How To Become A Data Analyst With No Experience

Becoming a data analyst can be an extremely rewarding career but first you need to know how to become a data analyst. In this guide I’ve outlined the skills required to become data analyst and the entry level data analyst salary for those looking to learn more about the field. 

As a data analyst, you can expect to be well-compensated, with a median annual salary of $70,200 and compensation varying between $28.72 to $121.39 per hour. This is the case even if you are a junior data analyst with no prior professional experience. You will obviously need to gain in-depth knowledge of how to become senior or lead data analyst first before being considered for such positions and higher compensation. However, this will come naturally with time and commitment as you grow professionally in the field. 

Who is a Data Analyst?

Nowadays, companies receive a tremendous amount of information every day that can be used to optimize their strategies. To get insights from the massive data collected, they need a highly qualified professional: the Data Analyst.

The task of a Data Analyst is to process the varied data concerning the customers, the products, or the performances of the company, to release indicators useful for the decision-makers. Thus, the information provided by the data analyst enables companies to define the products to be offered to customers according to their needs, the marketing strategy to adopt, or the improvements to be made to the production process.

How To Become A Data Analyst With No Experience

If you plan to switch being a data analyst but bear no experience in the industry, you can probably start with a degree in an online course in data analysts. The course would make your foundation strong in the subject, also allowing you to build practical projects and learn and develop your skills. Moving on, you can get into an internship or pick up some freelance work to gain experience and add to your profile this way would stand out and have an edge when you start looking for a high profile job as a data analyst.

Step 1: Polish up on your math skills

If you are coming from a quantitative background, data science should be an easy transition. Before analyzing data with high-tech tools, you need to get to the foundation of data analysis, which starts with plotting data points on graphs along the X and Y axes and finding correlations and trends between different variables.

To make sure you can write efficient code and draw accurate conclusions, here are some recommended math concepts to master:

  • Statistical methods and probability theory
  • Probability distributions
  • Multivariable calculus
  • Linear algebra
  • Hypothesis testing
  • Statistical modeling and fitting
  • Data summaries and descriptive statistics
  • Regression analysis
  • Bayesian thinking and modeling
  • Markov chains

Step 2: Learn a programming language (or two!)

Compared to other career fields, data science is more about what you know and how well you can prove your relevant skills and less about the prestige of your alma mater. The skill-based interview process tends to level the playing field for people coming from different backgrounds.

Once you have a solid foundation with math, you can begin to pick up a few of the must-know programming languages for aspiring data scientists: SQL, R, Python, and SAS.

  • Python is a scripting language with libraries that enable you to wrangle, filter, and transform big data and unstructured data. Python has applications for web development, software development, deep learning, and machine learning. It is the most frequently used tool by data scientists.
  • R is an open-source programming language useful for complicated mathematical and statistical calculations. It also allows for data visualizations and has a large support community to help you get started.
  • SQL is a relationship management tool through which you can query for and join data across multiple tables and databases.
  • SAS is an expensive tool used by large corporations for statistical analysis, business intelligence, and predictive analytics, but it is not recommended for individuals because of the cost. If you learn the other languages, you can easily pick up SAS on the job.

You can practice basic programming in Springboard’s free data analysis course and then complement those skills with more advanced programs, like the data science bootcamp.

Step 3: Take on side projects or internships

To build your resume, companies will want to see professional practical experience. As you start building out your knowledge base, you can apply your skill-set in real-world settings and get real-time feedback.

You can use freelancing platforms like Upwork or Fiverr, as well as search for part-time work or internships through social media and job boards. Kaggle also offers competitions with monetary prizes.

Before interviewing, make sure to practice solving coding problems on LeetCode and explore potential data science interview questions.

Show examples of past work samples on Github, LinkedIn or a personal website to build a good portfolio and a strong online presence.

It can be hard to gain experience without experience, but by leveraging online communities and starting small, you can prove that you have what it takes to turn data science knowledge into measurable business outcomes.

Step 4: Start as a data analyst

Data scientists and data analysts are not one and the same, and they are both career fields exploding in popularity.

Data analysts manage data collection and identify dataset trends.

  • Data scientists not only interpret data but also apply skills in coding and mathematical modeling
  • Data analyst positions can be easier to break into as a first job and can be a great launchpad to a data science career

For those interested in starting in data analytics, Springboard’s mentor-driven data analytics bootcamp covers framing structured thinking, analyzing business problems, connecting data using SQL, visualizing data with Python, and communicating analyses.

Step 5: Work hard—and network harder

Getting to know other data scientists is the best way to learn more about different career opportunities and maybe even meet your future team members. You can also discover what kind of company you’d like to work for (size, industry, culture), what projects appeal to you, and how to prepare for the job application process.

skills required to become data analyst

A good Data Analyst should have the following skills:


If you are someone who does not like Mathematics and calculations then you should drop the idea of becoming a good Data Analyst. You should know about an amazing relation between mathematics and data analysis before you drop the idea of becoming a good Data Analyst: The mathematics which you studied in your school and college days were usually not related to  real-life problems and situations, there are great chances that you will start loving mathematics when you start dealing with the real situations.

