Introduction
In this blog, we are going to discuss data analysts, trends for data analysts, and why companies hire them.
The practice of extracting insights from data in order to make better business decisions is known as data analysis. The process of data analysis usually involves five iterative stages:
- Choose the data you want to examine.
- Collect the data
- Prepare the data for analysis by cleaning it.
- Analyze the data
- Interpret the findings of the investigation.
Depending on the subject you’re trying to answer, data analysis can take many various shapes. More information on the many methods of data analysis may be found here. In a nutshell, the descriptive analysis explains what happened, the diagnostic analysis explains why it happened, predictive analytics generates future projections, and prescriptive analysis generates actionable recommendations.
In order to answer a query or solve an issue, a data analyst collects, cleans, and evaluates data sets. Business, finance, criminal justice, science, medical, and government are just a few of the fields where they can operate. Data analysts must predict and foresee what will happen in the future, which can be very useful in company strategic decision-making. A data analyst must recognize the trends that have occurred over time in this scenario. They must also give precise recommendations based on the patterns one has discovered.
It is apparent that data analysts will always be in demand in today’s and future eras because businesses rely on them. We have less demand in India because it is still developing, but we will have a good demand and future in this industry in India. As a result, big data courses are in high demand these days.
Data Analyst Resume
Now, we will be deep-diving into the blog about the ideal resume for data analysts. A typical resume is divided into various parts. In this section, you will come across the best practices to write a job-winning resume.
Before diving deeply into each section of the resume, let’s look into some common useful tips:
- Make section heads bold and larger.
- Use a classy typeface for your resume.
- Use proper white space.
- Create a resume that is as long as you need it to be. It is acceptable to have a resume that is longer than one page. It’s not a good idea to skip over crucial aspects of your profession.
- Set the margins to a single inch on both sides.
- Use reverse-chronology order for your experiences.
- Consider using a single or 1.15 line spacing
Personal Information
It’s an important component to include your contact information as well as your social media activities on sites like Linkedin, Stackoverflow, Medium, and others. You can’t afford to make a mistake in this situation. The interviewer encounters multiple resumes with inaccurate email addresses or phone numbers, making it impossible to communicate the results to the candidates. As a result, you should always double-check this section before submitting your resumes. The following fields are required:
- Full Name
- Title – “Data Analyst”
- Phone Number
- Email Address
- Location (Optional)
If you use Github for your data science projects or to contribute to an open-source project, however, you should add a link to your profile. By doing so, you will be able to demonstrate your abilities, which will help your application stand out and boost your chances of being selected for future stages. The same can be said for other platforms such as Medium, Linkedin, and so on. As a result, it’s less vital to be inventive and more important to be precise.
Note: Do not provide your company’s email address. Avoid email addresses that don’t seem professional.
Summary
This section of the resume is at the top. It’s a brief summary of your entire resume, and it’s at this stage that recruiters and hiring managers decide whether or not to pursue the prospect further. In an easy-to-read format, you must summarise your years of experience, skills, and education. This section should be no more than 4-5 lines long. First, state how many years of experience you have as a data analyst and what role you played in past businesses (if applicable); second, state your key technical capabilities; and third, state any relevant certifications for a data analyst function. Recruiters use the information on this page to determine whether or not you are a suitable fit for their organization.
The main goal is to wow the hiring committee so that they are compelled to look at the best portion of your resume and learn more about you. Because recruiters are bombarded with hundreds of resumes in a single day, they don’t have time to go over each candidate’s entire résumé. They only spend a few seconds on each CV, so take this section seriously because it will determine whether or not you get the job.
Below is the example for good and bad summary for an ideal resume:
Are you composing a resume for a junior data analyst? This is how your resume summary should look:
Skills
This area highlights your technical and interpersonal abilities. The key is that you must tailor your application to the position you are looking for (here Data Analyst). Your skills should revolve around data visualization, data cleaning, Matlab, R, Python, Machine Learning, Linear Algebra, and Calculus, etc. Mention the skills that will persuade your recruiters that you have the majority of the technical skills and software knowledge required for the job. This is your chance to show off your understanding of programming languages, data science frameworks, libraries, and tools, among other things.
To make it easier to distinguish between technical and soft skills, keep them separate. It allows recruiters to quickly grasp your qualifications.
Don’t list more abilities than you have, and be specific. Only include those that are critical to the data analyst’s job. Most importantly, you should be confident in your abilities; for example, only include languages in which you are comfortable coding because the interviewer may ask you to code for a problem statement just to assess your coding abilities in the programming language you stated. Here is not the place to boast about your abilities. Simply identify your areas of competence, and if you have only a basic understanding of a particular skill and want to include it in your bucket list because it is important for the data analyst role, put (beginner).
Since Data Analysis is a highly technical job, make sure to include technical capabilities as well as a part of general skills. Here are some of the skills that hiring managers look for in a data analyst:
- XML, ETL, and Javascript frameworks are examples of programming.
- Statistical packages and methodologies
- Databases and querying languages based on SQL
- R and/or SAS languages
- Data warehousing and business intelligence platforms
- Database Design
- Data visualization and reporting techniques
- Data mining, cleaning, and munging
Read More about Data Analyst Skills
Education
You must list your schooling in reverse chronological order in this section of the resume. This part may differ for newcomers and seasoned professionals.
