Data analysts are very much in demand in the job market right now. The traditional role of a data analyst involves finding helpful information from raw data sets. And one thing that a lot of prospective data analysts wonder about is how good they need to be at Math in order to succeed in this domain.

**While data analysts do need to be good with numbers and a foundational knowledge of Mathematics and Statistics can be helpful, much of data analysis involves following a set of logical steps. As such, people can succeed in this domain without much mathematical knowledge.**

There is, however, a lot more to this than just a simple answer. In this article, we will attempt to answer this question in detail. And for those looking to learn mathematics to excel as a data analyst, we have included a list of the most fundamental topics and the respective courses available online.

**Important Sidenote:** **We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! **– This is perhaps the most comprehensive article on the subject you will find on the internet!

Table of Contents

## How Essential Is a Good Mathematical Foundation for a Data Analyst?

A good mathematical foundation will definitely help you stand out as a data analyst, but it is far from the most important skill you will be needing. For instance, your understanding and knowledge of the domain you are working in and a general understanding of business and the business world are far more valuable skills to succeed as a data analyst.

It all comes down to one reason. A data analyst’s job is to search for valuable insights within raw data. These insights are then presented to the stakeholders of the business they’re working for so that the business operations can be optimized and the profits may be increased. As such, one of the most valuable skills of a data analyst is the ability to look at things from a business lens.

Much of the data processing, which is where most of the mathematics is applied, can be done through libraries or frameworks. So, to a typical data analyst, a keen sense of business and good communication skills are much more valuable than a deep knowledge of mathematics.

In the modern scenario, with the advent of powerful computers and software, data analysis depends more on logical tasks than it does on mathematics. The overall flow of the process would still pretty much be the same. You would be extracting/cleaning data, processing it, and then presenting the results as visualizations. Not a lot of mathematics is required in any of the steps.

The above scenario is true for the vast majority of the data analysis in the industry today. Sure, there are some big companies like Google and Amazon, which might require a much more rigorous (and mathematical) analysis, but they are the exceptions. This is because bigger companies often have to deal with a lot more variables than smaller companies. As such, their data requires much more meticulous analysis.

### Getting a Job at the Big Tech Company

So, as a response to the main question at hand, you will need to be pretty good at math if you want to work as a data analyst for the big tech companies. These companies often have an extremely competitive recruitment process, which is why every additional skill (and especially math skills) matters.

The logic behind this is that every bit of mathematical insight you come with could potentially lead you to make an improvement in the current algorithms. To a typical company, a slight improvement wouldn’t really make that big of a difference. But with a company like Google or Facebook, even the slightest improvement could magnify huge over the entire platform. This is how these companies maintain their edge over the competition.

For most businesses, however, it is much more important that you know the drill, the steps, and the domain you are involved in. A deep mathematical insight that could help you improve the data processing algorithm being used isn’t usually a priority.

### You Need to Be Good With Numbers Anyway

Having said all this, every data analyst, regardless of where they’re working, needs to be pretty good with numbers. As a data analyst, you will be dealing with a large amount of seemingly random data. It is your job (with the assistance of some algorithms) to find meaningful patterns within those numbers and figures.

So, good arithmetic and algebraic foundation are crucial for a data analyst.

## What Does a Data Analyst Need to Know?

We have established that it isn’t necessary for a data analyst to have an in-depth knowledge of math. But a crucial asset for any data analyst is knowing which part of math to use and when to use it. What this means is, you don’t have to be fluent at solving math problems, but you must have a good enough grasp of the theory to be able to anticipate the general solution.

To understand this better, you must understand the role of a computer in the job. Computers were invented to make calculations faster and most efficient. If you know the theory behind the solution of a genre of problem, you can use this to program a computer that can do the calculations many times faster than a typical human mind. So, knowing how to calculate fast on your own won’t really matter much in the real world.

Having said this, there are certain aspects of any problem-solving task that a computer can’t handle. This is where your foundational intuitions of math can come in handy. If you understand the theory behind the math being used, you can use this knowledge to design algorithms that the computer can then use to calculate the results faster.

### Knowing the Right Methods

Above everything else, a data analyst must be able to understand the dataset and the problem they are looking to solve. For this, he/she will need a sound knowledge of all the possible methods that can be used to find information within a dataset that can be used to solve a particular problem. Knowing the right methods will result in a faster and more correct analysis.

