These questions aim to evaluate a candidate’s ability to analyze and interpret large datasets, create models, and communicate insights to non-technical stakeholders. Preparing for data science interview questions can help you demonstrate your skills and increase your chances of landing a data science job. This article will elaborate on how to prepare for the interview and the data science interview questions you need to know.

Preparing for a Data Science Interview

Preparing for a job interview can make you feel more confident and increase your chances of success. Here are some tips to help you prepare:

1. Research the company: Research the company you’re interviewing with. Look at their website, social media channels, and news articles to understand their products, services, culture, and mission.
2. Review the job description: Review the job description carefully and ensure you understand the role’s requirements and responsibilities. Consider how your skills and experience align with the job requirements.
3. Practice your responses: Prepare responses to common interview questions, like “why do you want this job?” and “tell me about yourself.” Practice your responses out loud to get used to answering questions clearly and concisely.
4. Dress appropriately: Dress professionally and ensure your clothing is clean and pressed.
5. Bring copies of your resume: Bring copies of your resume to the interview in case the interviewer wants to refer to it.
6. Arrive early: Plan to arrive at the interview location early so you can find the location, use the restroom, and compose yourself before the interview.
7. Be polite and professional: Greet the interviewer with a smile, a firm handshake, and good eye contact. Use polite language and be respectful throughout the interview.
8. Ask questions: Prepare a few questions about the company or the role of the interviewer. This will show your interest in the company and help you learn more about the role.
9. Follow up: After the interview, send the interviewer a thank-you email or note to express your appreciation for their time and restate your interest in the role.

Basic Data Science Interview Questions

Answering data science questions during a job interview requires a solid understanding of fundamental statistics, programming, and data analysis concepts. Below we will go through some basic data science interview questions that the recruiter will likely ask you.

What is data science?

Data science is an interdisciplinary field. It uses statistical, computational, and analytical methods in order to extract insights and knowledge from structured and unstructured data. It combines elements of statistics, computer science, mathematics, and domain expertise to extract meaningful insights from data.

Data scientists use various techniques such as data mining, machine learning, and data visualization to analyze data and develop predictive models that can be used to inform business decisions. The field of data science has become increasingly important in recent years as organizations generate vast amounts of data and seek to leverage it to improve decision-making and gain a competitive advantage.

What is the difference between data analytics and data science?

Data analytics and data science are related fields, but they have distinct differences in their focus and scope. Data analytics analyzes data sets to conclude the information they contain, often to answer specific business questions or improve organizational performance.

It involves statistical methods and software tools to extract insights from data and often focuses on descriptive and diagnostic analysis, such as identifying patterns, trends, and correlations in data sets.

On the other hand, data science is a broader field encompassing data analytics but also includes more advanced techniques like machine learning and artificial intelligence. It involves using mathematical and statistical methods, computer programming, and domain expertise to extract insights from data and create predictive models.

In summary, data analytics focuses on descriptive and diagnostic analysis. At the same time, data science involves exploratory and inferential analysis in addition to descriptive analysis.

What are linear regression and logistic regression?

Linear regression is a technique employed to model the relationship between a dependent variable and other independent ones by including a linear equation in the data. It is used to predict continuous numeric values, such as a house’s price, based on size, location, and other features.

Logistic regression is a statistical technique utilized to analyze the relationship between a dependent variable and one or more independent variables, where the dependent variable is binary or categorical. It is a kind of regression analysis used to model the probability of a certain outcome, such as whether a customer will make a purchase or not, based on their demographic information.

Define confusion matrix

A confusion matrix is a table that can be used to assess the performance of a classification model by comparing the actual values of a target variable with the predicted values generated by the model. By analyzing the confusion matrix, data scientists can identify the strengths and weaknesses of the classification model and fine-tune it to improve its performance. It is also known as an error matrix.

What is the difference between supervised and unsupervised learning?

Supervised learning involves utilizing labeled data to train a model to make predictions or classify new data. In this type of learning, the model is given inputs and their corresponding outputs or labels, and it learns to map inputs to outputs by adjusting its parameters based on the differences between predicted and actual outcomes.

On the other hand, unsupervised learning involves using unlabeled data to discover patterns or relationships in the data without explicit guidance or supervision. In this type of learning, the model is given input data and learns to identify commonalities and differences within the data by clustering or dimensionality reduction techniques.

