What makes a good data scientist? Most employers and recruiters prefer proficiency testing to find the perfect candidate. Hiring someone who lacks basic Data Science skills can be a costly mistake. In this article, we’re going to discuss 5 must-have skills for data scientists in 2022.
However, successful data scientists have features that are not recognizable in a standard proficiency test. They have different skills and qualities that you may not learn from any book.
Companies and recruiters are increasingly turning to AI (artificial intelligence) solutions and ML (machine learning) to get the right employees. Using predictive and contextual algorithms, Headstart examines candidates and assigns them appropriate tasks can look like an automated and more accurate process than hiring the candidates manually.
Must-Have Skills For Data Scientists
1. Knowledge of Programming Language
2. Data Analysis Tools
3. Knowledge of Statistics
4. Data-Driven Decision Making
5. Data Reporting & Visualization Skills
These are the 5 must-have skills that companies look for in a data scientist. In order to become a successful data professional, you need to achieve a few milestones before becoming a data scientist. Below, we’ll explain all these 5 skills one by one and tell how knowledge of these skills can help you in your career growth.
1. Knowledge of Programming Language: In order to become a data analyst, data engineer, or data scientist, you must be proficient in one of the programming languages like R, Python, SAS, Hadoop etc. The role of a data scientist is not just about writing code but being comfortable & familiar with using different data analysis environments.
With the technologies upgrading, data scientists must be upskilling themselves with time in order to keep up with the trend.
2. Data Analysis Tools: A data analyst or a data scientist must be proficient with data analysis tools. With different programming languages, com different tools for data analysis. For example, if you learn Python Programming, knowledge of Python libraries like NumPy, Pandas, Matplotlib, and Seaborn is necessary. These tools & libraries are used in analyzing the data in top companies around the globe.
3. Knowledge of Statistics: Knowledge of mathematical models and statistics is a must-have skill in a successful data scientist. Use of statistics in data-based models and in developing algorithms is required. Without the knowledge of statistics, the career of a data scientist can not grow as expected.
4. Data-Driven Decision Making: For a company to grow, a data scientist has to make certain decisions at many points that ultimately result in driving growth and generating revenue for the company. The role of a data scientist requires smart people that understand the datasets and must be able to make data-driven decisions based on past & current data.
5. Data Reporting & Visualization Skills: the role of a data scientist not only requires data analysis skills but also how they present the data in front of the management or client so that it gets easy for others to understand the data. Using data visualization tools like Matplotlib & Seaborn can help them present the extracted data in graphical formats.
For data reporting, data scientists require knowledge of advanced BI & reporting tools like Microsoft Power BI or Tableau. These tools help data scientists in presenting their data in a beautiful & interactive manner using various types of visually immersive reports and dashboards.
If you’re a fresher or a working professional who wants to pursue your career as a data professional, you can get in touch with our team at Console Flare. Our team of counselors will help you understand the field of data from the very scratch and clear all your doubts regarding data science.
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