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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 1: DataFrame Creation

Pandas, a powerful data manipulation library in Python, provides multiple efficient and intuitive methods to create DataFrames. The advantages of using these methods include:

  • Versatility: Pandas offers a variety of ways to create DataFrames from different types of data sources. This ensures flexibility based on data availability and format.
  • Ease of use: The syntax for creating DataFrames is clear and consistent, simplifying data wrangling tasks.
  • Integration: DataFrames can easily be converted to and from other data structures, promoting interoperability with different libraries.

In the realm of data science and analytics, Pandas' DataFrame creation tools guarantee both convenience and consistency in your data processing workflow.

Oppgave

Swipe to start coding

Create a Pandas DataFrame using three different methods:

  1. Read data from a CSV file.
  2. Create a DataFrame from a NumPy array. Column names must be A, B and C.
  3. Construct a DataFrame from a Python dictionary.

Løsning

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Seksjon 3. Kapittel 1
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book
Challenge 1: DataFrame Creation

Pandas, a powerful data manipulation library in Python, provides multiple efficient and intuitive methods to create DataFrames. The advantages of using these methods include:

  • Versatility: Pandas offers a variety of ways to create DataFrames from different types of data sources. This ensures flexibility based on data availability and format.
  • Ease of use: The syntax for creating DataFrames is clear and consistent, simplifying data wrangling tasks.
  • Integration: DataFrames can easily be converted to and from other data structures, promoting interoperability with different libraries.

In the realm of data science and analytics, Pandas' DataFrame creation tools guarantee both convenience and consistency in your data processing workflow.

Oppgave

Swipe to start coding

Create a Pandas DataFrame using three different methods:

  1. Read data from a CSV file.
  2. Create a DataFrame from a NumPy array. Column names must be A, B and C.
  3. Construct a DataFrame from a Python dictionary.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 3. Kapittel 1
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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