# Introduction

Welcome to the **Data Science Interview Preparation** course! This course will assess your understanding of data science topics through both **theoretical** and **practical** exercises. This ensures that you are well-prepared to demonstrate your expertise during interviews. Let's delve into the integral parts of data science that we'll be covering:

### Python

Python is the backbone of modern data science. With its simplicity and readability, Python offers an extensive range of **libraries** and **frameworks**, making **data manipulation**, **analysis**, and **visualization** seamless. A deep understanding of Python is paramount not just for coding interviews but also for day-to-day data science tasks.

### Numpy

Numpy, short for **Numerical Python**, is a foundational package for **numerical computations** in Python. It provides support for **large multidimensional arrays** and **matrices**, along with a collection of **mathematical functions** to operate on these arrays. Grasping Numpy is crucial for tasks involving numerical data.

### Pandas

Pandas is the go-to library for **data manipulation** and **analysis**. It offers data structures for efficiently storing large datasets and tools for **reshaping**, **aggregating**, and **filtering** data. A data scientist often spends a significant chunk of time wrangling data, making Pandas an indispensable tool in their arsenal.

### Matplotlib

Visualization is a key aspect of data science. Matplotlib allows for the creation of **static**, **interactive**, and **animated visualizations** in Python. It provides a way to visually represent data, making it easier to discern **patterns** and **insights**.

### Seaborn

Building on Matplotlib, Seaborn is a statistical data visualization library that provides a **higher-level interface** for creating **attractive graphics**. It's tailored to work seamlessly with data frames in Pandas and arrays in Numpy, making the visualization process more intuitive and less time-consuming.

### Statistics

Data science is rooted in statistics. From **hypothesis testing** to **understanding distributions**, a solid grasp of statistics allows data scientists to make **informed decisions** based on data, **discern patterns**, and make **accurate predictions**.

### Scikit-learn

Machine Learning is a major subset of data science, and Scikit-learn is one of the most widely used libraries for ML. It provides simple and efficient tools for **data mining** and **analysis**. Knowing how to leverage Scikit-learn's tools is key for tasks like **model building**, **evaluation**, and **deployment**.

### Structure

Interviews often include a **practical** part in which you need to demonstrate the ability to **quickly complete a simple task**. This is necessary to make sure that you really know and **can put into practice** at least the **basic things** in the topics that you indicate in your resume.

The more difficult the position you are applying for, the more difficult the tasks will be. In this course, we will consider only fairly **simple tasks** that you can solve at a **Junior level** interview.

After each task, you can open **Code Description** to see an explanation of each line of code and possible alternatives. It is recommended that you first go through the task yourself and only after that open the **Code Description** section to test your knowledge.

Another important part of the technical interview is the **theoretical knowledge** test. It also tests how much you understand how the code or other **system will behave** in practice **in a given situation**.

### Conclusion

In conclusion, each of these components forms an integral part of a **data scientist's toolkit**. If you are unsure that you know enough to complete this course, we recommend that you first go through the **tracks**:

- Python from Zero to Hero
- Preparation for Data Science
- Data Visualization
- Foundations of Machine Learning

Let's embark on this journey together!

Everything was clear?

Course Content

Data Science Interview Challenge

## Data Science Interview Challenge

# Introduction

Welcome to the **Data Science Interview Preparation** course! This course will assess your understanding of data science topics through both **theoretical** and **practical** exercises. This ensures that you are well-prepared to demonstrate your expertise during interviews. Let's delve into the integral parts of data science that we'll be covering:

### Python

Python is the backbone of modern data science. With its simplicity and readability, Python offers an extensive range of **libraries** and **frameworks**, making **data manipulation**, **analysis**, and **visualization** seamless. A deep understanding of Python is paramount not just for coding interviews but also for day-to-day data science tasks.

### Numpy

Numpy, short for **Numerical Python**, is a foundational package for **numerical computations** in Python. It provides support for **large multidimensional arrays** and **matrices**, along with a collection of **mathematical functions** to operate on these arrays. Grasping Numpy is crucial for tasks involving numerical data.

### Pandas

Pandas is the go-to library for **data manipulation** and **analysis**. It offers data structures for efficiently storing large datasets and tools for **reshaping**, **aggregating**, and **filtering** data. A data scientist often spends a significant chunk of time wrangling data, making Pandas an indispensable tool in their arsenal.

### Matplotlib

Visualization is a key aspect of data science. Matplotlib allows for the creation of **static**, **interactive**, and **animated visualizations** in Python. It provides a way to visually represent data, making it easier to discern **patterns** and **insights**.

### Seaborn

Building on Matplotlib, Seaborn is a statistical data visualization library that provides a **higher-level interface** for creating **attractive graphics**. It's tailored to work seamlessly with data frames in Pandas and arrays in Numpy, making the visualization process more intuitive and less time-consuming.

### Statistics

Data science is rooted in statistics. From **hypothesis testing** to **understanding distributions**, a solid grasp of statistics allows data scientists to make **informed decisions** based on data, **discern patterns**, and make **accurate predictions**.

### Scikit-learn

Machine Learning is a major subset of data science, and Scikit-learn is one of the most widely used libraries for ML. It provides simple and efficient tools for **data mining** and **analysis**. Knowing how to leverage Scikit-learn's tools is key for tasks like **model building**, **evaluation**, and **deployment**.

### Structure

Interviews often include a **practical** part in which you need to demonstrate the ability to **quickly complete a simple task**. This is necessary to make sure that you really know and **can put into practice** at least the **basic things** in the topics that you indicate in your resume.

The more difficult the position you are applying for, the more difficult the tasks will be. In this course, we will consider only fairly **simple tasks** that you can solve at a **Junior level** interview.

After each task, you can open **Code Description** to see an explanation of each line of code and possible alternatives. It is recommended that you first go through the task yourself and only after that open the **Code Description** section to test your knowledge.

Another important part of the technical interview is the **theoretical knowledge** test. It also tests how much you understand how the code or other **system will behave** in practice **in a given situation**.

### Conclusion

In conclusion, each of these components forms an integral part of a **data scientist's toolkit**. If you are unsure that you know enough to complete this course, we recommend that you first go through the **tracks**:

- Python from Zero to Hero
- Preparation for Data Science
- Data Visualization
- Foundations of Machine Learning

Let's embark on this journey together!

Everything was clear?