The future of data science in 2021 is listed below:
- Extended data-driven strategies
- Data Privacy Regulations
- Clearly Defined Roles
- Artificial Intelligence for Data
- Minimized Codes using ML
- Application Programming Interface (APIs)
Data science is all about developing methods to record, store and analyze the data effectively. The main aim of data science is to extract the data and obtain insights and knowledge from both structured and unstructured data. Data science is a concept that covers the entire scope of data collection and processing.
Data science involves various tools, statistics, algorithms, and machine learning principles in order to obtain and understand the data from complex and large data sets through the context of mathematics, statistics, computer science, and information science.
In the current scenario, every day 2.5 quintillion bytes of data are generated around the world. Data generation has rapidly increased in recent times due to the Internet of Things (IoT).
According to the Paris 21 report 2019,
“90 percent of the world’s data has been generated in only the last two years.”
The top companies like Google, Amazon, and Visa are using Data Science in order to optimize themselves and to address their rapidly expanding data.
There is a huge demand for certified Data Scientists. According to Forbes,
“By 2021, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.”
(Source: Forbes)
- According to CIO.com, Data science helped Zoomcar (Self Driven Service) capture 75% of the Indian market.
- According to GCN.com, Data science could help Californians battle future wildfires.
The future of data science in 2021 is listed below:
- Extended data-driven strategies
- Data Privacy Regulations
- Clearly defined roles
- Artificial Intelligence for data
- Minimized Codes using ML
- Application Programming Interface (APIs)
1. Extended data-driven strategies
In many organizations, the decisions are taken based on authoritarian advice or general consensus due to a lack of data processing power. Data scientists are building the system for an organization that can anticipate, predict, and even speaks.
Organizations’ inability to handle the data to analyze can harm productivity and might slow down the project progress. Data science is a quantitative approach. The adoption of data science can increase productivity.
2. Data Privacy Regulations
Data is important for any organization. The management has become more cautious while sharing any data in business. In order to control data thefts and its impact, the GDPR – General Data Protection Regulation, was passed by states of the European Union in May 2018. It has also been reported that such regulation for data protection shall again be passed by California in 2021. With the revised data privacy regulations the future of data science is very bright.
3. Clearly defined roles
Data science is a very broad stream. The roles in an organization aligned with data create a lot of confusion. Data science typically separate the data roles into 4 distinct but overlapping positions:
- Data Architect — The data architect develops architecture effectively to organize, integrate, centralize and maintain data.
- Data Engineer — Develops, tests and maintains data architectures to keep data ready for analysis.
- Data Analyst — The data analysts process and interpret the data to understand and analyze the insights from structured and unstructured data.
- Data Scientist — Once the analysis is done, the technical aspects required are taken care of by data scientists.
4. Artificial Intelligence for data
The more the data, the more difficult to manage.
According to Raconteur:
- 500 million tweets are sent
- 294 billion emails are sent
- 4 petabytes of data are created on Facebook
- 4 terabytes of data are created from each connected car
- 65 billion messages are sent on WhatsApp
- 5 billion searches are made
By 2025, it is predicted that 463 exabytes of data will be created each day globally — that’s the equivalent of 212,765,957 DVDs per day.
Managing such huge data is very difficult. Automated tools can help data scientists with Routine tasks listed below:
- Exploratory data analysis
- Data cleaning
- Statistical modeling
- Building machine learning model
5. Minimized Codes using ML
In the current state, a lot of codes are written. This doesn’t mean tools like R, Python, and Spark will not be used. Machine learning plays an important role in reducing the effort in writing complex programs. When the data is fed to machine learning systems, they will collect, clean, manipulate, label, analyze and visualize the data. This generates neural networks.
In data science, the software engineer’s role will be “data curator”.
6. Application Programming Interface (APIs)
Using the Application Programming Interface (APIs) is very useful in data science. Data scientists will be able to rapidly construct their model, build and test multiple algorithms in one go, and can visually validate results with the entire team.
In the coming future, the softwares will be crafted by visually tapping and leveraging whatever service required through API.
For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.
The Data Scientist certification validates data scientist’s knowledge on SAS, R, Hadoop, Python and Spark and how to use data concepts such as data exploration, visualization hypothesis testing, and predictive analytics. There is a huge demand for Data Scientists in industries like Aerospace industry, IT industry, e-commerce industry, and healthcare industry.
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