skills required to become data analyst

Last Updated on July 30, 2023

Looking for the list of skills required to become data analyst? There are training sessions and courses available for the aspiring data analysts. The data analyst is responsible for researching organizations, policies, schemes and more. As per the current rule, there can be only one analyst in an organization as they need to analyze a large amount of data and use tools to generate reports. Along with this they also need to organize different kinds of analysis and develop charts.

Right here on College learners, you are privy to a litany of relevant information on how to become a data analyst with no experience, University of Findlay admission requirements, and so much more. Take out time to visit our catalog for more information on similar topics.

skills required to become data analyst

Data analysts collect, organise and interpret statistical information to make it useful for a range of businesses and organisations.

What does a data analyst do? Typical employers | Qualifications and training | Key skills

A data analyst is someone who scrutinises information using data analysis tools. The meaningful results they pull from the raw data help their employers or clients make important decisions by identifying various facts and trends. Typical duties include:

• using advanced computerised models to extract the data needed
• removing corrupted data
• performing initial analysis to assess the quality of the data
• performing further analysis to determine the meaning of the data
• performing final analysis to provide additional data screening
• preparing reports based on analysis and presenting to management

Typical employers of data analysts

• Banks
• Specialist software development companies
• Consultancies
• Telecommunications companies
• Public sector organisations
• Social media specialists
• Colleges and universities
• Pharmaceutical companies
• Manufacturers

Qualifications and training required

Both university graduates and school leavers can enter the data analysis profession.

For graduates, the usual entry point is a degree in statistics, mathematics or a related subject involving maths, such as economics or data science. Other degrees are also acceptable if they include informal training in statistics as part of the course, for instance sociology or informatics.

It is possible to enter this career without a degree. Data analyst apprenticeships are available with a range of employers. You will often need A levels (or equivalent) to apply.

Key skills for a data analyst

• A high level of mathematical ability
• Programming languages, such as SQL, Oracle and Python
• The ability to analyse, model and interpret data
• Problem-solving skills
• A methodical and logical approach
• The ability to plan work and meet deadlines
• Accuracy and attention to detail
• Interpersonal skills
• Teamworking skills
• Written and verbal communication skills

7 Must-Have Skills For Data Analysts

By Scott W. O’Connor  |  January 23, 2020 

The majority of companies today realize the value of a data-driven business strategy and are in need of talented individuals to provide insight into the constant stream of collected information. Research shows that nearly 70 percent of U.S. executives say they will prefer job candidates with data skills by 2021, and the demand for analysts will only grow as we continue to digitize our physical world.

If you’re just starting your research and are wondering how to make the transition to a career in data analytics, you’re not alone. Scanning job postings for data-driven positions is a great starting point, but many analyst roles are highly nuanced, making it difficult to discern which skills are the most necessary to invest in.

At Northeastern, our master’s in analytics program has been designed to provide students with the specialized combination of skills they need to not only thrive in their work, but to land a top position in the field of data analytics.

Some of these top skills for data analysts include:

• Structured Query Language (SQL)
• Microsoft Excel
• Critical Thinking
• R or Python-Statistical Programming
• Data Visualization
• Presentation Skills
• Machine Learning

11 Data Analyst Skills You Need to Get Hired in 2022

It’s no hyperbole to say that modern society runs on data. Humanity generates an incredible two and a half quintillion bytes of data (that’s 2,500,000,000,000,000,000 bytes) daily — and it seems unlikely that metric will decline anytime soon. According to a recent report from the International Data Corporation (IDC), the global Big Data and business analytics market has been expanding at a fast clip over the last several years, leaping from $122 billion in global revenue in 2015 to $189 billion in 2019 and driving towards a projected $274 billion for 2022.

With this rapid expansion comes a significant opportunity to develop your skills in data analytics, for example by enrolling in a data analytics boot camp geared towards those seeking to get into the field. Digital transformation has become the buzzword of modern business, and talented data analysts are needed now more than ever before. Career openings beckon from nearly every industry, from telecommunications to manufacturing, retail, banking, healthcare, and even fitness.

