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Data Science Interview Challenge
course content

Course Content

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 1: Array Creation

NumPy allows for an efficient and structured approach to create arrays. The benefits of this approach are:

  • Flexibility: NumPy provides numerous functions to create arrays, whether they are uniform, random, or based on existing data. This means you can generate data suitable for a wide range of scenarios.
  • Speed: Creating arrays using NumPy is generally faster than using standard Python lists, particularly for larger arrays.
  • Integration: You can use NumPy arrays seamlessly with many other libraries, enhancing compatibility.

In essence, when dealing with numerical data, using NumPy's array creation capabilities can enhance both the speed and the reliability of your data generation process.

Task

Swipe to start coding

Numpy provides powerful tools to efficiently create arrays filled with data.

  1. Use numpy to create an array of 10 zeros.
  2. Now, create an array of 10 fives.
  3. Generate an array with numbers from 10 to 20.

Solution

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SectionΒ 2. ChapterΒ 1
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book
Challenge 1: Array Creation

NumPy allows for an efficient and structured approach to create arrays. The benefits of this approach are:

  • Flexibility: NumPy provides numerous functions to create arrays, whether they are uniform, random, or based on existing data. This means you can generate data suitable for a wide range of scenarios.
  • Speed: Creating arrays using NumPy is generally faster than using standard Python lists, particularly for larger arrays.
  • Integration: You can use NumPy arrays seamlessly with many other libraries, enhancing compatibility.

In essence, when dealing with numerical data, using NumPy's array creation capabilities can enhance both the speed and the reliability of your data generation process.

Task

Swipe to start coding

Numpy provides powerful tools to efficiently create arrays filled with data.

  1. Use numpy to create an array of 10 zeros.
  2. Now, create an array of 10 fives.
  3. Generate an array with numbers from 10 to 20.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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