Data is the important success key of today’s businesses. If organizations know how to analyze and process data effectively, they will make accurate decisions.
That’s why the demand for data analyst recruitment is stronger than ever. So, what do you need to know before starting this job?
Let’s explore the article below.
The primary job of data analysts is to process, collect, and perform statistical analysis of data. From that information, they draw inferences and make the data more useful in one way or another.
Applying specific algorithms and suitable formulas, data professionals can refine raw data sources to extract the necessary information. This information will assist the business or organization in making the right decisions.
These professionals will spend most of their time analyzing the data. However, they also need to communicate with their colleagues about their new findings.
The work of daily data analysts includes:
- Gathering: Analysts need to collect a lot of raw data through surveys. Some businesses provide parameters for customers accessing Facebook pages, and websites, or buying data from specialized collection units.
- Cleaning: It’s hard to work directly with raw data when it’s full of errors, duplicates, and irrelevant information. First of all, it is necessary to use a programming language or a spreadsheet to eliminate this problem.
- Modeling: This stage requires the user to create and design data structures. Each type of data will have its own way of being collected and stored. Experts need to classify and model relevant information.
- Interpreting: After you have analyzed the data, you need to find trends and patterns to answer the questions.
- Presenting: The final step is to present your findings to colleagues. Please present by writing reports, or a collection of graphs, and visualizations for the best results.
As one of the most in-demand jobs today, a data analyst has high job demands.
They may work on Wall Street at private equity firms, investment banks, or hedge funds. However, the number of data analytics jobs for the marketing, healthcare, or retail industries is also increasing day by day.
Industries such as insurance, information technology, or credit also have a constant need for data analysis. Basically, these professionals are everywhere.
Giants in the information technology industry like Google or Meta handle dizzyingly large amounts of data. They need a large pool of professionals working continuously to analyze user and internal habits.
Data analysis is a job that requires a lot of knowledge and skills before practice.
So, you will have to grow slowly by learning the basics if you want to enter the world of data analytics.
Take a look at this career overview to see if you’re really the right fit for it and equip yourself with the knowledge you need to get started.
Most entry-level data analyst positions require the candidate to have a bachelor’s degree. There are many universities offering training in this field.
Besides, you can also join the online data analysis course if you don’t have enough time. These courses primarily focus on the specialist skills of the data analyst. Subjects you should focus on include subjects in math, statistics, or computer science.
This background knowledge is the launching pad to learn and learn about advanced data analysis tools like Microsoft Excel, programming languages like R, Python, or SQL; or other numbering and visualization software.
In addition to a bachelor’s degree, you will have more advantages if you hold additional relevant certifications. These certifications do not require a lot of hands-on experience.
Compared to regular university courses with foundational knowledge, participating in bootcamps and external certifications will give you real-world exposure. These authentic experiences will make your work stronger.
Some people can study at home if they have enough time and perseverance. Start by reading books that overview this concept and dig deeper later.
Any profession needs a specific skill list and data analysis is no exception. They need to blend both hard and soft skill sets.
If you are wondering about the skill set required for any individual looking to get started with data analysis, check out the section below.
Programming language knowledge is an indispensable requirement for those who want to start a career in data science or data analysis. The two most popular languages in this respect are R and Python.
Both languages provide you with data collection and processing frameworks that allow you to work more efficiently with data sets.
If you can master one of these two languages, your work will go more smoothly.
The job of a data analyst requires them to constantly access the data warehouse to update and process as needed.
SQL, or Structured Query Language, is the most popular database language today. Most data analysts should be fluent in this language.
The work of these specialists is very important when they are still building or incorporating new database structures. Therefore, recruiters are very welcome candidates who are proficient in SQL.
Once you’ve analyzed and distilled the information, you need to learn how to visualize the data and communicate it to others. The most common representational tools are graphs or charts.
This information will significantly affect how your business or customer perceives, interprets, and uses your data.
Clean, eye-catching, and engaging graphs or charts are the most effective way to present them. You can take advantage of several intuitive tools on the market like Tableau or Power BI with easy-to-understand and user-friendly interfaces.
Although natural language processing (NLP) and machine learning (ML) is not required for data analysts, you still need to understand their basics to be successful.
For more efficient data processing and analysis, try combining algorithms, machine learning, and NLP.
The spreadsheet is Excel’s most useful tool; it also has extremely intelligent analysis capabilities.
When you need a quick analysis, advanced Excel techniques such as VBA lookups or creating Macros will help a lot.
Programming languages like Python or R are only used for large data files.
Although most of your time is spent mining and processing raw data, communication is still an important soft skill. You will have to present your findings to a colleague, and this skill will support that.
This ability to communicate comes not only from words but also from gestures and data presentation. Make everyone understand your ideas and help the work progress faster.
Data mining and sorting is not a simple job and can be completed with just a few taps.
Sometimes analysts run into trouble when lines of code or algorithms don’t work as they should. At this point, they need to be able to find and fix problems.
In fact, the database is not always available and complete. Professionals will need to make the most of the data they collect if they want accurate results.
If you’re trying to interpret raw data or tell a story, you’ll need to be comfortable asking questions and researching to put the information into proper context.
Research also allows them to keep abreast of industry trends and helps defend their position when presenting their findings to colleagues on future directions.
No one can work alone, and data analysts collaborate with people in many different locations to get the job done.
They need to ensure that the steps to collect data or determine the type of database are done properly. This element requires strong collaboration with other departments.