On the other hand, few organizations hire someone who possesses both the skills. These were the real source of confusion. We do not want to get into this debate. Let the data guide you, DataScienceWeekly has published a list of average mathematical skills you should be possessing in order to become a good Data Analyst:

  • Linear algebra (and ideally basic multivariate calculus)
  • Regression  Linear regression and the things that violate the assumptions of linear models (e.g., autocorrelation in time series data, non-independent observations)
  • Probability theory especially Bayes’ Law and Central Limit Theorem
  • Numerical analysis (e.g., time series analysis and forecasting)
  • Core machine learning methods (clustering, decision trees, k-NN)


To become a good Data Analyst, you need to be an expert in at least one of the programming language. Though, it will be always a plus point if you are good in multiple programming languages that are commonly used in data analysis such as R, Python, C++, Java, MATLAB, PHP, etc.


Machine learning is technology based on artificial intelligence (AI) that provides systems the intelligence to automatically learn and improve with time and experience. They are not explicitly programmed. Machine learning provides a kind of algorithm that allows software applications to become more accurate in foretelling results.


If you are good at programming, mathematical, and software skills, but at the same time you do not have proper knowledge of statistics then you are lacking one of the unavoidable skill set that is required to become a Data Analyst.

If you are not good at statistics then you can give a kind of results to the stakeholders using which they may have to face a huge loss and related consequences. All good Data Analyst has a different set of skills and they work with different technologies, but one skill that keeps them under one roof is a deep understanding of statistics. So start learning statistics if you really want to work in the field of data analysis as a Data Analyst.


Before performing any sort of data analysis organizing data in a systematic way and performing calculations are two of the main tasks of Data Analysts, which indicates that you should be very good at using Excel. Excel itself is very powerful, all you need is to learn how to use Excel efficiently. You will get many online guides for using Excel to its full potential.

entry level data analyst salary

As of Jan 20, 2022, the average annual pay for an Entry Level Data Analyst in the United States is $43,250 a year.

Just in case you need a simple salary calculator, that works out to be approximately $20.79 an hour. This is the equivalent of $832/week or $3,604/month.

While ZipRecruiter is seeing annual salaries as high as $69,500 and as low as $25,500, the majority of Entry Level Data Analyst salaries currently range between $35,000 (25th percentile) to $50,000 (75th percentile) with top earners (90th percentile) making $55,500 annually across the United States. The average pay range for an Entry Level Data Analyst varies greatly (by as much as $15,000), which suggests there may be many opportunities for advancement and increased pay based on skill level, location and years of experience.

What are Top 10 Highest Paying Cities for Entry Level Data Analyst Jobs

We’ve identified 10 cities where the typical salary for an Entry Level Data Analyst job is above the national average. Topping the list is Sunnyvale, CA, with Santa Cruz, CA and Santa Rosa, CA close behind in the second and third positions. Santa Rosa, CA beats the national average by $6,752 (15.6%), and Sunnyvale, CA furthers that trend with another $8,382 (19.4%) above the $43,250 average.

Significantly, Sunnyvale, CA has a very active Entry Level Data Analyst job market as there are several companies currently hiring for this type of role.

With these 10 cities having average salaries higher than the national average, the opportunities for economic advancement by changing locations as an Entry Level Data Analyst appears to be exceedingly fruitful.

Finally, another factor to consider is the average salary for these top ten cities varies very little at 7% between Sunnyvale, CA and San Mateo, CA, reinforcing the limited potential for much wage advancement. The possibility of a lower cost of living may be the best factor to use when considering location and salary for an Entry Level Data Analyst role.

CityAnnual SalaryMonthly PayWeekly PayHourly Wage
Sunnyvale, CA$51,633$4,303$993$24.82
Santa Cruz, CA$51,155$4,263$984$24.59
Santa Rosa, CA$50,002$4,167$962$24.04
Livermore, CA$49,941$4,162$960$24.01
Williston, ND$48,994$4,083$942$23.55
Manhattan, NY$48,866$4,072$940$23.49
Barnstable Town, MA$48,486$4,041$932$23.31
Cambridge, MA$48,398$4,033$931$23.27
Palo Alto, CA$48,252$4,021$928$23.20
San Mateo, CA$48,027$4,002$924$23.09

how to become a junior data analyst

If you’re interested in becoming a Junior Data Analyst, one of the first things to consider is how much education you need. We’ve determined that 72.9% of Junior Data Analysts have a bachelor’s degree. In terms of higher education levels, we found that 14.0% of Junior Data Analysts have master’s degrees. Even though most Junior Data Analysts have a college degree, it’s possible to become one with only a high school degree or GED.

Choosing the right major is always an important step when researching how to become a Junior Data Analyst. When we researched the most common majors for a Junior Data Analyst, we found that they most commonly earn Bachelor’s Degree degrees or Master’s Degree degrees. Other degrees that we often see on Junior Data Analyst resumes include Associate Degree degrees or Diploma degrees.

You may find that experience in other jobs will help you become a Junior Data Analyst. In fact, many Junior Data Analyst jobs require experience in a role such as Internship. Meanwhile, many Junior Data Analysts also have previous career experience in roles such as Data Analyst or Research Assistant.

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