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DataWrangling with Python Sheamus McGovern | CEO at ODSC | Software Architect, Data Engineer, and AI Expert Datawrangling is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain.
Skills and qualifications required for the role Data scientists require a diverse set of skills and qualifications to excel in their role. Programming skills: Data scientists should be proficient in programming languages such as Python, R, or SQL to manipulate and analyze data, automate processes, and develop statistical models.
In Inferential Statistics, you can learn P-Value , T-Value , HypothesisTesting , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , DataWrangling , and EDA which is also known as Exploratory Data Analysis.
According to a survey by IBM, over 60% of Data Scientists report that keeping up with new technologies and methodologies is one of their biggest challenges. Additionally, the sheer volume of data generated daily complicates the process. As of 2023, it is estimated that 175 zettabytes of data will be created globally each year.
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