That said, the rewards of a career in data analytics won’t come without significant training and effort. Data analysts require specific skills to thrive in their field, and their qualifications are primarily tech-centric; however, those in the profession also need a handful of soft skills. There is no one way to go about gaining these skills. While many individuals opt into master’s programs, a growing cohort of learners has begun enrolling in boot camps, attracted by their reasonable price points and brief timelines. But regardless of the route you take, you will need to acquire a sturdy set of skills in order to become an in-demand data professional.

Below, we’ve listed the top 11 technical and soft skills required to become a data analyst:

1. Data Visualization
2. Data Cleaning
3. MATLAB
4. R
5. Python
6. SQL and NoSQL
7. Machine Learning
8. Linear Algebra and Calculus
9. Microsoft Excel
10. Critical Thinking
11. Communication

Looking to learn these skills and gain experience in a rapidly growing field? Discover more about Columbia Engineering Data Analytics Boot Camp. 

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Part 1: Technical Skills Required for Data Analysts

First, it’s essential to understand what a data analyst does. At risk of stating the obvious — all data analysts are concerned with, well, data. They use technical tools to parse through large quantities of raw information and develop meaningful insights in the process. Data analysts are also often responsible for removing corrupted data, determining data quality, and preparing reports for their employer.  

All of these tasks, as you might have already guessed, require data analysts to have a well-developed toolbox of technical skills. Here are a few to focus on. 

1. Data Visualization

As the term suggests, data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple: It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis. With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance. This capability empowers you — the data analyst — to gain a better understanding of a company’s situation, convey useful insights to team leaders, and even shape company decision-making for the better. 

Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”

Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” The number one skill reported by participants? You guessed it: data visualization.

2. Data Cleaning

As any Marie Kondo aficionado will tell you, cleaning is an invaluable part of achieving success — and data cleaning is no different! It’s one of the most critical steps in assembling a functional machine learning model and often comprises a significant chunk of any data analyst’s day. 

“Although we often think of data scientists as spending most of their time tinkering with ML algorithms and models, the reality is somewhat different,” tech writer Ajay Sarangam notes for Analytics Training. “Most data scientists spend around 80 percent of their time cleaning data. Why? Because of a simple truth in ML: Better data beats fancier algorithms.”

With a properly cleaned dataset, even simple algorithms can generate remarkable insights. On the flipside, uncleaned data can produce misleading patterns and lead a business towards mistaken conclusions. By necessity, data analyst qualifications require proper data cleaning skills — and there are no two ways around that. 

3. MATLAB

MATLAB is a programming language and multi-paradigm numerical computing environment that supports algorithm implementation, matrix manipulations, and data plotting, among other functions. Businesses interested in big data have begun turning to MATLAB because it allows analysts to drastically cut down on the time they usually spend pre-processing data and facilitates quick data cleaning, organization, and visualization. Most notably, MATLAB can execute any machine learning models built in its environment across multiple platforms. 

Understanding MATLAB isn’t a required skill for data analysts per se; however, given its wide-reaching applications and usefulness, having at least a working understanding of the environment can boost your marketability to employers.

4. R

R is one of the most pervasive and well-used languages in data analytics. One poll conducted by the Institute of Electrical and Electronics Engineers’s (IEEE) professional journal, Spectrum, found that R ranked fifth in a list of the top ten programming languages used in 2019. R’s syntax and structure were created to support analytical work; it encompasses several built-in, easy-to-use data organization commands by default. The programming language also appeals to businesses because it can handle complex or large quantities of data. 

Given its popularity and functionality, learning R should be high on the priority list for any aspiring data analyst.

5. Python

Learning Python, though, should be the top priority for would-be analysts. This high-level, general purpose programming language landed the number one spot in IEEE’s Spectrum 2019 survey, and for a good reason — it offers a remarkable number of specialized libraries, many of which pertain specifically to artificial intelligence (AI). 

Python’s applicability to AI development is particularly important. According to data published by Statista, the AI software market is on track to grow 154 percent year-over-year and achieve a projected height of $22.6 billion by the end of 2020. Understanding Python is a skill data analysts need to keep current in an increasingly AI-concerned professional landscape. Those interested in furthering their familiarity of Python should also look into its ancillary programs such as Pandas (an open-source data analysis tool that works in symbiosis with Python’s programming language) or NumPy, a package which assists Python users with scientific computing tasks.

