Trends In Scaling Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, thus applying knowledge and actionable insights from data across a broad range of application domains. Data science covers a diverse range of capability that brings valuable insights to enable business decision making. Data science has emerged over the last century as a field of study wherein enterprises are developing technologies like natural language processing, computer vision, deep learning.
Focus on Edge Intelligence
As per the research study by Forrester and Gartner, edge computing/edge intelligence will be the mainstream focus for organizations over the coming decade. Organizations will make use of their IoT capabilities to transform data (data analysis & aggregation) by incorporating edge computing thus increasing business scalability, reducing latency, and increasing the processing speed.
Deep fakes, Generative AI, & Synthetic data
The technology behind deep fake is known as generative AI. Organizations are researching on its huge potential in creating synthetic data for training the ML algorithms. It will have different benefits like training facial recognition & image recognition algorithms, and in creating language to image capabilities also ensuring that the privacy concerns are being avoided.
Blend of Data Visualization with AR & VR
One of the critical function of data visualization technology within the data science space is that it acts as a link between the specialist who evaluate data and the customers who consume the outcome of the efforts. Most of the organizations are blending data visualization with Augmented and virtual reality technology to create immersive data experience for the customers to be able to access and make use of the data with ease.
End to End Automated Machine Learning
As per Gartner, one of the dominant trends within the data science space is Hyper-automation. RPA, BPM, and advanced augmented analytics are the core concept of hyper-automation. Everything and anything can be automated to improve efficiency. Organizations can unlock higher level of digital transformation by combining automation with AI & ML, thus speeding up the process of algorithm selection and hyperparameter tuning. With advancements in technology like AutoML, data science procedure ranging from data cleansing and preparation to feature engineering and data exploration would become more automated over the coming decade. Developers can create algorithms, tools, platforms, neural networks by using AutoML.
The ever evolving Data Science
Software engineering was a buzzword a few years back; now having move to data science as a commonplace word. In addition, it has also become an aspiration for new job opportunities both in the commercial, research and educational sectors. There have been high quality educational courses designed that address both foundational aspects which includes primary, transition, operational and executive programmes. In addition, enterprises have been developing specialized programs that help leap-frog data science with specific contextualization of the workforce both from up-skilling as well as new capability building.
As per a study conducted by Kaggle to understand the challenges faced by data science teams in organizations taking into consideration 16000 data professionals are Dirty data (36% reported), Lack of data science talent (30%), Company politics (27%), Lack of clear question (22%), inaccessible data (22%), Results not used by decision-makers (18%), Explaining data science to others (16%), Privacy issues (14%), Lack of domain expertise (14%), and small organization and cannot afford the data science team (13%). As per the research conducted by Michael Grogan, a data science consultant, the top three languages remain Python, R, and SQL. However, there are several interesting changes in 2021 as compared to 2019.
· While 33% of roles advertised Tensor Flow among the demanded skills in 2019, less than 6% did in 2021.
· Demand for Spark was 38% in 2019, while it had fallen to 11% in 2021.
· Interestingly, demand for R has increased significantly from 41% in 2019 to 72% in 2021.
The pandemic has disrupted all the industries around the globe. Enterprises had no option but to adapt digitalization in no time, leading to a higher investment in data science and data analytics thus becoming the central focus for all the organizations. This is how data science is expected to develop over the next ten years.
