Conclusion

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:

• Learning theory without practical application

• Moving machine learning without prerequisites

• Using inappropriate data science terms in resume

• Not focusing on real projects and competition projects

• Forgetting that accuracy is not the most important thing

• Learning multiple tools

• Leaving public speaking and communication skills behind

• Not working on case studies

Education and interdisciplinary aspects

You will see that most jobs in the data science world require a master's (post-graduate) degree in a quantitative subject. However, if you do not have one, a diploma in data science or data science certification would be the second-best choice. Data science is interdisciplinary, which means that we combine two or more academic disciplines to perform a single task in data science.

Steps in a typical data science project

There are seven major steps in a typical data science project, starting with defining the problem in the most transparent way possible. The steps after problem definition include data collection, cleaning, exploratory data analysis, feature engineering, model building, and communication.

Numerical capabilities

You can build numerical capabilities by learning linear algebra, calculus, statistics, probability, and machine learning. Specifically, focus on parametric and non-parametric hypothesis testing, correlation and regression analysis, and probability distributions while starting. Additionally, Bayesian statistics, multivariate statistics, and time series analysis are essential. Lastly, you must understand the algorithms used in classification, regression, clustering, dimensionality reduction, decision trees, and boosting.

Programming

Programming is essential to data science as it allows data scientists to manipulate and analyze large datasets, build and evaluate models, and create visualizations and reports. SQL, Python, and R have widely used programming languages in data science for tasks such as data management, data cleaning, feature engineering, and machine learning. A very long list of functions, packages, and libraries is given in this course to help you prepare for interviews in the easiest possible way.

Portfolio making

There are things you should keep in mind while creating your resume, such as it should be a one-page document only, it should be customized for the company and the role you are applying for, and it should have relevant projects and keywords. The professional objective should highlight your experience and skills in data science and how they align with the company's goals. Even if you don't have proper job experience in data science, you can still add achievements to your resume, such as a published e-book, blog, or course.

Data scientist's toolbox

To become a data scientist, you should not just focus on programming tools but also on research and storytelling. Research is essential in understanding the domain and using data appropriately, and storytelling is crucial in communicating insights effectively to others. Yes, do not forget about the programming, but stick to a minimum, such as Python and SQL or R and SQL, when you are learning the concepts.

Python vs. R

Python is a free and open-source programming language that can be used for various purposes, such as data analysis, visualization, website development, software development, automation, and more. The syntax of Python is easy to read and understand, making it a high-level programming language. It also has a smoother learning curve compared to other languages.

R is also a free and open-source programming language that can be used for data analysis and data visualization. It is perfect for mathematical and statistical computations. The syntax of R is a bit more challenging to understand for a layman, but it is still widely used by statisticians and data scientists. Python is generally considered better due to speed and some other reasons. Its demand is also very high. Therefore, I suggest my students choose Python.

How to know that you are ready for interviews

To get the answer to this question, you need to answer the following questions yourself:

• Are you familiar with interview questions?

• Have you reviewed basic statistics and probability concepts?

• Have you reviewed linear algebra?

• Do you understand the basics of ML algorithms?

• Have you reviewed the basic programming concepts?

• Did you practice explaining your solutions?

• Did you participate in data science projects and competitions?

• Do you have a portfolio of your work?

• Are you prepared to talk about your experience and skills?

Dos and don'ts of preparation

The dos of data science interview preparation are to ask questions, focus on improving your skills, invest time strategically, and develop data thinking. The don'ts are to not cover all or many topics at a time, to not lose self-confidence, and to not start studying machine learning before having a good understanding of linear algebra, statistics, and probability.

Efficient job search

To find a data science job efficiently, only use these five job search engines; LinkedIn, Glassdoor, Indeed, Dice, and AI jobs. Try your best to approach companies directly, which will enhance your chances if they find your portfolio interesting.

Build your network with people already working in data science, such as data scientists, data engineers, machine learning engineers, data analysts, and so on. Before applying for a job, explore the role and the company thoroughly, customize your portfolio to meet the job responsibilities given in the role, do some freelance projects, and be honest and aware of the skills you know.

The interview

The interviews are generally taken in five steps, each of which is used to evaluate some common objectives and unique objectives, such as technical and nontechnical skills. The steps are as follows:

• Profile screening

• Candidature screening

• Test or data challenge

• Technical phone call

• On-site interview

The following are some skills that will be assessed throughout the interview process:

• Communication skills

• Business understanding

• Collaboration with others

Four mantras help you succeed in data science interviews: understanding the interviewer's expectations, using real-life examples, practicing case studies, and practicing industry-specific problems.

Types of data science interview questions

You can expect nine types of technical questions in data science interviews. As a programmer, you will find some of them easy, but others are more difficult to answer. But of course, you can answer them all if you practice. Here is the list:

• Statistics

• Probability

• Machine learning

• SQL

• Python

• R

• Guesstimation

• Puzzles

• Tricky questions

• Quick math

Final advice

It is normal to feel overwhelmed while learning some complex topics. However, it is essential to remember that no one can know everything or solve every problem. Many data scientists may lack confidence in their skills, but this is not due to a lack of knowledge. Instead, it is due to the constantly changing nature of data. To build confidence, it is recommended to set daily goals and work toward achieving them. It is essential to focus on the process rather than immediate outcomes and to remember that hard work will pay off in the long run.