Big Data Analyst

A Big Data Analyst analyzes large and complex datasets, applies statistical and machine learning techniques, and uncovers insights to drive data-driven decision-making and business growth.
Salary Insights
High-ROI Certifications
Potential Lateral Jobs
Publications/ Groups

Big Data Analyst

Payscale
Market
National (USA)
Base Salary
$65,957 / year
Satisfaction
Additional Benefits
Yes
Industry
All
Education
Bachelor's Degree

The average salary for Big Data Analyst is $65,957 / year according to Payscale.com

There are no updated reports for Big Data Analyst salaries. You can check potential lateral job opportunities in this information stack to find related salary information.

Big Data Analyst role may have an alternate title depending on the company. To find more information, you can check Payscale.com.

Career Information

As a Big Data Analyst, you will be responsible for analyzing large and complex datasets to extract insights and inform decision-making. You will need strong analytical and statistical skills, as well as proficiency in tools such as Hadoop or Spark. Strong problem-solving and communication skills are also important, as you will be responsible for translating data findings into actionable recommendations.

The average salary for Big Data Analyst is $65,957 / year according to Payscale.com
AI Disclaimer
The following text about the Job role of Big Data Analyst has been generated by an AI model developed by Cohere. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.

The Big Data Analyst is a crucial role in any organization that deals with large amounts of data. This role involves collecting, organizing, and analyzing data to help businesses make informed decisions and improve their operations.

The Big Data Analyst is responsible for collecting and organizing data from a variety of sources, including databases, social media, and other online sources. They must also be able to analyze and interpret this data to identify trends and patterns that can be used to improve business operations.

In addition to collecting and analyzing data, the Big Data Analyst must also be able to communicate their findings to stakeholders in a clear and concise manner. This may involve creating reports, presenting findings in meetings, or working with other teams to implement changes based on the data.

The Big Data Analyst must have a strong understanding of statistics and data analysis, as well as experience working with large data sets. They must also be able to work effectively with others and be able to communicate complex ideas in a simple and easy-to-understand manner.

Here are some of the most important skills and tasks for a Big Data Analyst:

  • Collecting and organizing data from a variety of sources
  • Analyzing and interpreting data to identify trends and patterns
  • Communicating findings to stakeholders in a clear and concise manner
  • Creating reports and presenting findings in meetings
  • Working with other teams to implement changes based on the data
  • Having a strong understanding of statistics and data analysis
  • Experience working with large data sets
  • Ability to work effectively with others
  • Ability to communicate complex ideas in a simple and easy-to-understand manner

If you are interested in becoming a Big Data Analyst, it is important to have a strong foundation in statistics and data analysis, as well as experience working with large data sets. You must also be able to communicate effectively with others and be able to work well in a team environment.

It is also important to have experience with a variety of data sources, including databases, social media, and other online sources. This will allow you to collect and organize data more effectively, as well as analyze and interpret it to identify trends and patterns.

Overall, the Big Data Analyst is a crucial role in any organization that deals with large amounts of data. This role involves collecting, organizing, and analyzing data to help businesses make informed decisions and improve their operations.

Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.

High-ROI Programs

Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Certification Programs
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.

Databricks Certified Data Engineer Associate

The Databricks Data Engineer Certification program is highly sought after, designed to evaluate an individual's proficiency in utilizing the Databricks Lakehouse Platform for performing fundamental data engineering tasks.

Intermediate
View More

Microsoft Certified: Azure Data Engineer Associate

DP-203

The Microsoft Certified: Azure Data Engineer Associate program is designed for professionals who have expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems.

Intermediate
View More

Google Certified Professional Data Engineer

The Google Certified Professional Data Engineer is a high-ROI program designed for experienced professionals to become highly equipped with specialty skills and functional knowledge in cloud data engineering.

Advanced
View More
Specialization Programs improving
If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.

Statistics for Data Analysis

Nanodegree

Mathematics for Machine Learning and Data Science Specialization

Specialization

Introduction to Data Science

Specialization

IBM Data Analyst Professional Certificate

Professional

Learn Intermediate Python

Nanodegree

Data Science with Databricks for Data Analysts Specialization

Specialization

How to Become a Business Analyst

Nanodegree

Data Analyst with R

Career Track

Advanced Statistics for Data Science Specialization

Specialization

This specialization equips you with essential concepts in probability, statistics, data analysis, matrix algebra, and linear models for Data Science.

Advanced Linear Models for Data Science 2: Statistical Linear Models

Course

This course delves deeper into expected values, multivariate normal distribution, distributional results, and residuals, enhancing your data science models with a strong foundation in R and college-level statistics and math.

Advanced Linear Models for Data Science 1: Least Squares

Course

This course provides a solid foundation in linear algebraic treatment of regression modeling, equipping learners with sound data science skills in linear regression, R programming, and linear algebra.

Data Science: Statistics and Machine Learning Specialization

Specialization

This comprehensive course equips you with essential Statistical and Machine Learning skills for Data Science, including R Programming, Regression Analysis, Data Visualization, and Random Forest.

Intermediate
View More
View More

Statistical Modeling for Data Science Applications Specialization

Specialization

This specialization enhances your Data Science skills by teaching advanced statistical modeling techniques, including Linear Models, R Programming, Regression, Calculus, Probability, and Linear Algebra.

Intermediate
View More
View More

Intermediate Statistical Modeling in R

Course

This course delves into sampling variability, mathematical transforms, collinearity implications, effect size, interaction, and understanding total and partial change to enhance your R Programming skills.

Intermediate
View More
View More

Introduction to Statistical Modeling in R

Course

This course delves into essential statistical methodologies using R programming, equipping you with the skills to design, train, and evaluate models while assessing prediction performance and exploring data with covariates and effect size.

Intermediate
View More
View More

Resource Stacks

We are soon crowdsourcing these resource stacks to collate the best resources, such as publications, community groups, job boards, etc., that are practically suitable for every contextual stack.
Publications
Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Communities updating
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
Research updating
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.
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