• Programming
  A data scientist will be skilled in one or more programming languages, such as R, Python, SAS, Hadoop, and so on. It is not just about writing code, but also about becoming acquainted with multiple programming environments for data analysis. With the area of data science seeing unprecedented attention and importance in organizations worldwide, a grasp of programming languages and the capacity to adapt to evolving technologies are critical to a data scientist’s success. Any reluctance to use programming tools might be a deal-breaker for a corporation that relies on your expertise to boost its business growth.
• Quantitative Analysis
  This is the essence of the job of a data scientist. A data scientist’s profile includes having a calculated and visceral awareness of a complicated environment and its behavior, munging data that is messy and difficult to deal with, and generating prototypes and models to test hypotheses. Machine learning — supervised and unsupervised learning algorithms, time-series forecasting, data-reduction techniques, neural networks, and so on – are all must-know ideas.
• Math and stats knowledge
  Without statistics, a data scientist and an organization’s future are at risk. Without math and statistics, it will be hard to generate hypotheses based on how a system would respond to changes, make statistically significant assumptions about data variances, define metrics to lay out objectives and measure progress, and draw reliable conclusions from the dataset. Writing code or properly employing functions will also be difficult if a solid basis in arithmetic and statistics is lacking.
• Visualization Skills
  It is well known that humans absorb information more quickly in the form of visuals than in words or figures. Working knowledge of data visualization technologies such as Tableau, Qlikview, Plotly, or Sisense will ensure that a data scientist can confidently convey insights to both a technical and non-technical audience, persuading them of the business value their insights can provide. A data scientist’s success may be determined by familiarising oneself with the concepts of visualizing and presenting attractive data to stakeholders.
• Multivariate Analysis and Linear Algebra
  It may or may not be expressly asked in an interview, but a data scientist may have to design their implementation models in-house at some time. This is especially true when data-driven solutions might result in transformational improvements for the firm. Data science is a relatively recent field with no fixed job definitions. When building out-of-the-box models, a working grasp of linear algebra and multivariable calculus might be useful. In addition, an interviewer may surprise you with a calculus question. A self-assured data scientist will advise them to go for it!

Conclusion
Whether you are an employer or a recruiter, this list of successful data scientists’ skills and qualities can help you find the best candidates. When making your next recruitment, search for applicants that have a solid combination of data intuition, statistical thinking abilities, a “hacker’s spirit,” and a fair dosage of creativity, in addition to technical talents. Data scientists with these qualities will undoubtedly help your organization develop and prosper.

The post Top 5 Skills for Data Scientist jobs appeared first on AICRA.