1. What are the responsibilities of Data Scientist I?
Data Scientist I identifies trends, patterns, and anomalies found in big data sets by performing extensive data analysis to develop insights. Performs data mining, cleaning, and aggregation processes to prepare data, implement data models, conduct analysis, and develop databases. Being a Data Scientist I interprets results from multiple structured and unstructured data sources using programming, statistical, and analytical techniques and tools. Collaborates with teams to understand each data analysis projects' underlying purpose, focus, and objectives. Additionally, Data Scientist I designs, develops, and implements the most valuable data-driven solutions for the organization. May require a master's degree in computer science, mathematics, engineering or equivalent. Typically reports to a manager. The Data Scientist I work is closely managed. Works on projects/matters of limited complexity in a support role. To be a Data Scientist I typically requires 0-2 years of related experience.
2. What are the skills of Data Scientist I
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
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Analysis: Analysis is the process of considering something carefully or using statistical methods in order to understand it or explain it.
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Data Analysis: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
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Economics: Economics is a social science that focuses on the production, distribution, and consumption of goods and services, and analyzes the choices that individuals, businesses, governments, and nations make to allocate resources.