Conclusion

Get an overview of what we have learned and how to move forward.

First of all, congratulations on reaching the end! We're supremely confident if you follow the recommended tips and tricks, you will land a job in data science. Let's have a look at the summary of this course.

Introduction

This course covers common queries related to data science as a career option, including the benefits of the field, the necessary education, and the steps required to start a career in data science, as well as tips for resume building, programming language choices, interview preparation, and job search strategies.

Additionally, it covers the dos and don'ts of data science interview preparation, top job search sites, and mantras for success in a data science interview. The course also includes examples of the questions that may be asked in a data science interview.

Why data science?

There are five major reasons to choose a career in data science—less competitive, not difficult, better future, attractive compensation, and many work options. Additionally, the lack of appropriately skilled data scientists is a concern in the market, and therefore, it is an opportunity for individuals to take advantage of the market.

Is it really for you?

Before choosing data science as a career option, consider how it can help achieve your professional goals. To make this decision, you can consider whether you have a problem-solving attitude or not, whether you are comfortable dealing with complex tasks, and whether you are a team player and have a broad view. It's essential to have a clear goal before making any decisions.

Job titles in data science

Data scientist is not the only job title in the data science profession. Some other titles are data analyst, business analyst or business intelligence analyst, data engineer, data architect, machine learning engineer, and statistician.

Common mistakes made by aspiring data scientists

There are eight mistakes almost every aspiring data scientist makes that slow down their journey toward becoming a data science professional, and these mistakes are: ...