6. SQL and NoSQL

If you want to break into data analytics, there are several database languages that you will need to be familiar with — if not fluent in — right off the bat. 

The first and foremost of these is Structured Query Language, better known by its acronym, SQL. SQL might have been created in 1970, but it remains invaluable to this day. In modern analytics, SQL persists as the standard means for querying and handling data in relational databases. 

This might seem counterintuitive, given that the analytics sector is in a near-constant state of growth and development. Data scientist Josh Devlin approaches this apparent lapse in an article for DataQuest, writing: “Why should someone who wants to get a job in data spend time learning this ‘ancient’ language? Why not spend all your time mastering Python/R, or focusing on ‘sexier’ data analyst skills, like Deep Learning, Scala, and Spark? While knowing the fundamentals of a more general-purpose language like Python or R is critical, ignoring SQL will make it much harder to get a job in data.” 

He has a point. The truth is, SQL has a foothold in companies everywhere. Its functionality and maintained effectiveness have kept demand high among companies, and its popularity shows no sign of abating soon. Learn SQL; if not for its function, then for your job prospects. Branded versions of SQL such as MySQL offer opportunities for gaining a greater understanding of relational database management systems. 

On the flipside, you also should focus on building your aptitude with NoSQL databases. As the name suggests, NoSQL systems don’t organize their data sets along SQL’s relational lines. By this definition, NoSQL frameworks can effectively structure their information in any way, provided the method isn’t relational. As such, it’s all but impossible to point to any one structure as the “standard” NoSQL framework. However, if you want to gain experience in NoSQL structures, it may be helpful to experiment with a framework like MongoDB, which organizes its database along flexible hierarchies instead of tabular relations.

7. Machine Learning

While machine learning isn’t a skill in the way data cleaning or learning a programming language might be, understanding it can help you become competitive in the data analytics hiring field. 

As mentioned earlier, Statista research indicates that artificial intelligence and predictive analytics comprise significant areas of investment right now. While not all analysts will find themselves working on machine learning projects, having a general understanding of related tools and concepts may give you an edge over competitors during your job search. 

8. Linear Algebra and Calculus

When it comes to data analytics, having advanced mathematical skills is non-negotiable. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice! 

Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives. 

However, you may find that you don’t need to build a robust theoretical background before pursuing real-world applications. Some in tech actually suggest taking the opposite track. For example, in the 2019 article “Mathematics for Data Science”, Towards Data Science writer and data analyst Ibrahim Sharaf El Den advised taking a top-down approach.

“Learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real-world projects, use library documentation and YouTube or Medium tutorials,” he explains. “You’ll start to see the bigger picture, notice your lack of theoretical background, to actually understand how those algorithms work […] studying math will make much more sense to you!”

That said, there is no one correct way to become a data scientist. Explore and find an educational route that works for you!

9. Microsoft Excel

Stressing the importance of Microsoft Excel skills almost seems laughable when one considers the significantly more advanced technology data analysts have at their disposal. To borrow a quote from Irish business writer Anne Walsh, “Mention Excel to techies, and it’s often dismissed with a sniff.” 

And it’s true — Excel is clunky in comparison to other platforms. Yet Microsoft’s workhorse spreadsheet platform is used by an estimated 750 million people worldwide. The term “Excel skills” frequently appears under the qualifications section for jobs posted on hiring services like Indeed or Monster. For all its apparent low-fi capabilities, Excel is well-used among businesses. 

Moreover, Excel, well, excels is automating certain features and commands for better data analysis. Excel has its own programming language, VBA, which it uses to create macros, or pre-recorded commands. When deployed correctly, VBA can save human analysts a lot of time on frequently-performed, repetitive projects such as accounting, payroll, or project management. Microsoft also developed its Analysis ToolPak with statistical modeling and data analysis in mind. As the company’s help center explains, “You provide the data and parameters for each analysis, and the tool uses the appropriate statistical or engineering macro functions to calculate and display the results in an output table. Some tools generate charts in addition to output tables.”

If you’re interested in learning more about the technical skills you need to further your career in data analytics, check out Columbia Engineering Data Analytics Boot Camp.

