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Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.
It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?
Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.
Understanding the differences between SQL and NoSQL databases is crucial for students. Big Data and Machine Learning The intersection of Big Data and Machine Learning is a critical area of focus in a Big Data syllabus. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.
Tools like pandas and SQL help manipulate and query data , while libraries such as matplotlib and Seaborn are used for data visualisation. Algorithm and Model Development Understanding various Machine Learning algorithms—such as regression , classification , clustering , and neural networks —is fundamental.
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