Data science combines math and data science statistics and sophisticated analytical artificial intelligence AI and machine learning that are complemented by particular expertise in subject to discover actionable information hidden within companys data. This information can be used to aid in decision making as well as strategic planning.
The increasing amount of data sources as well as resulting data is reason why data science an one of fastest growing areas in all industries. This is why it is no wonder that position of data scientist was called”the “sexiest job of 21st century” by Harvard Business Review link is outside ibm.com. Businesses are more dependent on them to understand data and favor concrete recommendations that rise business results.
The data science process involves variety of tasks tools and procedures which allow analysts to get actionable information. typical data science project is characterized by following phases:
- Data Ingestion process begins by data gathering of raw structured as well as structured data from all pertinent sources with various techniques. This can be done through manually entering data or web scraping. They can also include live streaming data taken from devices and systems. Data sources may contain data that is structured data like customers data and non structured data like log files videos audio images as well as data from Internet of Things IoT social media and many more.
- Data storage as well as data processing Because data may come in variety of types and formats businesses have to think about different storage solutions according to type of data required to be gathered. Data Management teams benefit in setting standards for data storage and structure that benefit streamline workflows related to analysis as well as machines learning as well as advanced learning model. process involves cleansing data by deduplicating changing and combining data with ETL extract transform and load jobs as well as different data Integration technologies. process of data preparation is crucial for increasing data quality prior to loading it into data warehouse data lake or any other storage.
- Data Analysis This is where data scientists perform an exploratory data analysis that examines patterns biases and distributions and ranges of value within data. data analysis is used to drive development of hypothesis for a/b testing. This also lets analysts evaluate value of datas application in modeling to aid in predictive analytics as well as machine learning and/or deep learning. Based on models precision business can rely upon these data driven insights to aid in decisions in business which allows models to improve scalability.
- Communicate In end these data insights are reported as reports or various data visualizations to make findings and their implications for business more easy to business analysts and other decision makers to grasp. An data science programming language such as R or Python has components that can be used to generate visualizations. Alternatively data scientists can use special visualization tools.
Data science versus data scientist
Data science is considered field & data scientists work in discipline. Data scientists do not have to be solely responsible for procedures that are involved in data science cycle. As an example data pipelines are typically taken care of by data engineers. However data scientist can make suggestions on what type of data is necessary or beneficial. Although data scientists are able to create machines learning designs scaling those initiatives to greater scale needs more engineering abilities in order to make program to be more efficient. This is why its normal for data scientist to work together with machine learning engineers to rise size of machines learning models.
Data scientist duties can coincide with those of data analysts duties specifically in area of exploration data analysis as well as data visualization. Yet data scientists expertise is usually more extensive than that of typical data analysts. Generally speaking data scientist leverage common programming languages like R as well as Python for greater statistical inference as well as data visualization.
For these roles data scientists require computer science as well as pure science talent above those required by common analysts in business as well as data analyst. Data scientists data scientist should also be able to comprehend particulars of business like automobile manufacturing and eCommerce or healthcare.
Simply put data scientist has to be capable of:
- Learn sufficient about your business that you can be able to ask right questions and spot areas of business concern.
- Utilize concepts of statistics and computer science as well as business sense to data analysis.
- Utilize variety of methods and tools to creating and extracting data that is stored in databases & SQL up to data mining to data techniques for integration.
- Discover insights from massive data together prescriptive analytics as well as artificial intelligence AI which includes machine learning model natural language processing and use of deep learning.
- Writing programs automate data processing and calculation.
- Use stories to clearly communicate significance of payoff to stakeholders and decision makers in all levels of technical knowledge.
- Discuss how payoff are used in solving business challenges.
- Work with others data science team members including data as well as business analysts developers of IT data engineers & app developers.
They are knowledge are extremely sought after which is why there are many people who are pursuing data science career are looking to explore several data science courses including certification programs data science courses as well as degree programs provided by universities.
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Data science and business intelligence
It is possible to get confused between words “data science” and “business intelligence” BI due to fact that they are both related to an organisations data and analysis of that data however they differ on way they are applied.
business intelligence BI can be usually generic term that refers to software that facilitates data processing data mining data management as well as data visualization. business intelligence instruments and procedures let users discover relevant information from data that is raw data and facilitate data driven decision making for organizations in variety of fields. Even though data science tools and processes are comparable in many ways but business intelligence is more focused on data of past as well as data gathered by BI tools tend to be more descriptive. tool uses data to determine what occurred prior to determining course of action. BI approach is focused on stationary unchanging data that is generally organized. However while data science employs descriptive data generally it makes use of it to identify predictive variables that is then utilized to classify data and to create forecasts.