Part 2: Key Soft Skills Data Analysts Need

All of the above technical skills are required for data analysts — but technical talent alone won’t carry you to a successful career. You could be a stellar data analyst on paper and still never get hired. The reason is simple: Technical capability isn’t the be-all-end-all for aspiring data analysts. There are several softer, non-industry-specific skills data analysts require to succeed. There are too many to list in this piece easily, so we’ll focus on two essential skills: critical thinking and communication. 

10. Critical Thinking 

It’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights and information from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing. 

Becoming a critical thinker can be difficult, but you can hone such skills by challenging yourself. The next time you find yourself facing an analytical task or exercise, try to think — what is the meaning behind the pattern you see? What does the data say about what has been accomplished? What shortfalls does it indicate? Don’t overlook the importance of honing your critical thinking skills when you prepare for a career in data analytics. 

11. Communication

At the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short. 

Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst. 

Explore Your Options!

Data analytics could be the career for you, but to succeed in the field, you need to gain the requisite skills. Explore academic opportunities near you; parse through available undergraduate degrees and master’s programs. If you’re looking for a quicker and more financially-feasible solution, consider enrolling in a data analytics boot camp! These multi-week educational options offer learners an opportunity to gain a thorough background in the tech discipline of their choice at a reasonable price point. 

Explore your educational opportunities and begin developing a solid foundation of data analyst skills. A world of data analytics awaits!

how to become a data analyst with no experience

If you’re looking to become a data analyst but lack experience, then you’re in the right place. With the demand for data specialists growing, it’s never been a better time to start your career path in data analysis. 

Think of this blog as your straightforward career guide to becoming a data analyst. Besides reviewing what you need to do to enter this field, we also explain what a data analyst does and the soft skills you must have to ace your job. 

Follow the guidelines and tips given below to get closer to landing your first job as a data analyst. 

Is It Possible To Become a Data Analyst With No Experience?

‌The short answer is yes.

You can become a data analyst without any experience, provided you have the required skills for the job. Why do we say that? For one, the data market is growing at a significant rate. In fact, in the Jobs of Tomorrow Report, the World Economic Forum has listed data jobs among the professions with the highest growth rates.

‌Second, there is a skills gap in the market. According to an NTUC Learning Hub study, 93% of the employees reported that their workforce is not sufficiently productive due to a scarcity of data skills. People like you can step forward to fill this gap. 

‌Finally, some of the skills required in the field of data analytics are transferable from other areas of work. Sought-after skills include attention to detail, research capabilities, collaboration, and data visualization. 

How To Become a Data Analyst With No Experience

Your first step is to develop the necessary skills. After that, you need to determine the trajectory you want your career to take. Finally, start marketing yourself as a data analyst, network, and climb up the ranks of data analytics. 

Here are the steps you need to take to become a data analyst:

Determine Your Ideal Career Path

If you prefer working from home, you’ll have great luck working as a freelance data analyst. Once you have a substantial clientele, you can work as a consultant at organizations. 

‌Alternatively, you can start as a junior data analyst and transition to data scientist after advancing your programming skills or getting a degree in data science. 

‌Another route for junior data analysts is to advance their skills and experience to become senior data analysts. From there, if you develop sufficient leadership abilities, you can eventually become a manager or Chief Technology Officer. 

‌Some companies require employees in managerial positions to have a Master’s degree focusing on data analytics. So, if you plan on steering your career path towards management, consider getting a relevant higher degree along the way. 

‌If you‌ want your career path to be specialization-inclined, choose a specific department. For example, as an entry-level data analyst, you can advance to marketing analyst if you hone your marketing skills. 

‌Likewise, if you are more interested in tech solutions, you can climb up to the rank of marketing or operations analyst after gaining adequate experience. Basically, you need to shape your career path according to your financial goals and personal interests.  

Take a Course or Get a Certificate

Once you have decided the career path you wish to take, sign up for a data analytics course or certification. Look for an expansive one so that you can apply for multiple job titles in the niche. 