Put simply, supervised learning requires labeled data, and unsupervised learning doesn’t require labeled data.

What are some sampling techniques?

Sampling techniques are methods used to select a subset of individuals or data points from a larger population for statistical analysis. Here are some standard sampling techniques:

1. Simple Random Sampling
2. Stratified Sampling
3. Cluster Sampling
4. Systematic Sampling
5. Convenience Sampling
6. Snowball Sampling
7. Multistage Sampling

What is selection bias?

Selection bias occurs when the selection of participants or data points for a study is not random or representative of the target population. It can lead to inaccurate or misleading conclusions and affect the generalizability of the study results.

To minimize selection bias, researchers need to use appropriate sampling techniques, ensure a random selection of participants, and reduce exclusions or attrition during the study. It is also essential to report the sample’s characteristics and evaluate the study results’ generalizability to the target population.

How do you make a decision tree?

A decision tree is a graphical illustration of a decision-making procedure that uses a tree-like model of decisions and their potential outcomes, including chance events and resource costs.

Here are the general steps to make a decision tree:

1. Define the problem
2. Identify the outcomes
3. Identify the factors
4. Develop the tree
5. Assign probabilities
6. Assign values
7. Evaluate the tree
8. Refine the tree

What is the difference between normalization and standardization?

Normalization and standardization are two common techniques used to preprocess data in machine learning. They differ from each other in many ways. Firstly, normalization scales the data to a range between 0 and 1, while standardization transforms the data into a mean of 0 and a standard deviation of 1.

Secondly, normalization is useful when the scale of the features varies widely, while standardization is useful when the features have different units of measurement or when we want to emphasize the differences between the data points. And lastly, normalization preserves the original shape of the data distribution, while standardization transforms the data distribution to have a mean of 0 and a standard deviation of 1.

What are the steps in an analytics project?

The steps in an analytics project can vary depending on the specific project, but here are the general steps involved:

1. Define the problem
2. Gather data
3. Data preparation
4. Data exploration
5. Data modeling
6. Model evaluation
7. Model deployment
8. Communicate results
9. Monitor and update
10. Continuous improvement

What is the difference between long and wide format data?

The terms “long format” and “wide format” describe data organization in data analysis. This format is ideal for analysis since it facilitates using common data manipulation tools such as filtering, sorting, and summarizing data.

On the other hand, wide-format data has one row per observation but multiple columns representing different variables. This format is useful when comparing a single variable across various categories.

What is survivorship bias?

Survivorship bias is a cognitive bias that happens when we focus only on the individuals or things that have “survived” a particular process or event while ignoring those that have not. This can lead to a skewed understanding of the overall picture because we only look at the success stories rather than the failures.

Conclusion

In conclusion, data science interview questions can be challenging, requiring candidates to deeply understand statistics, programming, and machine learning techniques.

While technical skills are important, it’s also crucial for candidates to be able to communicate their ideas clearly and effectively and to be able to demonstrate their ability to work collaboratively on complex projects. By preparing for these questions, candidates can increase their chances of success in data science interviews and advance their careers in this exciting and rapidly growing field.

Why Is Statistics Important for Data Science?

Statistics is an essential tool for data science as it provides the framework to analyze, interpret, and draw meaningful insights from data. Data scientists use statistical methods to summarize and describe large and complex datasets, identify patterns and relationships, make predictions and forecasts, and evaluate the effectiveness of their models. With statistical analysis, data scientists can better understand the behavior of the data and thus can make informed decisions based on their findings.

Additionally, statistical inference techniques enable data scientists to make generalizations about the population from which they collected the data, even when they only have a sample. Through a solid foundation in statistics, data scientists can make sense of the vast amount of data available to them and avoid flawed or misleading conclusions.

Therefore, statistics play a crucial role in enabling data scientists to unlock the full potential of data and leverage it to drive insights and informed decision-making in various industries.

Learning Statistics for Data Science

Now that we have discussed the importance of statistics for data science, it is time to discuss how to build a solid foundation in statistics. You need to understand several key concepts to understand the fundamentals of statistics for data science. Some of these critical concepts include:

• Probability
• Sampling
• Tendency and distribution of data
• Hypothesis testing
• Variations
• Regression

Keep reading to find out more about each of these concepts.

