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Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) HypothesisTesting: Formally testing assumptions or theories about the data using statistical methods to determine if observed patterns are statistically significant or likely due to chance.
One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. There is one Query language known as SQL (Structured Query Language), which works for a type of database. SQL Databases are MySQL , PostgreSQL , MariaDB , etc.
With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc.
Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. However, there are a few fundamental principles that remain the same throughout.
Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses. Inferential Statistics: A branch of statistics that makes inferences about a population based on a sample, allowing for hypothesistesting and confidence intervals.
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