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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: 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.

Task
test

Swipe to show code editor

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.

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 2. Chapter 4
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bookChallenge 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.

Task
test

Swipe to show code editor

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.

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 2. Chapter 4
toggle bottom row

bookChallenge 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.

Task
test

Swipe to show code editor

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.

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!

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.

Task
test

Swipe to show code editor

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.

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