If you’re a newbie, you might include the following details:
College diplomas (Degree name, college name, GPA score)
The intermediate school (School name, percentage/GPA)
High school (School name, percentage/GPA)
If you have a lot of experience, you can avoid mentioning your schooling and only write about your bachelor’s and master’s degrees. The component about work experience will be the main focus.
This is how the education section should look like:
Project
You should expect the interviewer to ask you a variety of questions on your technical and soft skills in this section of your resume. Include any data science-related projects that demonstrate your capacity to work on corporate-level tasks. Include the following information for each project:
Title, Your Responsibility, Technology Used, Indicate the project’s quantitative outcome.
If you’re just getting started in data science and don’t have many options, consider academic assignments you’ve completed in this or related subjects.
It’s a good idea to keep your Github profile up to date with information about your projects.
Don’t brag about your duties and responsibilities because interviewers may ask about them in order to assess your leadership, team management, time management, and other skills.
Work Experience
This section is more important for those with greater experience. The following is the fundamental format for inputting your work experience:
- Position name
- Company Name
- Dates
- Responsibilities & Achievements
In chronological order, your most recent job should be listed first, followed by the job before that, and so on.
A few factors determine how far back in terms of experience you can travel. You shouldn’t go back more than five years in most cases.
While you don’t have to mention every detail of your experience, you should make sure that everything you do appears to be seamless. Gaps in your work experience section of more than six months are a significant red flag for recruiters and hiring managers. If you have a gap like this on your resume, you should definitely explain it.
Below are the snaps that show the example of the right and wrong experience section for senior data analysts and junior data analysts respectively:
For senior data analysts
For junior data analysts
Achievements
Now is your time to stand out from the crowd with your resume and other applications. You can provide information about your accomplishments in this part to demonstrate your expertise in the subject of data science. If you don’t have one, you can list any achievements related to programming competitions and technical exams you’ve taken. Something special should be included in this section. It will clearly demonstrate how valuable you could be to the firm.
Certifications
Now is your time to stand out from the crowd with your resume and other applications. You can provide information about your accomplishments in this part to demonstrate your expertise in the subject of data science. If you don’t have one, you can list any achievements related to programming competitions and technical exams you’ve taken. Something special should be included in this section. It will clearly demonstrate how valuable you could be to the firm.
Add a section for big data certifications, software, or licensing to the data analyst skills area.
List the journals and magazines where you’ve published your study and findings.
If the conference you attended focused on skills that match the data analyst job description, include a section for it.
If you wish to work as a data analyst, consider the following five certifications:
- Cloudera Certified Professional: Data Scientist
- EMC Data Science Associate
- Certification of Professional Achievement in Data Sciences – Columbia University
- Coursera Johns Hopkins Data Science Certification
- INFORMS Certified Analytics Professional
Interest/Hobbies
“What does it mean for a recruiter to know about your hobbies and interests?” you might wonder.
On the other hand, your hobbies reveal more about who you are as a person. Include a hobbies section on your resume if you have the room as an easy way to convey individuality.
Before including your hobbies and interests on your resume, think about what you’re trying to communicate to potential employers. By describing your hobbies, employers can get a feel of how you spend your time and what other abilities you have.
Interests, on the other hand, may indicate subjects you’re currently exploring or wish to research, which could make you a good fit for the company.
Conclusion
If you followed all of the preceding advice, you’ve given yourself the best possible opportunity of landing that data analyst position.
Let’s summarise everything we’ve learned thus far:
- For your data analyst resume, prioritize the reverse-chronological format, then adhere to the content style guidelines.
- Start your resume with a summary or objective to get the recruiter’s attention.
- Give your accomplishments more weight than your responsibilities.
- Don’t veer off the beaten route; be specific and provide material that is relevant to data analyst jobs.
- For a successful application, create an engaging résumé.
FAQs
How do you show data analysis skills on a resume?
Include technical capabilities that are relevant to data analytics, beginning with your strongest abilities. Examine the job description to evaluate if your skills are appropriate for the position. Use bullet points to keep your talents section neat and easy to read, as well as to draw attention to important skills.
Is data analyst a stressful job?
Working as a data analyst can be stressful, but it all relies on your employer, business culture, and your own capability of doing work. Being a data analyst can be a dream profession for some of us. However, this isn’t the case for everyone. This position necessitates self-sufficiency and the ability to work independently. Furthermore, you must devote a significant amount of time and effort to improving your technical, communication, and business skills. Yes, you read that correctly. Is it the type of position where you require both technical and business skills, as well as the ability to communicate effectively.
What is a data analyst’s salary?
A data analyst’s pay could range from Rs.342,363 to Rs.1,750,000 per year. The demand for a data analyst has improved since the amount of data has expanded greatly in comparison to past years. As a result, a data analyst pay for a newcomer in India could be an excellent place to start. It should be noted that a data analyst’s income is determined by a variety of criteria, including experience, skills, location, and employer.
Do I need a degree to be a data analyst?
No, a degree is not required to work as a Data Analyst, but you must possess the necessary hard and soft skills to be considered for a position in data analysis. Keep in mind that most entry-level Data Analyst positions have typically needed at least a bachelor’s degree.