Probability

Regarding statistics for data science, probability is a fundamental concept that helps us understand uncertainty and make predictions based on available data. Probability means measuring the likelihood of an event occurring and is expressed as a value between 0 and 1. This concept allows us to quantify our confidence in our predictions based on the available data.

Probability theory is essential for developing statistical models, conducting hypothesis testing, and making informed decisions based on data. Understanding probability helps data scientists interpret the results of statistical analyses and communicate them effectively to stakeholders.

Sampling

In statistics, sampling is selecting a representative sample or subset of individuals or items from a larger population to make statistical inferences. Because it is often impractical or even impossible to examine an entire population, data scientists use sampling to draw conclusions about the population as a whole.

Sampling methods can be random or non-random, and we use different techniques depending on the research question, the population size, and the level of accuracy or precision required.

The goal of sampling is to obtain a sample representative of the population that accurately estimates the population parameters of interest, such as mean, variance, or proportion.

Tendency and distribution of data

Tendency and distribution are essential concepts in statistics that describe data’s central tendency and spread. Tendency refers to a data set’s typical value or center and is often measured using metrics such as mean, median, and mode. On the other hand, distribution describes how data is spread out or dispersed and can be represented using tools such as histograms, box plots, or probability distributions.

Understanding both tendency and distribution of data is crucial for making informed decisions and drawing accurate conclusions from data analysis. By examining the tendency and distribution of data, researchers and analysts can identify patterns, outliers, and other essential characteristics that can guide further investigation or action.

Hypothesis testing

Hypothesis testing is a statistical method used to determine whether an observed result is statistically significant or simply due to chance. It involves setting up two hypotheses, a null hypothesis (H0) and an alternative hypothesis (Ha), and testing the data to see which hypothesis is more likely to be true. The null hypothesis is typically the default assumption that no significant difference or relationship exists between the tested variables. In contrast, the alternative hypothesis proposes that there is a significant difference or relationship between said variables.

Hypothesis testing allows researchers to make data-driven decisions and draw conclusions based on statistical evidence rather than relying solely on intuition or anecdotal evidence.

Variations

Variations in statistics refer to the degree of dispersion or spread of data in a sample or population. A higher variation indicates a greater data spread, whereas a lower variation suggests a tighter clustering of values. Various factors can affect variations, including sample size, outliers, and data distribution. Measures of variation, such as range, variance, and standard deviation, provide insights into the diversity and distribution of data points.

Understanding variations in statistics is crucial in drawing accurate conclusions from data and making informed decisions based on empirical evidence.

Regression

Regression is a statistical method used to study the relation between a dependent variable and one or multiple independent variables. It is commonly used in many fields, including finance, social sciences, and engineering. The basic idea of regression is to find a mathematical equation that can describe the relationship between the variables. We then use the equation to make predictions about the dependent variable based on the values of the independent variables.

There are many types of regression, including linear regression, logistic regression, and polynomial regression. The choice of regression model depends on the type of data and the research question. Regression analysis is a powerful tool for understanding complex relationships in data and making predictions.

2 Statistics Books for Data Science

Learning statistics through a book is a great way to begin your journey into the world of data science. Plenty of excellent books cover the fundamental principles of statistics clearly and concisely, with plenty of examples and exercises to help you practice. It’s important to take your time, carefully review each concept, and ensure you fully understand each new idea before moving on to the next. With a good book and a little determination, you’ll be well on your way to becoming a skilled data scientist in no time!

Here are two book recommendations on statistics for data science if you don’t know where to start.

Think Stats

Think Stats by Allen B. Downey is a fantastic book for beginners with a background in Python programming. This book uses clear and concise explanations to cover important statistical concepts, including probability, hypothesis testing, correlation, and regression analysis. In addition, it focuses on practical examples and exercises which will allow you to apply what you have learned to real-world data sets.

Statistics in Plain English

Statistics in Plain English by Timothy C. Urdan is an excellent introductory book for those who want to understand statistics without getting bogged down in technical jargon, i.e., through plain English. Using simple language, the book covers a wide range of statistical concepts and methods, including probability, hypothesis testing, correlation, and regression analysis.

Conclusion

In conclusion, statistics play a crucial role in data science, providing the tools and techniques needed to extract meaningful insights from data. Data scientists must have a solid foundation in statistical concepts and methods to analyze and interpret data effectively. By applying statistical techniques to large and complex data sets, data scientists can identify patterns and trends that allow businesses to make informed decisions, drive scientific research, and ultimately contribute to innovation and progress in various fields.

