Over the last few years, there has been a rise in the need for data analysts, which has further accelerated due to the Covid-19 pandemic. As a result of this high demand for skilled data analysts, there has also been a proportional increase in salaries. According to PayScale, data analysts in various organizations and consulting firms receive an average salary of $64,000. However, individual factors such as the quality and duration of an analyst’s experience, technical proficiencies, the employer, location, and industry can significantly impact their salary.
The subject of this blog post is the leading compensation rates for data analysts across diverse industries, as well as suggestions for increasing your worth and bargaining for a more elevated remuneration as a data analyst. However, prior to delving into those topics, we will examine the responsibilities of a data analyst.
What Does a Data Analyst Do?
Businesses produce a vast amount of data that could yield significant insights, necessitating the assistance of data analysts. For example, identifying best-selling product categories by region or monitoring daily and time-specific customer behavior patterns. Data analysts are technical workers who leverage their analytical skills, programming language expertise, and business comprehension to perform statistical analyses on extensive data sets and extract practical information from them.
The usual activities involved in data analysis are as follows:
- Collecting data, cleaning it, and examining it for trends to support business decisions and suggest improvements
- Using Python, SQL, or other programming languages and statistical tools to process data and decipher information
How Much Do Data Analysts Make?
Based on insights from LinkedIn, the typical yearly compensation for a data analyst in the United States amounts to $90,000. However, salaries can vary depending on factors such as the individual’s background, geographical location, field of expertise, nature of the organization, and the like. Additional perks such as yearly bonuses and signing incentives may also be available outside of the base pay.
According to the 2022 Robert Half Technology Salary Guide, an entry-level data analyst makes an average of $116,375, while those with exceptional skills and specialized experience can earn up to $167,913.
Data Analyst Salaries by Role
Below are various job titles that fall under the data field along with the possible career trajectories for each. Please note that these are not mandatory progressions and serve only as potential options for you to consider.
| Job Title | Data Scientist | Data Analyst | Data Engineer |
| Job Description | Capture, clean, sort, and organize big data. Identify ways to do this more efficiently and effectively. | Gather, review, and analyze data to provide actionable insights for critical business decisions. | Design and develop information systems and build infrastructure/ architecture used in data generation. |
| Career Paths | Depending on specialization: – Data visualization – Data mining – Business intelligence – Database management – Data analyst | Business/marketing/financial analyst Systems analyst Analyst programmer Analytics manager | Data architect Database administrator Big data engineer |
| Average Salary | $105,000 | $65,000 | $97,000 |
Having grasped the fundamental contrast between data scientist, data analyst, and data engineer, let’s explore further into the possible career routes for a data analyst.
Data Analyst
One of the main duties of a data analyst at the beginner level is to recognize trends, patterns, and connections in the data gathered and draw conclusions from it. In addition, they utilize data mining methods to structure the data and provide stakeholders with reports. These insights are then used by businesses to make crucial decisions.
The salary range for novice data analysts is between $41,000 and $93,000, while senior data analytics professionals with extensive experience can earn more than $250,000.
Business Analyst
Fundamentally, business analysts are individuals who solve problems in an analytical manner and are part of a business or organization. They collaborate with various stakeholders, including technical teams, managers, vendors, and others to provide recommendations that are backed by data after identifying areas that need improvement.
Business analysts typically have a background in business or project management and possess critical thinking, logical reasoning, analytics, and design thinking skills. Familiarity with tools such as SQL, Microsoft Visio, BPMN, and Excel is beneficial for data mining and cleaning, as well as reporting. In the United States, the average salary for business analysts is $70,000, while those with 3-4 years of experience may make up to $109,000 annually.
Marketing Analyst
The role of a marketing analyst (MA) is similar to that of a business analyst, with a focus on interpreting marketing data to gain knowledge about sales, markets, channel performance, and promotions. To accomplish this, an MA must possess research and analytical capabilities, as well as expertise in marketing tactics like customer profiling, ROI determination, and assessment of advertising channels.
The salary for a marketing analyst typically begins at $59,400 and can increase up to $122,000 depending on their level of experience. Additionally, this position offers opportunities for advancement to higher-paying roles, including senior analyst, marketing consultant, and strategist.
FAQs About Working as a Data Analyst
Is Data Analytics a Good Career?
Data analytics is a highly lucrative profession with ample growth prospects. The daily production of approximately 2.5 quintillion bytes of data has prompted various industries to exploit it, with data analysts spearheading this movement.
Do Data Analysts Get Paid Well?
The pay for data analysts is on the rise due to the growing demand for their skills. Starting salaries for data analysts are above $40,000 while senior positions typically offer compensation exceeding $100,000.
How Much Money Can a Data Analyst Make?
According to LinkedIn, the typical initial income for a data analyst in the USA is $90,000 in 2021. However, the US Bureau of Labor Statistics suggests a median salary of $86,200, and the Robert Half analysis indicates a midpoint wage of $106,500 for data analysts.
Do You Need a Degree To Be a Data Analyst?
It’s possible to establish a career in data analytics without obtaining a formal degree. Attend extensive bootcamps such as the Springboard data analytics bootcamp, which provides a six-month job assurance to enhance your abilities. Gain knowledge on how to navigate the data analytics sector, negotiate compensation, and other valuable insights.