‌A relevant course in data analytics offers the following benefits: 

• Mentorship: The best data analytics courses have one-on-one guidance from a mentor. Besides being crucial in motivating young or career-changing professionals, it also helps newcomers get feedback on their work.
• Skill Building: A well-curated data analytics course will also hone your existing skills and help you develop new ones to excel in the field. 
• Career Coaching: If you aim to get an entry-level data analyst job, you should have career advice from industry experts. Most top data analytics courses come with a job guarantee and provide career coaching to get you started in the field. 
• Hands-on Curriculum: You can read every book about data analytics, but it still won’t prepare you for the real world. On the other hand, a well-structured course will educate you about data analytics concepts with curated resources and practical exercises. You can then use these projects to build your portfolio. 

‌The Springboard Data Analytics Bootcamp is an excellent option in this regard since it gives you unlimited mentor support, hands-on experience, and career support from industry experts.

‌Since the course also requires you to create two capstone projects with realistic data analytics scenarios, you will have something to put on your resume when applying for a job. In this way, even if you are not experienced, you have something to show for your skill.‌

Build a Portfolio

A portfolio will help you land a job since it establishes credibility and shows the projects you have previously worked on. Your portfolio should typically highlight the following things:

• Your technical skills
• Your creativity towards research
• Data analysts ability
• Ability to draw insights
• Team working and communication skills

‌Read this extensive guide to learn how to build a portfolio for data analysis.

Refine the Skills Needed for Your Target Job

By this point, you should have a foundational knowledge of the field along with some preliminary skills. Now, you need to align your subsequent steps with your target job. Identify your existing soft skills and determine which of those need more work. 

For instance, you may have excellent research skills but lack the expertise of giving presentations. Bear in mind that senior analysts have to present their findings to the executives and stakeholders in an organization. 

So, if you want to follow your pre-established career path, you need to work on acquiring these skills or honing the ones you believe are required for the next job title in your career path. 

Consider Related Jobs Where You Can Transfer into Data Analytics

You may need to work a job related to data analytics to build up your skills. For example, you could begin working as a data entry operator. In this role, your core responsibility would be logging data receipts and transferring information from paper format to computer formats. As you develop the necessary analytical skills, you can advance to a junior data analyst position. 

‌Spend time identifying your soft and core skills. Think about how you can transfer them into data analytics. Consider: do you need to acquire new skills? 

‌Perhaps you have considerable marketing experience and know the basics of analytics. These skills can be transferred into a data analysis role, such as a marketing data analyst.

‌Yet, you’ll need to acquire new skills like learning Python and other data analytics tools that will eventually land you a job in this field. The trick is to know your value and showcase it to employers through your resume and portfolio. 

‌For example, as a data capture specialist, you will have to make the data capturing process more efficient or upgrade databases to process queries faster. With some analytical and programming skills, you can turn your career towards entry-level data analytics. 

‌Other related jobs include a data entry clerk, information processor, typist, configuration officer, and junior computer operator. 

5 Entry Level Data Analyst Jobs You Can Get Without Experience

Many high-level data analyst jobs require you to have a certain degree of experience. There are many data analytics jobs that you can get without experience, including:

• Entry Level Data Analyst 
• Data Entry 
• Research Analyst 
• Data Associate 
• Entry Level Business Analysts

1. Entry Level Data Analyst

An entry-level data analyst collects, analyzes, and manages data. You would have to perform research involving industry data and define market trends based on it. Additionally, you will work with a team of statisticians and expert analysts, creating reports and presenting them to managers, stakeholders, and executives. 

2. Data Entry

As a data entry clerk, you will be in an administrative position, handling routine clerical tasks, mainly surrounding data entry into a computer system. You have to make sure the company’s database is up-to-date accurately. 

Some responsibilities of a data entry specialist are: 

• Reviewing records to ensure accuracy 
• Inputting data into databases 
• Updating the database with revised or new information 
• Backing up the database regularly to preserve information 
• Preparing files and digital material to be printed 
• Retrieving electronic files and records required by other members of the organization 

3. Entry Level Business Analyst

A junior or entry-level business analyst supports data analysis for a business and helps provide insights for better solutions and operational improvements. You will work as part of a team with senior analysts. Your tasks could be anywhere from assisting to performing industry research. 

