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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 4: Handling Missing Values

Managing gaps in your datasets is a task that no data scientist can overlook. In this area, NumPy offers an extensive set of tools. Whether it's detecting, removing, or filling missing values, NumPy has functionalities tailored to handle these tasks with ease.

Employing NumPy's capabilities in handling missing values not only refines your datasets but also paves the way for a more robust and reliable analysis, a cornerstone in data science undertakings.

Tehtävä

Swipe to start coding

Sometimes, datasets might have missing or non-numeric values. Handle them efficiently with numpy.

  1. Check for the presence of NaN values. Set True if NaN exists, False if not.
  2. Replace NaN values with 0.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

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Kiitos palautteestasi!

Osio 2. Luku 4
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book
Challenge 4: Handling Missing Values

Managing gaps in your datasets is a task that no data scientist can overlook. In this area, NumPy offers an extensive set of tools. Whether it's detecting, removing, or filling missing values, NumPy has functionalities tailored to handle these tasks with ease.

Employing NumPy's capabilities in handling missing values not only refines your datasets but also paves the way for a more robust and reliable analysis, a cornerstone in data science undertakings.

Tehtävä

Swipe to start coding

Sometimes, datasets might have missing or non-numeric values. Handle them efficiently with numpy.

  1. Check for the presence of NaN values. Set True if NaN exists, False if not.
  2. Replace NaN values with 0.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 2. Luku 4
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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