If you are interested in pursuing a career that is as challenging as it is rewarding, look no further! Becoming a data scientist is an excellent career choice, so read on as we tackle all there is to know about this profession and, more specifically, how to get a data science job.

Becoming a Data Scientist

Once people decide to consider a future in data science, they typically have two questions:

• How to become a data scientist?
• What skills are needed to be a data scientist?

Although each person’s journey toward success is different, most, if not all, data scientists succeed by obtaining the necessary skills and knowledge in the field through formal education.

Education

Typically, the minimum level of education required for data science professionals is a bachelor’s degree in computer science, data science, or another related field. Still, we advise you to continue updating and upgrading your knowledge as well as skill set by furthering your education and pursuing a master’s degree in data science or another discipline related to it. After all, with a higher level of education, you also increase your chances for better career opportunities and an increase in salary over time.

Skills

To become a data scientist, you need to obtain a number of technical and non-technical skills that allow you to properly deal with your daily responsibilities and duties. Some of the technical skills needed for the job include:

• Knowledge of statistical analysis and computing
• Ability to process large amounts of data sets
• Data visualization
• Machine Learning and AI
• Deep Learning
• Mathematics
• R Programming
• Python coding

On the other hand, in order to have the required mentality and ability to work well within a team of data scientists and other data-related professionals, as well as learn to automate data entry, you need to obtain and develop the following soft skills:

• Communication
• Critical thinking
• Collaboration
• Business acumen
• Adaptability
• Problem-solving
• Data intuition
• Intellectual curiosity

How To Get a Data Science Job

Suppose you have completed your studies and obtained the skills needed for the job. What then? How should you find suitable opportunities and secure the desired job positions? Well, there are a few steps you must go through in order to find jobs you can apply for and present the best possible version of yourself to potential employers.

Build a portfolio

Firstly, it is essential to have a portfolio that documents your work experience and capabilities in the field of data science. Such a portfolio acts as a collection of your best work and demonstrates your skills in the data science field. You should include a combination of any relevant code you have written, writing samples that show your abilities to effectively communicate with others regarding data, and other appropriate documentation.

Create an outstanding resume

Although it is easy to create a resume and send that same one to each job you apply for, if you want to have a better chance of getting an interview, you must create customized resumes for each position. You can do so by highlighting all the relevant skills and job experience that mirror the job description provided.

To create an outstanding resume, you must include the following information:

• Name
• Email
• Phone number
• Portfolio
• LinkedIn or other relevant profiles
• List post-secondary degrees (Ensure to include the year of graduation)
• If applicable, mention certifications you have completed, online courses in data science, or other relevant subjects
• Previous job experience (Make sure to list everything in reverse chronological order)
• Additional information on the industry and roles you have worked in
• If applicable, add accomplishments like awards
• Mention all relevant skills (specifically ones the job description demands)

Network

The next step is to network and create valuable connections that will help you find and secure job opportunities.

You probably heard the saying, “Who you know is more important than what you know.” Well, that is the ideology behind networking, and it has proven to be a successful approach. Reports show that around 85% of jobs are found through networking, and about 79% of professionals believe that career success depends on the connections they have created with other professionals.

Nowadays, it is easier than ever to network since universities, in particular, provide sufficient opportunities for you to meet and create connections with other professionals, whether they be other students, professors, alumni, teaching assistants, or other professionals in the field. Attending networking events and conferences related to data science or simply speaking to your professors about the market of data science will provide you with better opportunities within the field.

Find a mentor

We all need help and guidance sometimes. So, an excellent tip for anyone joining the data science field is to find a mentor that can provide foundational social capital for you as you begin your career, help guide various project works, and offer professional advice. Look for a mentor, like a professor or another professional in the field of data science, who can leverage their expertise and experience to guide you. Such a professional can help you grow—professionally and personally—make better decisions, and gain insight into data science.

Prepare for your interviews

If all other steps go well, you will quickly find and apply for a job in the data science field. Once you secure an interview, you should be well prepared to wow the hiring manager and secure the job. Do extensive research on the company you are interviewing for as well as the role—employers love hiring people who are well-informed about their objectives and eager to join the team.

