the interpretation of the obtained data and present it in a comprehensible form, is not an engineer. They are the core elements of the big data into insights from stakeholders or colleagues. For Data analysis, the results will also be used to make forecasts and to identify potential opportunities.
9. Enterprise Architect
The Enterprise architects are specialists and provide the best architecture models to companies, to provide stakeholders and senior management to aid in the selection of the right IT solutions for the analysis of the data. The Top companies such as Microsoft, Cisco, etc., etc. the hiring of architects and business clients to maintain IT structure
10. Infrastructure Architect
The infrastructure architect of the company makes sure that the application, and the databases that will be used by the company to be efficient. They help to optimize cost. They make sure that their businesses have the tools they need to analyze big data.
11. Business Intelligence (BI) Developer
A business intelligence (BI) developer is responsible for creating and developing the strategies that will help you to make better business decisions. They make use of the existing BI and analytics tools or developing their tools, to make it easier to understand the workings of the system. They are responsible for the regular development and improvement of IT solutions to the programming, designing, testing, debugging, and implementation of such tools.
12. Machine Learning Research Scientist
Machine learning scientist is an expert in machine learning, computer science and even artificial intelligence. It develops technology for vehicles and robots to help people enjoy new levels of independence, access, and mobility.
Conclusion
Data science is a growing field, and there are plenty of chances to get a career in data science. There are lots of data science career options for freshers available in the market. You can learn Data Science Courses at InsideAIML.