Artificial intelligence and data science are two of the most important technologies changing the way we live and work. In recent years, AI investments have surged from $12.75 billion in 2015 to $93.5 billion in 2021, and market forecasts by Zion Market Research show that these figures will reach $422.37 billion by 2028, driving the economy is shown to be important.
In terms of jobs and duties, AI and ML engineers/programmers and data scientists are essential to building AI tools. Although the terms data science and AI are often used interchangeably, it should be understood that data science is a key pillar and aspect of AI, and knowledge of it is essential for training and building AI models. . It is also important to distinguish between data analysts and data scientists. The former has implications for structured data, while the latter focuses on structuring unstructured data for further use.
As the importance of AI engineers and data scientists grows each year, INDIAai surveyed the job landscape to understand their journey and future prospects. Our survey was conducted to enlighten her readers, especially students who want to build a career in her AI and data science.
methodology
Before digging deeper to highlight some interesting findings, we cleaned, filtered, and processed the data to make it ready for analysis. Having done this, the data were divided into multiple age groups and total years of experience. In terms of geographic spread, most of the respondents are from Delhi NCR, followed by Maharashtra and Karnataka. Among the 130 respondents, we found a balanced distribution between AI/ML professionals and data scientists. Respondent profiles ranged from being an AI/ML engineer and architect with his focus on NLP and computers to his vision, to professors at prestigious universities with interests in research such as image processing and meta-learning.
We performed multiple analyzes to understand how educational qualifications and additional training/programming courses affect job prospects in this field. To do so, we categorized the dataset into total years of experience, education, highest degree earned, and courses taken, and analyzed their impact on salary packages and data science careers. Additionally, the data are sliced and diced at multiple levels to analyze the results and gain interesting findings.
The survey focuses on the educational background and career paths of respondents in data science and AI/ML, but takes a softer approach to salary packages in this area. In this regard, rather than specifically asking about current salary packages, respondents were asked for their opinion on expected average salaries for Data Scientist/AI/ML professionals. This approach has paved the way for multiple interpretations, but INDIAai reflects only one of his.
Main findings
Out of the many findings from the survey, INDIAai would like to point out some interesting findings.
- Engineers dominate the fields of data science/AI/ML.
- Nearly 70% of respondents who did not start their data science/AI/ML careers were able to move into this field after eight years of work experience.
- Familiarity with programming languages such as R, Python, and Java can be a good starting point for a career in data science/AI/ML.
Comparison with other employment surveys
Research is always essential to understand the current scenario in any field/domain or sector. Multiple studies are conducted at different levels with several objectives. Such objectives may change from time to time and from place to place. Belatedly, there is a growing body of research around the world to understand the data scientist’s journey and to educate others about the same. One such notable survey is his Kaggle survey. INDIAai took inspiration from research and fine-tuned it for purpose. Unlike Kaggle’s research, the scope of his research is limited to one country, India, and his findings are supplemented by in-depth interviews with experts in the field.
important point
This research has provided a better understanding of the data science/AI/ML job market and the steps required to start/shift a career in this field. Here are some lessons that Team INDIAai learned from the investigation:
- Among freshmen without a data science background, there is a strong urge to pursue a career in the data science/AI/ML space.
- As the sector is still developing, there is a young workforce (30-40 years old) in this sector.
- Candidates with non-engineering backgrounds can also advance into data science/AI/ML areas with prior training/courses
- Experienced professionals in this field are well rewarded financially