4. Research Analyst

Research analysts examine the accuracy of the collected data to make sure it provides meaningful insight into trends. They use mathematical, analytical, and statistical models to explore data-based patterns revealing lucrative business opportunities that can improve performance. 

5. Data Associate

A data associate tracks data in research-based studies provide guidance about compelling data visualizations, reports issues with software and tools, develops dashboards, and curates templates and searchable Excel sheets using in-house tools. 

The responsibilities differ across industries, but data associates are generally responsible for collecting data, managing it, and inputting it into the company’s software. 

Data associates commonly work in the healthcare sector, where some prerequisite skills include Microsoft Excel, Oracle Clinical, and the organization’s data visualization tools. A clinical data associate has the following roles: 

• Coordinating with research staff to collect accurate clinical data 
• Supervising outsourced and internal clinical data management processes 
• Validating and implementing reporting methods 
• Designing clinical data collection methods
• Training clinical staff that uses technical software 

How Can You Increase Your Odds of Getting Hired as a Data Analyst With No Experience?

Finding a data analysis job without experience is not impossible. However, you will be facing a considerable amount of competition. If you’re wondering how to get an entry-level data analyst job without experience, these tips will help.

Focus on Must-Have Skills

As a data analyst, you need specific core and soft skills. You must have a knack for problem-solving, combined with excellent mathematical skills and knowledge of programming languages. Additionally, you should know how to use data visualization tools to create comprehensive graphs and charts for presentations.

• Problem-Solving: The whole crux of data analysis is to use data to solve a problem. So, you should have excellent problem-solving skills that you can put to work in your job.
• Math Skills: Data is all about numbers and figures, so it makes sense why you need advanced math skills. First, make sure your basics are in order. For instance, you should know how to work with complex divisions, fractions, and decimals. Knowing multivariate calculus is a plus, too. If possible, develop statistical skills, such as regression analysis and correlations.
• Programming Languages: Data analysts often use computer models and programs to analyze and extract data. While SQL is of utmost importance, you should also learn other programming languages, such as JavaScript and Python.
• Communication: When you are in an entry-level role, you have to work in a team, often consisting of senior analysts and experts from other departments. Work on your communication skills to ensure you can communicate detailed information to the rest of the organization in simple and easy-to-understand words.
• Data Visualization: As a data analyst, you might understand the jargon, but your peers working in marketing may not. Thus, you need to develop visuals such as charts and graphs to explain the data clearly. For example, Tableau is a tool data analysts use to analyze data and create visualizations for their team members.

Find a Mentor

When you are new to a field, having a mentor can be very helpful to progress steadily. A mentor has already gone through everything that is coming your way, so they can prepare you for what is to come.‌‌ Moreover, they can guide you through the job requirements, teach you new skills, and make you aware of possible mistakes.

‌If you do not have a friend or family member who is a data analyst, there is no need to worry. The Springboard Data Analytics Bootcamp comes with dedicated mentor support, allowing you to learn from the very best in the field and get your queries resolved.

Create Your Profile for the Job You Want to Have

Having a generic resume is good if you want to apply to several jobs. However, if you have a specific job title in mind, curate your resume accordingly.

‌‌For instance, take a look at this data analyst job listing by Amazon. The listing clearly states that the applicant must have hands-on ETL/SQL experience and should be highly proficient in Microsoft Office.

‌‌It also mentions that R code, web development, and Python knowledge are a plus. If you want to get this job, make sure your profile fulfills the exact requirements a company is looking for.

‌Instead of mentioning you know programming languages, specifically talk about R and Python. Doing so will increase the chance of you getting or at least being shortlisted for the job.

The Role of a Data Analyst

A data analyst uses data to answer questions, solve problems, and provide ways for improvement. In short, the job involves studying trends and basing predictions and future strategies on current data.

‌For this, data analysts use computer software and statistical models since the information must be regulated, calibrated, and normalized to ensure accuracy and integrity.

‌After deciphering the information, data analysts also have to report and communicate it to the executives and stakeholders in an organization.

Some of their responsibilities include:

• Analyzing data using mathematical and statistical tools
• Mining data from different sources, such as market trends and social media
• Cleaning, calibrating, and optimizing data to remove irrelevant information
• Providing data-based reports
• Identifying opportunities for progress and improvement
• Identifying current patterns and trends in the data

What Is It like Working as a Data Analyst?