Then, review your portfolio and overall resume, paying close attention to all the skills and experiences listed in case you are questioned or tested about any of them. We would advise you to do mock interviews with your mentor or a friend so you feel comfortable answering questions related to the data science field. This way, you will feel better prepared, more confident, and less stressed about interviewing for the job that can decide your professional future.

Where To Look For Data Science Jobs

We already mentioned that one way to look for and find available data science job positions is through your networking links. However, there are other ways to hunt for a job, including using technology. The online world has made finding suitable employment in any domain reasonably easy. Various online websites have millions of job listings available for you to explore, including thousands of positions in data science. You can look through sites such as:

• Glassdoor
• Indeed
• Ziprecruiter

These sites have features that help you narrow your search according to the exact role you desire, the company you want to work for, the location, salary, and other factors that interest you as you look for data science jobs.

3 Entry-Level Data Science Jobs

Though we have repeatedly mentioned that whether it be to create a portfolio or an outstanding resume, you need relevant job experience in data science, you do not necessarily need to have worked prior to applying for a job in the field. So, how to get a data science job without experience? Well, begin small and apply for entry-level data science jobs that help you receive on-the-job training, which later turns into the experience you refer to when you apply for higher-level data science jobs. Below, we have listed three popular choices for entry-level employment in the data science field.

Junior data scientist

One of the top entry-level positions aspiring data scientists pursue is that of a junior data scientist. Such a role is relatively new to the data science field and is primarily concerned with collecting, analyzing, and presenting data that other teams within the organization use. Their responsibilities are similar to those of a senior data scientist; however, the main difference is that they typically work on less critical projects.

To succeed in this role, you must understand the fundamentals of statistics and have relevant computer skills, including the ability to work with various programming languages and databases. The exact skills needed for the role may depend on the company you work for.

Junior data engineer

The next role you could apply for is that of a junior data engineer. Such positions are typically available part-time and full-time, depending on whether you have recently graduated or are continuing your studies and can dedicate only some of your time to the role.

When working as a junior data engineer, you must thoroughly understand the technical side of data, specifically how it is gathered, stored within the software, and analyzed. Once again, the duties and responsibilities of this entry-level role are similar to those of senior positions. However,  such professionals are usually found working on smaller projects that don’t have much impact on the company.

Junior data analyst

Junior data analysts, like the previously mentioned roles, have obtained the basic skills and knowledge required for working in data science and data analysis; however, they are still learning how to apply themselves in a professional setting.

You will be required to manage and analyze big data using various tools under the guidance of more experienced professionals. You will work with other data scientists, analysts, and engineers as you help them with their projects by cleaning up data they can use.

Data Science Job Outlook and Salary

When comparing the amount of data created daily (roughly 2.5 quintillion bytes per day) with the small number of people working as data scientists (a little over 3000 data scientists in the United States), there is always a demand for such professionals in the labor market. In fact, various lists of the best jobs with guaranteed future growth and high-paying career options include data scientists in them.

According to the Bureau of Labor Statistics, employment of data scientists in the United States is projected to experience a 36% growth from 2021 to 2031. This percentage is much higher than the average for all other occupations, as around 13,500 new job openings are expected each year throughout the decade. Similarly, the salary associated with the profession is satisfactory too. Recent reports show that the average salary of data scientists in the United States is $102,906 annually. However, the exact figures depend on the role, company, location, experience, and other factors.

Conclusion

Many industries have already accepted that in order to be successful, they must make the most of the millions of bytes they gather daily. To use all these data sets, they need people like you—ready and willing to obtain the theoretical knowledge and skills required to make sense of data and transform it into valuable information. So, start your data science journey and take your first steps towards a career filled with success.

Working as a business intelligence analyst means you’ll use data and help businesses make better decisions and help them become more efficient in achieving their goals. In this article, you’ll learn what is a business intelligence analyst, including how to become one, the necessary skills, and how much they make.

What Is a Business Intelligence Analyst?

Business intelligence analysts, also known as BI analysts, can analyze data and recommend strategies for a company. They also use data to help businesses make well-informed decisions. BI analysts are proficient in computer programming languages, technologies, and systems. 

Their main goals are to increase productivity and improve workforce efficiency, market positioning, and customer experience with accurate insights.

Business intelligence analysts analyze large amounts of data by examining databases and then produce reports to generate business insights. As such, BI analysts must have a wide range of analytical and business skills that help them in data analysis.