BONUS: How to Learn Big Data?
Big data is a vast quantity of information that is produced on a daily basis by individuals, as implied by its name. This information can encompass various forms, including a single Facebook post.
The rate at which data is growing is rapid. A report predicts that by 2025, there will be a daily production of 463 exabytes of data across the world, which is equivalent to 212,765,957 DVDs every day.
Big data analytics is necessary to handle and manipulate massive amounts of data that are generally unstructured, such as image data, text data, audio data, and other forms of data.
The process of discovering valuable patterns from vast quantities of unstructured data is called big data analytics. This involves numerous stages, including data cleansing and pattern identification. The aim is to collect and manage large amounts of data using a concept known as big data, which is characterized by the 3 V’s –
- Volume- It refers to the size of data, which means how much data is generated.
- Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
- Velocity- It refers to the speed of data, which means at what speed data is generated.
Next, we will proceed to the systematic approach to Big Data.
How to Learn Big Data Step by Step?
Step 1- Learn Unix/Linux Operating System and Shell Scripting
It is important to have a strong foundation in shell scripting as numerous tools utilize a command line interface that relies on shell scripting and Unix commands.
Data pipelines can be constructed using Shell Scripting, which involves creating a text file with a series of commands for an operating system that runs on UNIX.
These resources are available for you to acquire knowledge in Unix/Linux Operating System and Shell Scripting.
Resources
- Linux Command Line Basics (FREE Course)
- Shell Workshop (FREE Course)
- Configuring Linux Web Servers (FREE Course)
- Linux Fundamentals (Coursera)
- Introduction to Bash Shell Scripting (Coursera Project)
Step 2- Learn Programming Language (Python/Java)
Java continues to be the foundation for numerous big data frameworks due to the fact that several key modules of renowned big data tools are Java-based.
Although Python is capable of processing Big Data, Java is more straightforward and does not require third-party assistance.
The choice to learn either Java or Python is yours to make.
Hadoop is a framework within Java that enables the creation of big data applications. On the other hand, Python offers numerous open-source libraries and tools. If you are a novice, it is recommended that you opt for Python since it is relatively easier to comprehend and implement. However, if you are experienced, then Java would be a better choice.
Let us now explore the sources for studying Java and Python.
Python Resources
- The Python Tutorial (PYTHON.ORG)
- Python for Absolute Beginners! (Udemy)
- Python for Everybody (Coursera)
- Python 3 Tutorial (SOLOLEARN)
- CS DOJO (YouTube)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
- Python Crash Course (Book)
Java Resources
- Java Programming Basics (Free Course)
- Become a Java Programmer (Udacity)
- Become a Java Web Developer (Udacity)
- Core Java Specialization (Coursera)
- Introduction to Java (Coursera)
- Java Programming Masterclass covering Java 11 & Java 17 (Udemy)
Step 3- Learn SQL
A strong understanding of SQL is essential as it is the most in-demand skill for Big Data. Additionally, knowledge of NoSQL is necessary since unstructured data may need to be dealt with.
Experimenting with SQL within relational databases enhances our comprehension of how to search through extensive datasets.
Resources
- Learn SQL Basics for Data Science Specialization– Coursera– This specialization program is dedicated to those who have no previous coding experience and want to develop SQL query fluency. In this program, you will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.
- Excel to MySQL: Analytic Techniques for Business Specialization– This Specialization program is offered by Duke University. This is one of the best SQL online course certificate programs. In this program, you’ll learn to frame business challenges as data questions. You will work with tools like Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
- W3Schools– You can learn DBMS and its concepts from the Free Tutorial of W3Schools.
- NoSQL systems– In this course, you will learn how to identify what type of NoSQL database to implement based on business requirements. You will also apply NoSQL data modeling from application-specific queries.
Step 4- Learn Big Data Tools
After acquiring expertise in Python, Java, and SQL, the subsequent stage is to gain proficiency in the tools used for Big Data. Familiarity with tools such as Hadoop and MapReduce, Apache Spark, Apache Hive, Kafka, Apache Pig, and Sqoop is necessary.
Resources
- Intro to Hadoop and MapReduce(Udacity)- This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.
- Spark (Udacity)- This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.
- Hadoop Developer In Real World (Udemy)- This course will cover all the important topics like HDFS, MapReduce, YARN, Apache Pig, Hive, Apache Sqoop, Apache Flume, Kafka, etc. The best part about this course is that this course not only gives basic knowledge of concepts but also explores concepts in deep.
- Big Data Specialization (Coursera)– In this specialization program, you will get a good understanding of what insights big data can provide via hands-on experience with the tools and systems used by big data scientists and engineers.
Step 5- Start Practicing with Real-World Projects
Congratulations on becoming proficient in Big Data skills! Now, it’s essential to begin working on actual projects, as they are crucial to securing a job as a Big Data Engineer.
The greater number of projects you undertake, the more comprehensive knowledge you will acquire in Data. Additionally, projects will enhance the strength of your Resume.
If you’re looking to learn, you can begin with real-time streaming data obtained through APIs on social media platforms such as Twitter.
That concludes everything! By following these steps and acquiring the necessary skills, you will be unstoppable in entering the Big Data field.
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