A data analysts’ typical workday involves digging through data, converting it into a usable form, analyzing it, and presenting it to the company. As a junior data analyst, you will be collaborating with your team and reporting to your manager.

‌Senior data analysts are responsible for supervising their teams and may take on specific projects.

Final Words

‌With that, we conclude our guide on how to become a data analyst. Here is a quick recap of everything we discussed: start by determining a career path for yourself, take a course, hone your skills, network, and make sure you have a stellar portfolio. 

Your focus should be on sticking to your career path and aligning every step with it, whether it is getting another degree or developing advanced skills. 

Enroll in the Springboard Data Analytics Bootcamp to learn data analytics skills, receive mentorship from industry experts, and be a part of the community. Finally, start networking as soon as you have found your desired career path by joining LinkedIn data analytics groups and attending local meetups.

data analyst salary

How Much Do Data Analysts Make? 2022 Salary Guide

Written by Coursera • Updated on Dec 23, 2021Share

Learn how much you can expect to make as a data analyst, with tips to boost your salary.

Data analysts use mathematical and analytical methods to transform data into better data-driven business decisions. As the amount of data available to businesses increases, so too does the demand for skilled data analysts to process and interpret it. Data analysts are typically paid well for their skills.

In this article, you’ll learn how much data analysts earn on average, as well as how various factors, like experience, industry, location, and job title, can impact your data analyst salary. If you’re interested in starting or advancing your career as a data analyst, we’ll also talk about some ways you may be able to boost your earning potential.

What is an average data analyst salary?

The average base pay for a data analyst in the United States in December 2021 is $69,517, according to job listing site Glassdoor. The US Bureau of Labor Statistics reports a median annual salary of $86,200], while human resources consulting firm Robert Half lists a midpoint salary for a data analyst at $106,500.

While this range varies, each of these salary figures is significantly higher than the mean annual salary across all occupations in the United States, $56,310].

Several factors can influence how much your salary will be as a data analyst. Let’s take a closer look at a few of these considerations.

Data analyst salaries by experience

One of the biggest factors that can influence your salary is your level of experience. In general, the more years you spend working as a data analyst, the more you can expect to earn. Here’s how experience can impact your data analyst salary, according to Glassdoor [1]:

• 4 to 6 years: $72,469
• 7 to 9 years: $76,050
• 10 to 14 years: $78,360
• 15+ years: $79,309

Moving into a leadership role can further boost your earning potential. Glassdoor reports that analytics managers earn an average salary of $121,232 in the US, while directors of analytics earn $147,147.

Data analyst salaries by industry

Just about every industry can use data analytics to drive better business decisions. But the industry you choose to work in can have an impact on your pay. The industries where demand for data professionals is highest tend to be the same industries that pay the most on average. 

Finance and insurance, professional, scientific, and technical services, information technology, management, and manufacturing represent more than three quarters of data job openings, according to The Quant Crunch, an IBM report on the demand for data science skills.

Data analyst salaries by location

Where you live can also have a big impact on how much you can make as a data analyst. Typically, working in a big city like San Francisco, New York, Boston, or Washington, DC correlates to a higher salary (as well as a higher cost of living). As more and more companies employ a geographically dispersed workforce (including remote workers), it’s common for companies to offer location-based salaries—salaries that take into account location rather than merit alone.

According to Robert Half, these are the midpoint salaries for data analysts in the following large US cities:

• San Francisco: $151,230
• New York: $149,633
• Boston: $142,710
• Washington, DC: $141,645
• Chicago: $132,060
• Phoenix: $125,670

Salaries for other data professionals

Getting a job as a data analyst might be the first step in your data career. As you gain experience and new data science skills, you might move into a more advanced or specialized position. Here are a few of them, along with their average US salaries according to Glassdoor. 

• Business analyst: $77,218
• Database administrator: $83,700
• Business intelligence analyst: $85,690
• Statistician: $88,989
• Data engineer: $112,493
• Data scientist: $117,212
• Data architect: $118,868
• Analytics manager: $121,232
• Machine learning engineer: $131,001