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Data Science Interview Challenge
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Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

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Challenge 3: Dictionary Manipulation

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

  • Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
  • Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
  • Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Aufgabe

Swipe to start coding

Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

Dictionary Manipulation

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 4
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book
Challenge 3: Dictionary Manipulation

Dictionaries in Python offer a structured and flexible way to store data. The strengths of dictionary manipulation include:

  • Key-Value Pairs: Dictionaries store data as key-value pairs, allowing rapid data retrieval by just knowing the key. This can drastically improve performance in look-up operations.
  • Dynamic and Flexible: Dictionaries in Python can grow and shrink dynamically and can even contain mixed data types, offering great flexibility.
  • Data Structuring: They provide a way to organize data hierarchically and can be nested, making them perfect for complex data representation.

In essence, dictionary manipulation ensures efficient and organized data storage and retrieval.

Aufgabe

Swipe to start coding

Given a dictionary of items and their prices, write a Python function to return items with prices greater than a given value.

Dictionary Manipulation

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 4
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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