Additionally, the information that business intelligence analysts provide is helpful for companies to determine market trends and stay on top of their competitors. Once they compile the necessary information, BI analysts share it with the adequate departments.

What Does a Business Intelligence Analyst Do?

A business intelligence analyst is responsible for many things. Most importantly, they are responsible for analyzing data to identify if there are gaps in any areas and how they can help to improve the company’s productivity.

Using data modeling and data analysis techniques, a business intelligence analyst determines market trends and data patterns, allowing managers and departments to make smart decisions.

Apart from examining data analysis and organizational databases, other daily activities of a business intelligence analyst include communicating and collaborating with stakeholders by giving presentations or writing reports to share the information gained from data.

However, the duties of a business intelligence analyst vary based on several factors, such as the company they work for or their industry. Typically, the primary responsibilities of a BI analyst include:

• Analyzing business processes
• Compiling and analyzing organizational data, such as financial, expenditure, employment, and revenue reports
• Reviewing and validating customer data
• Reviewing and analyzing competitor data
• Compiling data about problems and recommending solutions to increase the performance of systems and efficiency of processes
• Evaluating the efficacy of strategies
• Generating the performance of the company by doing a cost-benefit analysis on projects
• Developing procedures and strategies for the analysis and collection of data
• Identifying business strategies and processes to help improve the company’s efficiency

How Do You Become a Business Intelligence Analyst?

Becoming a business intelligence analyst is not easy, and there are a few steps to follow.

Step 1: Earn a bachelor’s degree

The first step in becoming a business intelligence analyst is to earn a bachelor’s degree. Common majors include computer science, data science, business administration, economics, statistics, and other related fields that give you insight into business processes.

No matter which field you choose, you should focus on courses related to statistics, data analysis, technology, database design, and data visualization, as these are beneficial for your role in the future.

Step 2: Complete an internship

Most bachelor’s degree programs offer internship programs in the field of business or other specific industries. Suppose you want to become a business intelligence analyst. In that case, you should consider completing an internship program because this can help you gain work experience, so you can qualify for any position you want.

For starters, an internship in the financial industry would be beneficial since it will equip you with the necessary information about the inner workings of a business. These internship experiences will give you a better understanding of the types of projects that a business intelligence analyst deals with daily.

Step 3: Obtain professional certifications

You should also consider getting certified if you want to highlight your skills in certain areas. Some employers require specific certifications to work as a business intelligence analyst.

For example, you can opt for the Certified Business Intelligence Professional (CBIP) certification, Microsoft’s: Data Analyst Associate certification, business administration certification, and computer programming certifications. You can also get certified in computer languages, such as SAS.

Step 4: Pursue an advanced degree

Many business intelligence analyst graduates want to pursue a master’s degree. The most common degree is a Master of Business Administration (MBA). Still, you could also get a master’s degree in information technology or a related industry in which you want to specialize.

It’s important to note that a master’s isn’t usually required if you have professional certifications and sufficient work experience, but it does open more career opportunities.

Step 5: Search for business intelligence analyst positions

After acquiring the necessary education, certifications, and experience, you are ready to start looking for job positions as a business intelligence analyst. It’s essential to review the job description of the place you’re interested in, as this can tell you more about the qualifications and skills that you need to apply.

How long does it take to become a business intelligence analyst?

Becoming a business intelligence analyst can take you a minimum of four years. Throughout this period, you must complete at least your bachelor’s degree and an internship to gain entry-level experience. However, depending on your specialization, it may take longer if you want to obtain a master’s degree, gain more work experience, and get professionally certified.

What skills do you need?

To be a successful business intelligence analyst, you must possess hard and soft skills. You must analyze and understand data and have excellent communication and problem-solving skills. Additionally, you’re expected to be detail-oriented and have good presentation and time-management skills.

Some other skills may include:

• Leadership abilities
• Ability to brainstorm and collaborate with team members
• Ability to work within a diverse workforce
• Critical thinking skills
• Proficient in ETL (extract, transform, load)
• Excel and SQL
• Database design
• Data mining and analytics
• Cloud computing

Business Intelligence Analyst Salary and Job Outlook

A job as a business intelligence analyst is one of the best roles in the STEM (Science, Technology, Engineering, and Mathematics) field. That said, this is a job that pays well.