Finding and drawing a conclusion in a vast ocean of information is quite difficult.
These professionals need to be able to notice the small clues to a larger message that are hidden in a bunch of data.
Besides, being careful also helps the analysis process go more smoothly. Even a small error can cause bigger problems in the system.
Becoming a data analyst means getting used to a lot of different tools.
The world’s most popular spreadsheet software includes calculation and graphing functions ideal for data analysis.
Its invaluable built-in features include form creation tools and pivot tables to organize data. It also has a wide range of functions that make data rationalization easier.
Python is a popular programming language due to its high availability. Unlike complex languages, Python focuses on readability.
No data analyst is ignorant of this language. Besides, Python is also extremely flexible when it comes to having a huge library of resources suitable for many different data analysis tasks.
Like Python, R is also a popular programming language for data analysis and statistics.
R’s syntax is more complex, and the learning curve is steeper as well. However, this language is extremely useful in visualizing data and solving heavy statistical computing tasks.
Statistical Analysis System, or SAS, is a popular commercial suite of data analysis and business intelligence tools.
This software was born in 1960 and is still popular to this day. Its primary uses are data mining, model prediction, reporting, and customer profiling.
Power BI was only born in 2010 but has gradually established itself as a reliable business data analysis toolkit.
With a minimal learning curve, Power BI allows users to create interactive visual dashboards and reports.
The biggest plus of Power BI is its excellent data connectivity. It also integrates with cloud sources, text files, or SQL servers.
Tableau offers a great data analytics solution for those who want to create interactive visualizations and dashboards without deep coding expertise.
The high-volume database processing toolkit is very efficient yet uncomplicated. However, since Tableau does not have a scripting layer, there are some limitations.
Data analytics is one of the most in-demand professions right now. The trend of focusing on customer behavior of businesses has clearly demonstrated that.
According to a report by the World Economic Forum, data analytics is the leading growth and does not stop. The professions related to it also have a high growth rate.
According to the Bureau of Labor Statistics, the position of the operations research analyst is expected to grow by 25%, statistical mathematician by 33%, and market research analyst by 22% from 2020 to 2030.
Total employment growth averaged 7.7% and is clearly much higher.
Currently, the top 10 companies that are in need of recruiting data analysts the most and most reputable are:
- Beyond The Arc
- SG Analytics
In the US, the base salary for a data analyst is $71,899, according to our latest statistics. This number can vary depending on your area of work, seniority, and other factors.
According to the Bureau of Labor Statistics average report, the annual salary of data analysts is $82,326.
These numbers are absolutely compelling; they are much higher than the average annual salary across all occupations in the US of $58,260.
Every job has its own pros and cons; for data analysts, those include:
The demand for data analysis is constantly increasing every day, leading to a lot of fertile job opportunities for professionals.
Around 70% of job postings in the analytics ecosystem are for data analysts who require even less than 5 years of work experience. Since this is a highly specialized job, very few people can do it.
The job market of this industry is also very flexible as you can enter a wide variety of occupations. Thanks to the development of technology, some organizations are recruiting data analysts for better performance.
Another lucrative aspect of data professionals is their attractive salaries and benefits, which can even reach six figures. If you are capable enough, your life will be very comfortable.
Online privacy is one of the biggest barriers when it comes to data collection, inspection, and transformation.
Besides, the ethical issues surrounding online privacy are not likely to end anytime soon. Analysts also often tend to incorporate privacy breaches due to security flaws when handling data.
This fast-paced field will need a data analyst to stay on top of trends and advancements. While this is true in other professions, the burden of constantly renewing knowledge and skills is enormous in data-related fields.
An analyst’s role will also vary according to the company’s specific area of expertise. However, the reality is that analysts have to deal with a lot of raw data to work with, which is undeniably time-consuming.
Ddata analytics has a balanced sex ratio between men and women of 7:3. While this is better than other fields of computer engineering, men dominate the field.
The good news is that a lot of opportunities are still out there regardless of gender.
If you still have any questions regarding data analytics, please refer here.
You can become a data analyst if you get hands-on experience with various programming languages like Python, R, or different analysis tools.
It is important to further develop communication, analytical, and creative skills. Experience with leading analytics platforms like Google or Adobe analytics will also help.
A skilled data analyst can have a very successful and varied career path.
Companies need data analysts to improve their business functions, provide clarity for business queries, and work on various data projects.
Data analysts are also required in hospitals that focus on patient analytics to monitor patient health.
Yes, being a data analyst can be stressful, but this depends a lot on your employer, the company’s culture, and what’s causing your personal stress.
While knowing how to code and knowing one or three programming languages is essential to becoming a data analyst, writing code for data analytics does not require as much in-depth knowledge as a degree in computer science.
Developing the skills required to become a data analyst can take anywhere from 10 weeks to 4 years. This range can be explained by the fact that there are different paths to becoming a successful analyst.
As a data analyst, you should work regular office hours a week. Typically, this can range from 40 to 60 hours per week.
The short answer is No. Despite the ageism in the industry, a recent Zippia study found that the average age of data analysts in the US is 43 years old.
You don’t need a formal degree to become a data analyst, but you do need a structured and structured approach to learning the necessary skills. The best way to do so is through a project-based course.
Becoming a data analyst is a process that requires effort from yourself.
Have a firm grasp of fundamental knowledge, such as programming languages or processing tools. You should also take online courses and read related books.
Another thing to remember is regular practice and personal projects; it not only helps you gain experience but also thickens your portfolio to make a good impression on employers.