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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

book
Challenge 4: Nested Loops

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

  • Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
  • Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
  • Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Tâche

Swipe to start coding

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Nested Loops
  1. Set up for loop.
  2. Set up while loop.
  3. Append inner row.
  4. Increase counter.
  5. Increase row index.
  6. Append matrix.

Solution

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Section 1. Chapitre 5
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book
Challenge 4: Nested Loops

Nested loops in Python are a cornerstone for advanced iteration over data sets. Their importance lies in:

  • Multi-dimensional Data Handling: They allow for iterating over complex data structures like matrices or multi-dimensional lists, enabling deep data traversal.
  • Combination and Permutation: Nested loops are the go-to solution when you need to evaluate all possible combinations or permutations of multiple lists.
  • Granular Control: They offer fine-tuned control over iteration, allowing programmers to dictate the exact flow and conditions of looping.

Using nested loops, developers can harness the power of iteration to its fullest potential.

Tâche

Swipe to start coding

The goal is to generate a matrix (a list of lists) where the outer list contains n lists and each inner list contains m integers. These integers should be in ascending order starting from 1.

Nested Loops
  1. Set up for loop.
  2. Set up while loop.
  3. Append inner row.
  4. Increase counter.
  5. Increase row index.
  6. Append matrix.

Solution

Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 1. Chapitre 5
Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
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