How To Become A Data Analyst
How is Data Analyst defined?
The most credible description of data analysts is probably – detectives.
This means that in a pile of clues, data analysts see what no one else does. They put the best clues together and draw conclusions, diagnoses, and predictions.
In a nutshell, data analytics implies:
- the collection and storage of raw data,
- organization and cleansing of such data,
- making conclusions and recommendations.
The point is always to find an answer to a specific question or a challenge that the company marks as important. In a real-life dynamic business world, the data can be related to sales numbers, market research, manufacturing processes, linguistics, and other numerous behaviors. There are bottlenecks and room for improvement or a better understanding of a certain phenomenon in any given area. A data analyst can ultimately ensure it by applying technical expertise and advanced technology.
Sounds great but do you actually know how to become a data analyst? It takes a lot of work, effort, and motivation. Are you up for it?
Types of data analytics
The main question – What happened?
It's the simplest method that can either stand on its own or serve as a basis for the other three analytics types. By means of current and past data, trends and relationships are identified to arrive at an explanation. It can be done with basic software like Excel or Microsoft Power BI. Examples include website traffic and engagement reports, yearly financial reports, or survey results analysis.
The main question – Why did something happen?
This type of analysis will usually begin with a hypothesis which will then be proved or disproved.
Here, it's important to understand what happened, what caused it, and to what degree. When data spikes are identified, only knowing the reason will enable repeating or preventing them.
For instance, is there a relation between the last marketing campaign and increased sales? Why do our followers keep canceling subscriptions? What impacts are our employees leaving the company? And so on.
The main question – What is going to happen?
Predictive analytics implies using past data to recognize trends in order to make assumptions about future events. It is used every day in the preparation of weather reports or betting forecasts.
One familiar tool for this type of analytics is regression analysis, which helps determine the relationship between two or more variables.
The main question – What should we do about it?
Finally, the last type of data analytics is performed to choose the most optimal course of action. Obviously, this type of data analytics is valuable for business decision-making. Machine-learning algorithms are used to clean large amounts of data. This way, it's done a lot faster and more efficiently than humans can do manually. However, algorithms can't fully replace human reasoning, so a decision-maker should always add context before making the last call.
Examples include investment decisions, content suggestions, and fraud detection in banking.
Main data analyst skills
Now that we've covered four types of data analytics, we can reveal what you need to be a data analyst.
There are plenty of skills that would make a perfect analyst. But as the requirements differ from job to job and industry to industry, we will focus on the core ones. The below list also answers a question – what do data analysts do?
1. Data cleaning and preparation
Data extraction, cleaning, and preparation is basically an introduction to data analytics. This makes knowing how to do it an essential skill for anyone serious about this line of work.
It involves retrieving data from the relevant sources and preparing it for numerical and categorical analysis. In any pile of data, there are always missing, duplicate, or inconsistent data sets, which then need to be cleaned not to affect the analysis.
It's not the most attractive part of the job, but it requires the application of problem-solving skills that are also mandatory for this position.
2. Data analysis and exploration
At the beginning of any analysis, there is usually a question. When the question comes from a business perspective, a data analyst has to understand it in the language of data. Once he has the frame to work with, he searches for answers among piles of data.
On the other hand, data exploration doesn't necessarily start with a question. It can also begin with a data analyst looking for trends and relationships between data. It's just another way of searching for business value in the sea of unknown and unrelated.
Both of the first two main sets of skills are performed with advanced analytics tools. Keep reading to learn more about the available technology.
3. A high level of mathematical and statistical ability
Clearly, a foundation of any good data analyst is mathematics and statistics. When you're a "numbers person" it helps tremendously to understand the data that you're working with.
It's no surprise that many data analysts thus come from a background in those two areas.
4. Data visualization
The work of data analysts also involves creating visualizations of trends and patterns for better comprehension. To do this, you must learn how to tell a story from complex data. Once an answer is found, it needs to be clearly communicated to decision-makers. This means designing clean, visually compelling charts that will help others understand the results. Some of the best tools to create interactive visualizations include Tableau, Qlik, and Sisense.