### Understanding the Input and the Result

Once the right method has been selected, the computer can handle the calculation. Now the computer will generate a certain result for a certain input. The data analyst must be able to understand what both the input and the result mean.

In fact, he/she must be able to look at these factors with a keen business eye. Only then will they be able to derive the most useful information from the analysis, thus fulfilling their job as a data analyst.

### Being Able to Communicate the Result Over to Your Boss

As a data analyst, your job isn’t restricted to just finding useful information within a raw dataset. The job is only half complete until you actually communicate your findings over to your superiors, who hold power and authority to execute any changes to the operation of your business.

Say you find helpful information about user preference from a data set that a business can implement in order to improve its profits. In this case, your job as a data analyst will involve communicating this finding over to your bosses in a language they understand and can work with. This involves removing much of the technical details and sharing the key insight in a way they can conceive.

This will involve knowing your way around visualizations (including graphical illustrations of the results/dataset). And you will need good communication skills to present these findings.

## The Four Essential Math Topics for a Data Analyst

While a deep knowledge of mathematics isn’t particularly necessary for a data analyst, they will need to have some knowledge in a few important domains. Having a good background in these topics will ensure an intuitive approach to data analysis, which could be crucial if you’re looking to land a job in a big company like Google or Amazon.

The four essential math topics for a data analyst include statistics & probability, algebra (basic & linear), calculus, and discrete mathematics. Let us now look at what each one of these four topics covers and how each is relevant to data analysis.

### Statistics and Probability

Solid knowledge of statistics and probability is a must for every data analyst. In fact, it wouldn’t be an overstatement to claim that statistics and probability are the two most important mathematical topics for a data analyst. And given that the job of a data analyst involves finding helpful patterns and pieces of information from raw data, it isn’t hard to see why these two topics are at the core of a data analyst’s skill sets.

Simply put, statistics and probability are the two branches of mathematics that are used for the analysis and display of datasets that may appear random but have useful insights and information hidden in between them.

The topics within statistics and probability are very vast. For an aspiring data analyst, the necessary topics include:

- Basic Probability
- Probability Calculus
- Bayes Theorem
- Conditional Probability
- Data summaries
- Variance
- Covariance
- Correlation
- Central Tendency
- Probability Distribution Functions (including Normal, Binominal, Uniform, Chi-Square, T-Distribution)
- Central Limit Theorem

Other topics include:

- Sampling
- Measurement
- Error Management
- Analysis of Variance (ANOVA)
- Linear Regression
- P-Values
- Hypothesis Testing
- Confidence Intervals
- A/B Testing
- Random Number Generation

If you need to start at the basics of statistics and probability, Khan Academy offers a great beginner’s course where you can learn most of the topics mentioned above. A solid intuitive understanding of statistics and probability is a must for every data analyst. So we suggest you take this course before you jump into more advanced courses.

If you’re comfortable with the basics, or if you’re looking for a fast track into data analysis, there are a couple of MOOC courses you could take. Coursera offers a course called “Statistics with R specialization” from Duke University. This would be a good choice since you will be learning R as well, which is a very important language for data analysts.

Another similar course is The University of California in San Diego’s “Statistics and Probability in Data Science using Python” that is available in edX. Here, you will be learning Python on the side, another very important language for data analysts.

### Algebra

#### Basic Algebra

As a data analyst, a basic algebraic foundation is just as crucial as knowledge of statistics and probability. We’re not talking about anything fancy here. Most folks will already be familiar with a lot of these concepts from high school.

We are talking about topics like:

- Basic Geometry
- Trigonometric identities
- Series
- Sequences
- Sums
- Inequalities
- Functions
- Graphs (Logarithmic, Exponential, Polynomial)
- Coordinate systems (Cartesian and Polar)
- Conic Sections

If it’s been a while since you last went through these topics, it might be a good idea to refresh your knowledge. Once again, Khan Academy comes to the rescue. It offers a free course that will refresh your knowledge on each of the topics mentioned above and more. You will be reviewing these topics while also gaining deep intuitions on them.

The “Data Science Math Skills” course by Duke University on Coursera is another great place to get a deep intuitive grasp on these topics.

#### Linear Algebra

Another side of algebra that every aspiring data analyst should be familiar with is linear algebra. Linear algebra is at the heart of all modern tech and engineering. Every time you get recommended a video on YouTube or you do something silly like swapping opposite gender using an App, there is a lot of linear algebra underneath all of those tasks.

