The meaning data science is it is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and Practices from the fields of mathematics, statistics, artificial intelligence and computer engineering to analyze large amounts of data.
EXAMPLES OF DATA SCIENCE.
The examples of data science in business includes processes such as aggregating a customer’s email address, credit card Information, social media handles, and purchase Identifications in order to identify trends in their behaviour.
DATA SCIENCE USES:
It is used for a wide range of applications including predictive analytics, machine learning, data visualization recommendation systems, fraud detection Sentiment analyser and decision-making in various industries like health care, finance, marketing and technology .
DATA SCIENCE TOOLS TO CONSIDER USING IN 2024:
The sources of tools to be considered using in 2024 are PANDAS, APACHE SPARK, TABLEAU; MATPLOTLIB, JUPYTER NOTEBOOK, SAS, MATLAB, SCIKIT – LEARN, TENSORFLOW, NUMPY, APACHE HADOOP, EXCEL, PYTORCH, R, MONGODB, RAPID MINER, KNIME, MICROSOFT POWER BI, SQL, DATA ROBOT, ALTERYYX, BIGML and etc..
Tableau it is very popular with data scientists because ite drag-and-drop interface makes it easy to sort and compare .
Best software we need the know are Excel, Python, R, Tableau, MySQL, SAs, Jupyter Notebook.
There are four main types of data sciences. They are descriptive, Inferential, predictive and prescriptive
General types of data we know are quantitative and qualitative, both are equally important .They used for demonstrate effectiveness and importance or value.
Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, prescriptive Analytics and cognitive analytics these are the main data analytics.
Data is the information of any kind, in the context of business and computing, well the types of data are structured data, unstructured data and data and big data.
IMPORTANCE OF DATA SCIENCE:
Data science is important because it combines tools methods and technology to generate meaning from data. Modern organizations are inundated with data, there Proliferation of devices that can automatically collect and store Information.
The following is the life cycle of data science
FUTURE OF DATA SCIENCE
1. Increase in Data science roles
2 Clearly defined scope of data sciences
3 Creation of more jobs
4 Normalized data science Education
5 Advancements in ML to foster Data science
By all means, advancement in Machine learning is the key contributor towards the future of data science. In particular, data science also covers
* Data integration
* Distributed Architecture
* Automating Machine learning
* Data visualization
* Dashboards and BI
* Data Engineering
* Deployment in production mode.
* Automated, data-driven decisions
Data science is expanding due to the immense Contribution made by machine learning. It has improved the data science Scenario in the following ways-
* Advanced Personalization
* Giving advanced search Engine results to the user.
* Code free environments
* Quantum computing.
In the 21st century the most prominent job is searched are data scientists and various other data science related requirements positions the following are the skills required for data science jobs
→The Analytical expert who are responsible for finding insights and patterns in the data is the DATA SCIENTIST.
→The data architect is responsible for implementing the blue prints of a company’s data platform and delineates various models, policies, rules that govern the storage of data as well.
→ The data Engineer is responsible for building big data pipelines y models for the data scientists to work on.
→A data Science manager is responsible for handling & managing data science projects, to perform and to meet the project deadlines.
→A statistician is the oldest job title among all the roles discussed above he/she is the person who statistical modelling for understanding various trends in the market. A machine engineer is responsible for development of deep algorithms in the Process.
→ A decision scientists is used to design thinking and behavioural Sciences to better understand the clients.
→AI ethics specialists are responsible to ensure that AI systems are created & implemented responsibly & ethically in data science.
→A big data engineer is a specialist in charge of creating, constructing and Managing infrastructure & large-scale data processing systems for handling enormous amounts of data.
→The machine learning engineer is the person who develop machine learning pipelines that can handle data, train models, and generate predictions by combining data science, software engineer and domain knowledge.
We went through some of the important roles of data scientists and other important fields in data science, it is a wide field, and there are numerous Opportunities available in this field.
SUMMARY:
The source, tool of study of data to extract meaningful insights for business. It provides text Processing capabilities, analytics capabilities, a corpora & sample data of various types, and it also supports a number of machines learning features, like Classification and clustering algorithms. Data science is a dynamic and rapidly evolving field that plays a pivotal role in our data -driven world.