5. Problem-solving skills
Problem-solving skills are essential as they allow data analysts to get out of complex situations they find themselves in daily. It enables determining the cause of the problem, identifying, prioritizing, selecting, and implementing a solution.
Whatever the circumstances, strong problem-solving skills are an incredible asset for any data analyst.
6. Effective communication
Although as a data analyst you are fascinated with numbers, communication skills are equally significant. You must be able to communicate in multiple formats, including writing, speaking, explaining, and listening.
Without solid listening skills, you might not understand the business question or a need shared in initial meetings. Similarly, during projects, you will certainly need to be able to explain complex findings to less technical specialists. Finally, top-notch writing skills are key for a clear and concise presentation of your analysis and recommendations.
7. Must-know tools
There are plenty of data analytics tools that support the sophisticated handling of data. Basic ones include Excel and Microsoft Power BI, while some of the more advanced ones are Tableau, SAP BusinessObjects, and Sisense.
Want to know more? Check out the article focused specifically on the top 10 data analysis tools for effective data management.
Salary and other good-to-know stats
According to the career expert site, Zippia, the average data analyst salary in the US is $67.000.
Entry-level pay rises up to approximately $50.000, and top-performing data analysts make around $90.000 per year. Salary-wise, the highest earners can be found in tech, while health care and manufacturing are at the bottom of the salary chain.
There are currently close to 90.000 data analysts employed in the US with the same number of male and female employees.
When it comes to data analyst school and education, around 65% hold a bachelor's degree, and 15% own a master's degree. Backgrounds differ from mathematics and statistics to coding, business, finance, and data science.
Typical employers of data analysts
- Specialist software development companies
- Telecommunications companies
- Public sector organizations
- Social media specialists
- Colleges and universities
- Healthcare companies
How to become a data analyst?
For starters, master the skills discussed above. If you lack experience, you need to get your hands dirty and feel the power of available data analytics tools. This can be through internships, volunteering, using free software versions, e-learning courses, or hanging out with professionals.
Of course, it's desirable to have a data analyst qualification in mathematics, statistics, or anything related. However, this is not a prerequisite. Who knows, you might just be the new Good Will Hunting.
As noted earlier, not all data analysts have official education under their names. So this time, we'll focus more on practical advice.
1. Learn the basics of programming
Specifically, master languages like R or Python and the leading spreadsheet and data analysis software – Microsoft Excel. Practical knowledge like this is basically essential for any given day at the work of a data analyst.
2. Engage in projects
Make sure they include all the different stages of data analysis: the collection and storage of raw data, organization, and cleansing of such data, analysis, visualization/design, making conclusions and recommendations. For this, you'll need both access to large amounts of raw data and tools every step of the way.
3. Apply for online data analytics courses
Regardless of whether you own a degree, quality online courses are often more focused and get you from A to B faster. For example, if you lean towards data analytics and have studied Finance, it's not surprising if you have not gained strong practical experience in data analysis during your education.
Here's a reality check – did you know that Python is a favorite language for rapid finance analysis? If not, here's a way to get introduced to the Python language in the Finance context you're already familiar with.
4. Share your work and infiltrate the community
Branding doesn't only work for companies. You are your own brand, and there are plenty of channels today to market yourself. You might not be up for spotlights, but being active and visible can land unexpected opportunities and connections. So brush up on your LinkedIn profile or start a website to share your thoughts and ideas.
5. Develop a data analyst portfolio to showcase your work
SIf you're doing things right, you need to showcase it. A picture says more than words, right. This part is equally important as the visualization phase of data analytics. Like directors want charts more than spreadsheets, employers fall for portfolios more than resumes. This is not to say you should disregard your CV, not at all. However, supplementing it with a collection of your work will land you that dream data analytics job a lot quicker.
So, detective, what are you going to do?