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Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Big data platforms such as ApacheHadoop and Spark help handle massive datasets efficiently. Operations Analysts focus on improving business processes by leveraging performance data.
Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. Check out this course to upskill on Apache Spark — [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).
Additionally, its naturallanguageprocessing capabilities and Machine Learning frameworks like TensorFlow and scikit-learn make Python an all-in-one language for Data Science. SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases.
Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. It allows unstructured data to be moved and processed easily between systems.
Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, NaturalLanguageProcessing , Statistics and Mathematics. You should be skilled in using a variety of tools including SQL and Python libraries like Pandas.
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