Top 10 Technical Skills to Get a High-Paying Data Analyst Job

The global big data analytics market is booming and is expected to grow by more than $400 billion between 2023 and 2030. There has also been a sudden shift in data analytics trends in the last few years. According to a Gartner report, changes in the global market conditions have made it necessary to use Artificial Intelligence (AI) frameworks to increase growth and innovation, provide better governance, and enhance data analysis. It’s easy to see, then, that being a big data analyst is one of the coolest jobs in the market at present. This is especially true for professionals interested in big data and technologies such as AI and machine learning. So, let’s examine what a career as a big data analyst looks like and how rewarding it could be in the long run.

What Job Titles are Associated with Big Data Analytics?

The concept of big data analytics refers to the practice of collecting, sorting, examining, and analyzing large amounts of data to generate valuable insights for businesses. However, as all businesses and industries need market insights to make data-driven decisions and grow, one can opt for multiple big data analytics roles. In general, here are the most popular job titles that require similar skill sets:

1. Business analyst
2. Business intelligence analyst 
3. Data specialist
4. Analytics consultant
5. Operations analyst
6. Analytics specialist
7. Research analyst
8. Data engineer
9. Data manager
10. Business intelligence engineer
11. Quantitative analyst 

ALSO READ: Key Differences Between Data Analyst and Data Scientist: Which Role is Better

What Industries Demand Big Data Analysts?

Due to the profound use of big data, many companies are investing heavily in big data analytics. To be more precise, a staggering 97.2% of global businesses are investing in big data and AI. Therefore, all industries, whether technology, healthcare, marketing, fintech, banking, agriculture, and more, demand big data analysts to interpret data. Nevertheless, the industries with the highest demand for AI and big data professionals are:

• Technology/Telecom
• Finance
• Advertising and marketing
• Consulting
• Healthcare
• Retail and consumer goods

What are the Technical Skills Required to Become a Big Data Analyst?

1. Data Visualization

Data visualization generally refers to visualizing or representing data in a graphical form like charts, tables, or pictures. Evidently, the primary purpose of data visualization is to make complex data easy to understand. However, since big data involves a huge amount of data that needs to be simplified and easily explained to various stakeholders, data visualization tools and techniques have become increasingly important. In fact, data visualization is a critical aspect of big data analysis, as it enables data analysts to convey insights and findings effectively to decision-makers.

ALSO READ: How SQL for Data Science Helps with Data Visualization

2. Data Cleaning

Big data analysis requires skills and accuracy as businesses make crucial decisions based on analysis reports. Therefore, one needs to collect and analyze valuable and correct data. However, as big data is often collected in large amounts, factual errors or duplicate data are likely. Fortunately, this is where data cleaning skills come in handy as they help remove unwanted data, duplicate data, incomplete data sets, or corrupt data.

3. Data Analysis

The key role of a big data analyst is to examine and interpret data to find out uncommon trends or patterns. Strictly speaking, these trends or patterns are further analyzed to generate valuable insights for the business and make data-driven decisions and boost growth. Hence, data analysis is one of the most essential skills for a big data analyst.

4. MATLAB

It is an advanced programming language and development environment which facilitates numerical computation, visualization, and programming. Without a doubt, MATLAB is invaluable when it comes to working with large data sets and performing complex calculations.

5. Python

It is another powerful programming language that supports data analysis, machine learning, and artificial intelligence. Learning Python is also important for a data analyst; it helps to manipulate and analyze large data sets and build predictive models.

6. Machine Learning

Machine learning refers to building and training predictive models using algorithms to learn from and make predictions on large data sets. It is useful for a big data analyst because it can help build algorithms to process huge amounts of real-time data.

7. Artificial Intelligence

AI has a crucial role to play in big data analysis. To begin with, it automates and streamlines data preparation, data visualization, statistical analysis, predictive modeling, and other data analysis processes. To sum up, learn AI to derive maximum value from data.

8. Data Structure and Algorithms

The ability to design and implement efficient data structures and algorithms is crucial to process and manipulating large data sets. However, to acquire this skill, big data analysts need proficiency in programming languages such as Python, Java, and C++, as well as knowledge of data structures and algorithms.

9. Data Mining

The data analysis process involves discovering patterns and insights in large data sets using machine learning, statistical analysis, and other data analysis techniques. One essential skill required in this process is data mining. Furthermore, knowledge of programming languages such as Python and R and a deep understanding of AI and ML algorithms are also necessary to excel in data mining.

10. SQL and NoSQL Databases

Lastly, big data includes structured and unstructured databases, which are difficult to store and manage. Moreover, retrieving data sets from large databases is another significant challenge. As a result, it is crucial to learn Structured Query Language (SQL) and NoSQL database technologies such as MySQL, PostgreSQL, and MongoDB. Mastering these technologies makes it possible to efficiently extract, manipulate, and access data. And all this is required to ultimately succeed in working with big data.

What is the Earning Potential for a Big Data Analyst?

The average annual salary of a big data analyst in the U.S. is $78,216. However, the earning potential largely depends on skills, experience, industry, and location. Here’s a list of the average annual salary of a big data analyst according to different locations in the U.S.:

How Can Emeritus Help Big Data Analysts Move up the Career Ladder?

To conclude, AI is the core area to focus on as per changing data and analytics trends in the market. Artificial intelligence is the focus of data and analytics, whether it is learning AI adaptive systems, formalizing data-centric AI models, AI-centric data models, or building AI risk management tools. Enrolling in Emeritus’ online data science courses is the best place to acquire the necessary AI and big data analyst skills. They ensure career growth, and these courses provide opportunities for professionals to expand their network, leading to excellent opportunities in big data analytics.

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