According to PayScale, a business intelligence analyst’s annual salary ranges between $48,701 and $93,243, or $66,645 per year. However, the salary depends on the company’s size, location, specialization, or experience level.

Regarding job outlook, the job market for business intelligence analysts is estimated to increase by 14% from 2020 to 2030, with around 99,400 openings projected each year. More and more organizations are looking for ways to stand out from the competition. Since BI analysts can provide necessary insights, such organizations always have opportunities to get employed.

Conclusion

A business intelligence analyst is a significant factor in a company’s development. They use their skills to gather and analyze data, find patterns that could potentially improve a company’s practices, and share those findings with their team or clients. If you are into business and like to see growth and at the same time you are good with numbers, a career in this field may be for you. In addition, the job market is currently thriving, and the salary is always a bonus, so you know you’ll have plenty of career opportunities working as a business intelligence analyst.

To put all this into perspective, let’s see what data science jobs you can qualify for with such a degree and the average salaries for those positions.

What Is a Data Science Degree?

A data science degree is a program that equips students with the necessary knowledge to tackle data sets and derive meaningful information from them. This degree enables graduates to use the skills developed through its courses—such as computer programming, applied and pure mathematics, database systems, data security, and software development—to solve data-related problems, find unseen patterns, build predictive models, and make data-driven business decisions and recommendations.

Data Science Job Outlook

According to the U.S. Bureau of Labor Statistics (BLS), the future of data science jobs is bright. Its reports predict that the number of data scientist jobs is expected to grow 19% over the next two decades, almost three times faster than the average growth rate for other jobs. Furthermore, we can expect a 27.9% increase in demand for data science skills in the workplace in the next decade, so what you learn while pursuing a data science degree will come in handy in various workplaces.

Highest Paying Jobs in Data Science

More companies are starting to work closely with data; thus the field of data science keeps growing, and new roles dealing with specific parts of data science are added. If you are wondering which entry-level data science jobs you should consider seeking, below you can read about some common job titles in data science that are worth pursuing, especially considering the salaries they offer.

1. Database manager

Database managers are responsible for managing the storage and organization of a company’s information in a system. A big part of this job involves keeping up with current database design and application developments to ensure they can successfully build and protect database systems.

Salary

The average salary for a database manager in the United States is reported to be $75,550 per year. The lowest recorded salary for this profession is $45,497 per year, whereas the highest reaches $125,453 per year.

2. Data analyst

Data analysts are responsible for helping to develop and maintain collection systems and databases. They must work on gathering data, organizing, and processing it until it is understood by the professionals of other fields who have to work with the accumulated information.

Salary

The average salary for a data analyst in the United States is reported to be $69,517 per year. The lowest recorded salary for this profession is $45,597 per year, whereas the highest reaches $105,986 per year.

3. Data warehouse manager

Data warehouse managers lead the group of workers responsible for the design and maintenance of the data warehouse systems that enable and support business intelligence activities. These professionals also oversee the process of creating database architecture.

Salary

The average salary for a data warehouse manager in the United States is reported to be $128,373 per year. The lowest recorded salary for this profession is $91,004 per year, whereas the highest reaches $181,088 per year.

4. Database developer

Database developers are responsible for creating, directing, and testing computer database programs to ensure they are efficient and can safely process large amounts of information. They also help decide what database management system best suits the client’s needs.

Salary

The average salary in the United States is reported to be $91,748 per year. The lowest recorded salary for this profession is $67,173 per year, whereas the highest reaches $125,313 per year.

5. Business intelligence analyst

Business intelligence analysts use data to help businesses make safe and reliable business decisions by collecting, cleaning, analyzing, and interpreting data to find patterns that signal an area where companies can improve themselves.

Salary

The average salary for a business intelligence analyst in the United States is reported to be $85,690 per year. The lowest recorded salary for this profession is $60,937 per year, whereas the highest reaches $120,498 per year.

6. Database administrator

Database administrators are primarily responsible for classifying and storing the company’s data. Then they also work towards securing the databases, merging them if needed, and helping restore any information that is lost through cyberattacks and hacks or incidents.

Salary

The average salary for a database administrator in the United States is reported to be $83,700 per year. The lowest recorded salary for this profession is $59,733 per year, whereas the highest reaches $117,284 per year.

