This data science resume building guide will take you through the following topics –
What makes a good resume?
Building a good resume can be a tricky challenge, but keeping a few important things in mind will help your resume stand out. Choosing the right format and making the resume aesthetic is very important. Next, always keep your resume up-to-date and relevant to the job you’re applying for. The most crucial step to take care of is proofreading. Poorly spelled words and a grammatically incorrect resume will reduce your chances of being hired by multiple folds.
Taking care of all these things will help you list your educational and professional background, work experience and skills in a way that leaves a good impression on prospective employers and recruitment managers.
Watch this video by Intellipaat to learn more about writing an impressive resume.
Since 2012, the demand for data scientists has grown by 650%! As per LinkedIn’s 2017 US Emerging Jobs Report, Data Scientist was the second most sought-after job, in a list of 20 emerging jobs. So now let’s learn about a career in the data science industry and the different job roles available.
Careers in Data Science
A research from the year 2013 revealed that 90% of the total data present in the world was created in the 2 years prior to 2013. Imagine that. In a matter of two years, we accumulated 9 times the total data collected over thousands of years of humankind combined. An estimate showed that the amount of data by the end of 2020 would have been a whopping 45 zettabytes. To make all of this information of value and of use to us, and to apply it to the real world and practical scenarios, we need data science, hence making it one of the most promising career options.
What is Data Science? Read our blog on Data Science to know more.
If you’re someone trying to start a career in data science, a few job roles in industry are explained below, for you to decide what would suit you best, as per your skill set.
Data Analysts, as the term suggests, analyze large amounts of data, decipher meanings, and understand trends. Every day, around 2.5 quintillion bytes of data is created in the world. Data Analysts use this data and extract valuable information from data and present them in a manner that everyone can understand. People with a strong knowledge of mathematics, statistics, and computer science are the best fit for this career.
Business Intelligence Analyst
BI Analysts make use of data analysis to transform data into insights that improve business value. They can study trends that will help all departments in the organization and the management to make intelligent decisions for the benefit of the organization. BI Analysts explore revenue losses and suggest improvements to increase profits. They should have a good knowledge of statistics, SQL, and programming languages like C# and Python.
Data Mining Engineer
These are specialists who mine hidden information from large amounts of data and suggest how this information can provide value to the organization. As the job requires the use of statistical software to analyze data, data mining engineers should have a combination of business intelligence and technical skills like Java, Perl, and Python, and Data Analysis tools like SQL and Hadoop.
Their responsibility is to design electronic databases for storing and organizing data. To do this, they have to understand the organization’s existing data infrastructure and then write codes to create secure databases that can be used by thousands of people. Data architects should have technical skills like Machine Learning and Natural Language Processing and knowledge of applied mathematics and statistics.
To learn more about building a career in data science, read our blog on How to start a career in Data Science – Data Science Jobs
In America alone, there are more than 35 thousand skilled data scientists and hundreds of companies willing to hire them. With the role having such a high demand, getting a job as a data scientist is no piece of cake. Having a degree or a certification is not enough. So, now that you know about the career options and demand of data science, let’s learn about a small but crucial step that most people forget to pay attention to – The data science resume.
Building a Data Science Resume
Data science blends the multiple disciplines of machine learning, algorithms, data inference, programming, mathematics, and statistics to extract useful inference from data and solve complex problems. Anyone skilled in these disciplines, be it a fresher or a seasoned professional, can get a job in the field of data science if they build a strong resume. In this section, we discuss a few suggestions you should follow, to build the perfect resume.
Keeping it short
The first step would be to list down everything that you want to mention in your resume. The key is to make everything concise and to the point. From all of your certifications and projects, choose the ones recent and most relevant to data science and mention only those. It is very important to prioritize.
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Selecting a Resume template
A few years back, all resumes would have the same, simple design, but these days, it is also an added point if you work on the visual appeal of the resume. This makes it unique and your resume stands out from the crowd. So the next step would be to pick a design for the resume according to your own aesthetic preference. There are multiple free online tools that will let you build a CV using pre-existing designs. Pick a design template that is appealing and unique, but yet simple and makes the content easily readable. Using too many colours would do more harm than good.
Profile and Contact Information
Once you’ve picked a suitable design template, mention your contact information in the resume. Mention your email address, contact number, and links to your LinkedIn and GitHub profiles. Make sure all your data science-related projects are present on your GitHub account for employers to see. It also leaves a good impression if you write a brief description about yourself, in a few sentences that tell about your skills, interests and highest qualification.
Read our blog Data Science Tutorial for Beginners to start learning today.
Writing your Skills
Writing about your skills is the most important step while building your resume. They should always be mentioned in the order according to their relevance to the job role you’re applying for.
Some of the skills required to start a career in data science are –
- Python Programming
Python is the most popular programming language among data scientists these days. Be it data mining or running embedded systems, python can do everything. Pandas, a python library, is used for data analysis and can do anything from plotting data with histograms, to import data from spreadsheets.
- R Programming
R can implement ML algorithms to give us a vast variety of statistical and graphical techniques like time-series analysis, clustering, classical statistical tests, etc. It is used for calculations and data manipulation.
- SQL Databases
SQL is a programming language used to manage and query data that is held in a relational database management system. SQL is used to read, retrieve or update data, and to insert new data.
- Machine Learning and Artificial Intelligence
ML helps in analyzing large amounts of data using algorithms. Using Machine Learning, major parts of a data scientist’s jobs can be automated. The most skilled data scientists are highly familiar with machine learning techniques and advanced machine learning such as recommendation engines and Natural Language Processing.
