Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

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

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

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

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

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 1

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

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

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

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

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 1

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

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

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

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

Everything was clear?

Thanks for your feedback!

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

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

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

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

Section 2. Chapter 1

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