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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

bookChallenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

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Section 3. Chapter 4
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bookChallenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

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!

Section 3. Chapter 4
toggle bottom row

bookChallenge 4: Altering DataFrame

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

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!

Pandas provides a plethora of tools that allow for easy modification of both data and structure of DataFrames. These capabilities are essential because:

  • Data Cleaning: Real-world datasets are often messy. The ability to transform and clean data ensures its readiness for analysis.
  • Versatility: Frequently, the structure of a dataset may not align with the requirements of a given task. Being able to reshape data can be a lifesaver.
  • Efficiency: Direct modifications to DataFrames, as opposed to creating new ones, can save memory and improve performance.

Getting familiar with the techniques to alter data and the structure of DataFrames is a key step in becoming proficient with Pandas.

Task

Harness the power of Pandas to alter data and the structure of DataFrames:

  1. Add a new column to a DataFrame with values Engineer, Doctor and Artist.
  2. Rename columns in a DataFrame. Change the Name column into Full Name and the Age column into Age (years).
  3. Drop a column City from a DataFrame.
  4. Sort a DataFrame based on the Age column (descending).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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