For a data analyst, linear algebra is crucial for the visualization of data and the working of algorithms. The topics within linear algebra that every aspiring data analyst must be familiar with include:

- Matrices and Vectors and their basic properties
- Basic matrix/vector operations (like Scalar Multiplication, Transposition, Conjugation, the calculation of Determinant, etc.)
- Matrix Multiplication
- Inversion of a Matrix

Other topics include:

- Special Matrices (like Triangular, Identity, Unit, Hermitian, Unitary, etc.)
- Matrix factorization
- Vector properties (like Space, Linear Least Square, Orthogonality Orthonormality, etc.)
- Eigenvalues and Eigenvectors

Once again, our friends over at Khan Academy have a great course to familiarize you with the topics mentioned above. Coursera has a course called “Mathematics for Machine Learning: Linear Algebra” by Imperial College London, which also covers the topics mentioned above.

### Calculus

The third and final topic of the three math topics that are a must for data analysts is calculus. Whether you like it or not, calculus shows up everywhere, and data analysis is no exception. And although you won’t need an in-depth knowledge of this topic, being familiar with it will definitely give you an edge over your competition.

The topics you will need to be familiar with include:

- Single Variable Functions
- Limit and Continuity
- Differentiation
- Maxima & Minima
- Beta & Gamma Functions
- Mean Value Theorems
- L’Hospital’s Rule
- Multi-Variable Functions
- Partial Derivative
- Differential Equations (Ordinary and Partial)

Khan Academy has a great course on basic calculus. It covers everything an aspiring data analyst needs as part of his/her mathematical background. “Mathematics for Machine Learning: Multivariate Calculus” is another course offered by Imperial College London on Coursera.

### Discrete Mathematics

Discrete mathematics is a bit different from the continuous mathematics we are used to talking about. But when you’re doing mathematics using a computer, you will always be dealing with a discrete number. This is because there are only a finite number of bits that can represent a number or data.

So what exactly does discrete mathematics help you with? To put it short, it helps you understand the time and space complexity that needs to be considered when running a program on a computer. Thus, discrete mathematics becomes very important when it comes to designing algorithms.

Once again, a deep knowledge of this subject isn’t an absolute necessity for a typical data analyst. But like we discussed earlier, if you want an edge in the job market or possibly get recruited by one of the top companies right now, you will need to demonstrate an ability to improve upon the methods (like the algorithms) currently being employed. It is here that a good knowledge of discrete mathematics can come in handy.

Topics that fall within the domain of discrete mathematics include:

- Set Theory
- Combinatorics
- Countability
- Basic Proof Techniques
- Data Structures
- Graph Properties
- Growth of functions
- O(n) notation

This Coursera specialization titled “Introduction to Discrete Mathematics for Computer Science Specialization” has five courses spread across six months. It is plenty to give you an in-depth understanding of discrete mathematics.

## Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, **I wanted to share few top data science resources that I have personally vetted for you. **I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

**DataCamp:****If you are a beginner focused towards building the foundational skills in data science**, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal:**Click Here to Sign Up For DataCamp Today!**

**IBM Data Science Professional Certificate:****If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort:****Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today!**(To learn more: Check out my full review of this certificate program here)

**MITx MicroMasters Program in Data Science:**If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters.**Click Here To Enroll Into The MIT MicroMasters Program Today!**(To learn more: Check out my full review of the MIT MicroMasters program here)

**Roadmap To Becoming a Data Scientist:****If you have decided to become a data science professional but not fully sure how to get started**: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.

## Conclusion

Deep knowledge of mathematics isn’t the highest priority for a data analyst. There are more important skills than this, including a keen business eye and deep domain knowledge. But having a very good foundational knowledge of mathematics can be very helpful to excel as a data analyst. Big companies, where recruitments are a lot more competitive, look for special skills in the analysts they hire. Mathematics could therefore give you an edge.

For the most part, this job involves following a set of logical steps. You will need to be good with numbers in either case, but in-depth knowledge of mathematics may not be necessary.

**BEFORE YOU GO:** Don’t forget to check out my latest article – **6 Proven Steps To Becoming a Data Scientist [Complete Guide]**. **We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.**

**Affiliate Disclosure:** We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.