- Business Strategy
Data Scientists should understand businesses, the problems that are faced, and should have the ability to provide solutions by conducting analyses. This helps them use data in a way that is helpful to the organization
- Data Visualization
A data scientist should be able to represent data graphically, using charts, graphs, maps, etc. Visualization is very important to make sense of the large amount of data generated each day.
Read our blog Most Valuable Data Science Skills Of 2021 to know about more data science skills
Projects, Accomplishments and Publications
Next, mention your accomplishments, awards, and publications. Since you’re seeking a role in the data science industry, mentioning publications, projects, and achievements related to data science is very important.
In case you have multiple publications, only mention the most relevant few, published in reputed journals. It is a good idea to attach a link to your publication. Companies always give priority to candidates who have hands-on experience in the field. While mentioning projects, always give a brief description about them, and in the case of group projects, explain what your role and contributions were. Next, let’s discuss some data science project ideas.
Data Science Projects for Resume
For anyone aspiring to be a data scientist, it is important to have more than just certifications. Having good projects mentioned in your resume leaves a good impression. Given below are a few interesting data science projects suggestions to work on, to strengthen your resume –
- Movie Recommender
This project recommends movies and TV shows to users, based on movies they’ve previously watched. Netflix and Spotify use similar systems.
- Detecting Driver Drowsiness
While driving for long durations, drivers often feel drowsy and tend to fall asleep. This causes thousands of accidents each year. Working on this project, you can use the Python programming language to build a system to detect when a driver is feeling drowsy and alert them using an alarm.
- Fake News Detection Software
With the increasing use of Social Media and the internet, fake news spread like wildfire. You can develop a fake news detection project using Python programming language, to detect if news being spread is fake or real.
- Image Caption Generator
Computers see images differently than we do. To computer systems, images are merely a series of numbers, representing colour value in each pixel. Using python language, you can develop this project which generates a caption for images in English.
- Gender and Age Detection
This project can be developed using the Python programming language. Using a single image, you can predict the gender of the person and their age range.
For more suggestions on Data Science projects, read our blog Top 10 Data Science Project Ideas for Beginners in 2021
Justifying Analytical and Communication Skills
When it comes to building a resume for Data science professionals, or freshers seeking work in the industry, in addition to mentioning technical and mathematical skills, it is also very important to justify your analytical and communication capabilities. Leadership and communication skills can be highlighted by mentioning projects and experiences of working in a team setting, on collaborative projects.
Work Experience and Education
While mentioning work experience, briefly explain your role and responsibilities in each organisation and the period of time you worked there for. Visible gaps between job roles can sometimes leave a bad impression on employers, so always justify that gap, if any. After work experience, mention your education backgrounds and names of institutions.
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Selecting Font style and Arrangement
Once everything is mentioned pick a font style and organize every mentioned in a neat manner. It is best to mention projects, accomplishments, employment history, and certifications in a reverse chronological manner.
Spell Check and Proofreading
It is always very important to check for spelling and grammatical errors and have someone go through your resume to suggest changes or point out any errors. When you’re trying to justify your ability to work in a field like data science, which requires excellent analytical and communication skills, grammatical errors are a huge red flag and can reduce your chances of being hired.
To know more about Data Scientists, their skills, and resumes, watch
Customizing and updating your resume
Finally, always customize your resume for every job profile and company. Make sure to always mention skills that the employers are seeking, so they know what you have to offer. This shouldn’t be too difficult if you already have a strong resume, to begin with. You would only need to go through the job description before applying, look for the skills that the role requires, and make sure to highlight certifications, projects, and job experiences in that area.
Finally, do not forget to add only the things you actually have and to not exaggerate your abilities. While in a job interview, it is very important for you to be able to justify your resume. Remember, recruiters get thousands of applications and resumes, don’t give them any reasons to reject your application and pay attention to small details too.
To crack your interview, read Top 70+ Data Science Interview Questions and Answers for 2021
Data Science Resume Samples
Now that we have learned in detail about building a data science resume, let’s look at some data science resume examples.
Data Science resume for freshers
When you’re looking to start a career in data science, building a strong resume mentioning all your skills is very important. It is also crucial to make the resume look unique and neat. Given below is a good data science fresher resume.
Data Science resumes for experienced professionals
If you’re a data science professional with some experience in the field, mentioning relevant work experience and responsibilities at each job role is crucial. Here are a few data scientist resume samples you can consider.
Do’s and Don’ts of a Data Science Resume
Finally, now that you have learned everything you need to know to build yourself the perfect data scientist resume, one that makes your application stand out, let’s go over a few things you should do, and mistakes you should not make.
|Include updated contact information.||Don’t include date of birth, religious belief.|
|For each work experience, include a brief summary about your role and responsibility in the organisation.||Don’t use vague descriptions for jobs and projects. Mention exactly what you did, and what impact it had.|
|While writing educational qualification, always mention the name of the institute, and the time period you studied there for.||Don’t list technologies and programming languages that you’re not proficient in. Over exaggerated abilities might get you an interview, but not being able to justify them will get you rejected.|
|Include relevant project work.||Don’t make any grammatical errors.|
|Make your resume look unique.||Don’t make your resume too generic. Customize it as per job roles.|
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It may seem that building a strong resume is almost impossible, it is still very important for everyone to make their resume stand out from the crowd, in order to get a job. Now you know that it’s really not that hard. Just remember to follow everything you learned in this blog today, and you will build yourself the perfect resume.
If you have any more doubts or queries, post them in our Data Science Community