7. Statistician

Statisticians help analyze and interpret numerical data precisely, and this way, they work to do more informed planning within the company and aid in decision-making with the collected information. These professionals also apply statistical and analytical techniques to the data they work with to identify trends.

Salary

The average salary for a statistician in the United States is reported to be $88,989 per year. The lowest recorded salary for this profession is $60,589 per year, whereas the highest reaches $130,702 per year.

8. Business intelligence developer

Business intelligence developers, through reporting systems, help companies access valuable information. They also examine records, analyses, and data visualizations provided from warehouse data to aid in solving company problems and in decision-making.

Salary

The average salary for a business intelligence developer in the United States is reported to be $94,743 per year. The lowest recorded salary for this profession is $68,553 per year, whereas the highest reaches $130,937 per year.

9. Infrastructure engineer

Infrastructure engineers work with digital networks, databanks, and virtual platforms to help provide insight for various situations within the company. These professionals are also responsible for the maintenance of networks and documents and for generating repair plans in case of any malfunction.

Salary

The average salary for an infrastructure engineer in the United States is reported to be $101,089 per year. The lowest recorded salary for this profession is $66,335 per year, whereas the highest reaches $154,051 per year.

10. Data scientist

Data scientists are professionals responsible for implementing the insight collected from data to find relevant patterns. As a data scientist, your responsibilities would include applying the knowledge gathered from data to the company’s systems to help identify any vulnerabilities or repetitive models that could benefit the company.

Salary

The average salary for a data scientist in the United States is reported to be $117,212 per year. The lowest recorded salary for this profession is $82,339 per year, whereas the highest reaches $166,855 per year.

11. Data modeler

Data modelers are responsible for planning, applying, and maintaining data models that measure how an organization manages data flow in a database management system. They are also responsible for creating data modeling solutions, including the application of relational, dimensional, and NoSQL databases.

Salary

The average salary for a data modeler in the United States is reported to be $94,142 per year. The lowest recorded salary for this profession is $68,643 per year, whereas the highest reaches $129,113 per year.

12. Big data engineer

Big data engineers are responsible for multiple tasks, such as designing algorithms and predictive models, building datasets for data mining, and improving data quality, reliability, and efficiency. They have to sort through and work with unorganized sets of data from numerous sources and in various formats.

Salary

The average salary for a big data engineer in the United States is reported to be $104,463 per year. The lowest recorded salary for this profession is $75,078 per year, whereas the highest reaches $145,351 per year.

13. Data architect

Data architects are responsible for evaluating a company’s data sources and then creating plans for data management systems, finding ways to make the most meaningful information available for workers, and maintaining them. They also help present visually the data management framework and transition company requirements into databases and warehouses.

Salary

The average salary for a data architect in the United States is reported to be $118,868 per year. The lowest recorded salary for this profession is $83,368 per year, whereas the highest reaches $169,484 per year.

14. Enterprise architect

Enterprise architects are responsible for supervising, maintaining, and upgrading IT networks and services, the software, and hardware. They help businesses manage their current enterprise architecture, create visualization representations of data and other assets, and implement the overall architecture models to perform all business-related activities.

Salary

The average salary for an enterprise architect in the United States is reported to be $150,782 per year. The lowest recorded salary for this profession is $108,652 per year, whereas the highest reaches $209,250 per year.

15. Machine learning engineer

Machine learning engineers are responsible for designing software to automate predictive models. They link the information from data and artificial intelligence systems to produce programs capable of functioning without direct human assistance.

Salary

The average salary for a machine learning engineer in the United States is reported to be $131,001 per year. The lowest recorded salary for this profession is $87,952 per year, whereas the highest reaches $195,120 per year.

16. Machine learning scientist

Machine learning scientists work to help design and maintain machine learning models and algorithms that will better understand software problems and help provide solutions to those problems. They have to select appropriate data representation methods and identify the factors that affect the machine learning model’s performance.

Salary

The average salary for a machine learning scientist in the United States is reported to be $137,053 per year. The lowest recorded salary for this profession is $96,960 per year, whereas the highest reaches $193,725 per year.

Data science as a field keeps growing, and so do the jobs introduced above. Nowadays, due to the pandemic, you can also find remote data science jobs and earn the sums we mentioned from the comfort of your home. Of course, salary should never be the only factor you consider when choosing your career path. Yet, you must consider your worth as an employee and ensure that the value of your work is well compensated no matter where you end up.