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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

Challenge 2: Basic Model Creation

In the realm of machine learning, the creation of models can be broadly categorized into supervised and unsupervised learning.

Supervised learning is a method where a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, and it learns to map the inputs to the desired outputs. Examples include regression, where we predict a continuous value, and classification, where we assign input data into one of the predefined categories.

On the other hand, unsupervised learning operates without labeled data, aiming to identify patterns or structures within the data. The algorithm isn't told the "correct" answer but rather tries to extract insights on its own. Techniques such as clustering, where data is grouped based on inherent similarities, and dimensionality reduction, where redundant or less informative features are minimized or removed, are classic examples.

Both supervised and unsupervised learning methods are fundamental in data science and offer various tools to address a wide range of problems and challenges.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Everything was clear?

Section 7. Chapter 2
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Challenge 2: Basic Model Creation

In the realm of machine learning, the creation of models can be broadly categorized into supervised and unsupervised learning.

Supervised learning is a method where a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, and it learns to map the inputs to the desired outputs. Examples include regression, where we predict a continuous value, and classification, where we assign input data into one of the predefined categories.

On the other hand, unsupervised learning operates without labeled data, aiming to identify patterns or structures within the data. The algorithm isn't told the "correct" answer but rather tries to extract insights on its own. Techniques such as clustering, where data is grouped based on inherent similarities, and dimensionality reduction, where redundant or less informative features are minimized or removed, are classic examples.

Both supervised and unsupervised learning methods are fundamental in data science and offer various tools to address a wide range of problems and challenges.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Everything was clear?

Section 7. Chapter 2
toggle bottom row

Challenge 2: Basic Model Creation

In the realm of machine learning, the creation of models can be broadly categorized into supervised and unsupervised learning.

Supervised learning is a method where a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, and it learns to map the inputs to the desired outputs. Examples include regression, where we predict a continuous value, and classification, where we assign input data into one of the predefined categories.

On the other hand, unsupervised learning operates without labeled data, aiming to identify patterns or structures within the data. The algorithm isn't told the "correct" answer but rather tries to extract insights on its own. Techniques such as clustering, where data is grouped based on inherent similarities, and dimensionality reduction, where redundant or less informative features are minimized or removed, are classic examples.

Both supervised and unsupervised learning methods are fundamental in data science and offer various tools to address a wide range of problems and challenges.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Everything was clear?

In the realm of machine learning, the creation of models can be broadly categorized into supervised and unsupervised learning.

Supervised learning is a method where a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, and it learns to map the inputs to the desired outputs. Examples include regression, where we predict a continuous value, and classification, where we assign input data into one of the predefined categories.

On the other hand, unsupervised learning operates without labeled data, aiming to identify patterns or structures within the data. The algorithm isn't told the "correct" answer but rather tries to extract insights on its own. Techniques such as clustering, where data is grouped based on inherent similarities, and dimensionality reduction, where redundant or less informative features are minimized or removed, are classic examples.

Both supervised and unsupervised learning methods are fundamental in data science and offer various tools to address a wide range of problems and challenges.

Task

Train a RandomForest classifier to predict wine types based on their chemical properties and evaluate the performance of the model.

  1. Split the data into training and test sets.
  2. Train a Random Forest Classifier using the training set. Set the number of trees of the forest to 20 and max depth of every of them to 4.
  3. Evaluate the model's performance using a classification report.

Section 7. Chapter 2
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