How to keep up with the latest trends and technologies in data science?

Data Science is among such cutting-edge technologies that have gained traction over the years. In addition to being called the century’s most popular domain, it also has a huge number of applications for a broad spectrum of businesses, making it the domain of choice for experts who want job security. The demand for jobs involving data science is rising as we become reliant on technology, data, and new emerging industries.

To know more, read Why choose an MBA in Analytics & Data Science?

Data science resourcing has taken center stage with a favorable, skills-driven segment and the most industry-agnostic talent. Data scientists are projected to be among the most sought-after talent pools since their skill sets are becoming increasingly valued and essential for enterprises across industries. According to the U.S. Bureau of Labor Statistics, the employment of data scientists is expected to grow 36% from 2021 to 2031, which is much faster compared to other occupations.

What are the latest trends in data science?

Data science is an arena that’s perpetually evolving, with data science technologies continually devised. The following trends in data science are among the most recent:

• AutoML (Automated machine learning)

AutoML is used to automate machine learning model selection and optimization. Data scientists and other users with less specialized knowledge of machine learning can construct and deploy models more quickly with the aid of autoML tools by choosing methods and tuning parameters automatically. AutoML will probably be more widely available to users in the future as low-code, and no-code dev kits proliferate.

• Federated learning

Federated learning is a distributed method of machine learning that enables numerous hardware or software systems to contribute to the model’s training. Data breaches and other security concerns are less likely to occur, thanks to federated learning, which permits data to stay on local servers or devices rather than centralizing it in one place. Federated learning is especially helpful for applications like healthcare, wherein confidential patient files need protection. 

• Explainable AI (XAI)

Explainable AI is a recent development in data science that focuses on creating transparent and understandable models and algorithms. This is crucial in fields like banking and healthcare, where using AI models to make decisions can have far-reaching effects. Data scientists can more easily spot biases and inaccuracies by using XAI approaches, which assist them in comprehending how a model reached a specific conclusion or prediction.

• Natural language processing (NLP)

NLP, a subfield of ML, centers on the capacity of computers to comprehend and produce human language. NLP is utilized in many different contexts, such as chatbots, machine translation, and sentiment analysis. Pre-trained language models, like GPT-3, are one of the current NLP trends. These models could be adjusted for certain applications, which enables programmers to create more complex language-based systems without having to build from zero.

• Graph databases

A sort of database called a “graph database” stores data as nodes and edges as opposed to conventional tabular formats, making them well-suited for gathering and processing data with complicated relationships, including supply chains and social networks. Many uses for graph databases exist, such as fraud detection, recommendation systems, and network research. The application of graph databases is projected to increase as more data are produced by linked devices and systems.

• Time-series analysis 

Time-series analysis is the process of examining data that has been gathered over time, like share prices or weather forecasts. It is crucial in a spectrum of uses, including finance, healthcare, and energy, as more data is produced by sensors and other IoT devices. Utilizing deep learning models, including RNNs (Recurrent Neural Networks, to identify intricate patterns in time-series data is among the most recent developments in the field of time-series analysis. 

Best jobs you can get after a data science course

You have a variety of job opportunities after pursuing a data science education, depending on your abilities, preferences, and experience. Here are six prospective careers in data science and their associated salaries:

• Data Scientist: 

These professionals are expected to gather, analyze, and interpret complicated data as data scientists to find correlations, patterns, and insights. The annual salary of a data scientist can go up to ₹25.7 Lakhs. 

• Machine Learning Engineer: 

A machine learning engineer builds and maintains machine learning models using statistical and computational methods. They frequently have extensive training in coding, analysis techniques, and machine learning technologies. The average annual salary of a machine learning engineer can go up to ₹21.0 Lakhs.

• Data Analyst: 

Large dataset statistical studies are carried out by data analysts, who also gather, process, and handle the data. They work collaboratively with data scientists and several other related stakeholders to find patterns and conclusions that can be applied to business decisions. The average annual salary of a data analyst can go up to ₹12.0 Lakhs.

• Business Intelligence Analyst: 

Data is used by business intelligence analysts to assist organizations in adopting strategic choices. To evaluate data and display it to stakeholders in a relevant way, they make use of data visualization tools and statistical techniques. The annual salary of a business intelligence analyst can go up to ₹16.3 Lakhs.

• Data Engineer: 

Data engineers design, develop, and maintain a variety of infrastructures for large-scale data processing and storage. They work alongside data scientists to guarantee that data is readily available and usable. The annual salary of a data engineer can go up to ₹22.0 Lakhs.

• Data Visualization Analyst: 

Data visualization analysts design and produce understandable visual data representations for stakeholders. They build dynamic dashboards and reports using programs notably Tableau, Power BI, and D3.js. The annual salary of a data visualization analyst can go up to ₹17.0 Lakhs.

Why is MUJ’s online MBA in Analytics and Data Science the best?

Online MBA in Analytics and Data Science offered by Manipal University Jaipur provides learners with advanced knowledge and practical skills, and expertise in the data science domain. The course curriculum covers essential topics such as programming in data science, machine learning, data visualization, business analytics, and many other industry-relevant topics to help students gain in-depth knowledge of data science and analytics.

To know more, read Important concepts to learn during an MBA in Analytics and Data Science.

Manipal University Jaipur’s (MUJ) online degree programs are instructed by renowned professors and industry experts who exchange priceless insights regarding how to take on real-world obstacles. Online Manipal assists students with placement assistance to help them to find employment with respectable firms that offer competitive pay scales. Online Manipal also frequently hosts webinars with business professionals so that students can gain first-hand knowledge of how things operate in reality in the data science domain.

Conclusion

The structure of the global workforce, including that of data science professionals, has diversified due to the emergence of new technologies like blockchain, quantum computing, cloud computing, AI, and ML. The explosive boom in demand, the prospective promise in the field, and the increase in institutions providing specialized data science degrees suggest that data science specialists have a bright future ahead. 

The discipline of data science offers tremendous income potential, possibilities for lifelong learning, and the chance to have a profound effect on the globe. Thus, if you’re still wondering if data science is a feasible career in 2023, the answer is a resounding YES.