Data science and BI are not incompatible. Digitally savvy companies employ both of them to comprehend and get value from their data.
Data science tools
Data researchers rely on most most popular programming languages for exploratory data analysis as well as statistical regression. Open source software supports built in statistical modeling and machine learning as well as graphics abilities. They include following read more on ” Python vs. R: Whats Difference?”:
- R Studio Open source programing language and environment designed that allows development of statistical computing as well as graphics.
- Python: It is flexible and dynamic programming language. Python is flexible and dynamic programming language. Python contains variety of libraries like NumPy Pandas Matplotlib which allow you to analyse data efficiently.
For sharing of code as well as other data data scientists may use GitHub notebooks and Jupiter.
Certain data researchers may favor an interface for users as well as two commonly used tools in enterprise to analyze data comprise:
- SAS SAS tool is complete that includes visualizations as well as interactive dashboards for studying data reporting data mining as well as predictive modelling.
- IBM SPSS Provides latest analytical techniques vast collection of machine learning algorithms and text analysis. It also provides extensibility of open source integration with massive data and seamless implementation into software applications.
Data scientists can also acquire expertise together large data processing systems including Apache Spark open free framework Apache Hadoop & NoSQL databases. Theyre also proficient with an array of data visualization tools. These include basic graphic tools available in Excel and business presentations like Microsoft Excel custom built commercial visualization tools such as Tableau or IBM Cognos & open sources tools such as D3.js a JavaScript library for developing Interactive data visualizations as well as RAW graphs. To build machines learning algorithms data scientists frequently turn to various frameworks such as PyTorch TensorFlow MXNet as well as Spark MLib.
Due to significant learning curve for data science Many companies seek to increase return on investment of AI projects. However they frequently find it difficult to recruit expertise required to realise data science projects maximum potential. To bridge this gap companies are turning to multipersona data science as well as machines learning DSML systems and establishing position that of “citizen data scientist.”
Multipersona DSML platforms make use of auto service portals self service as well as user interfaces with no code or low code so users with minimal or no knowledge of digital technology or data science can generate commercial benefits with data science as well as machine learning. They also assist experts data scientists offering an enhanced technical interface. Utilizing an integrated DSML platform allows collaboration throughout entire enterprise.
Data science as well as cloud computing
Cloud computing increases scope of data science through offering ability to access extra processing capabilities storage capacity as well as additional tools needed to complete data science projects.
Because data science often relies on large data sets using tools that are scalable with volume of data are crucial especially for projects that require time. Cloud storage services include data lakes bring accessibility to storage infrastructures that are capable of processing and ingesting huge amounts of data easily. These systems bring an array of options for end users by letting them set up massive clusters whenever required. It is also possible to incorporate incremental compute nodes to speed up data processing enabling companies to make short term sacrifices in order to get more long lasting result. Cloud based platforms generally have various pricing structures like subscription or per use in order to satisfy requirements of end user whether theyre large enterprises or small scale business.
Open Source technologies are extensively utilized for data science tools. Since theyre hosted in cloud organizations do not have to install set up maintain and update tools locally. variety of cloud providers such as IBM Cloudr provide tools that are pre packaged and allow data researchers to create models that dont require coding thus opening up access to technological innovations as well as data insight.
Data science uses
Enterprises have potential to reap many advantages through data science. most common uses are optimization of processes through automated intelligent systems improved targeting and personalization in order in order to boost experience of customers CX. But some more specific examples are:
A few examples of use cases that illustrate data science as well as artificial intelligence:
- A multinational bank offers faster loans via mobile app with computer generated credit risk models as well as cloud based hybrid structure thats both strong and safe.
- An electronics company is creating high performance 3D printed sensor technology to help assist future driverless cars. technology relies heavily on data science and analytical tools to improve its ability to detect objects in real time.
- The robotic process automation RPA service provider has developed an cognitive process mining system which reduces processing times by between 15 and 95% in its clients. system is equipped to recognize material and tone of customer email messages & direct service personnel to prioritize those most important and urgent.
- An digital media technology company has developed an audience analysis platform that lets its customers see material that is attracting TV viewers with wide number of digital channels. This solution makes use of deep analytics as well as machine learning to collect real time information about habits of viewers.
- A city police department has created statistical tools for analysis of incidents link is located beyond com to benefit officers know accurate time and place to allocate resources to stop criminality. This data driven system creates dashboards and reports to increase officers awareness of situation for.
- Shanghai Changjiang Science and Technology Development employed IBMr Watsonr technology in order to develop an AI powered medical assessment system that analyzes documents from medical professionals to categorize people based upon their likelihood of having stroke. It also has system which can determine effectiveness of various strategies for treatment.
