Data Analyst
As a data analyst, you will be working with large amounts of information to find patterns, shapes, and insights into the data. You can use this knowledge to help predict future events or identify issues that are happening now.
Data analysts work directly with other professionals outside of analytics in order to get the right answers. For instance, an accountant would handle financial records, while someone else could perform statistical analysis on these numbers.
The people around you will vary depending on what area of analytics you choose to focus on. Some jobs may require only basic math skills whereas others may ask you to do more advanced mathematics.
There is no specific degree for becoming a data analyst, but most employers look at either bachelor’s degrees in business administration, computer science, statistics, or economics along with some form of certification.
With all those academic pieces under your belt, employers trust you have the fundamental skills needed to become a professional analyst.
Analyzes data to find insights
As our world becomes more connected through technology, there are now limitless possibilities for gathering information. With every conversation you have, every item you purchase, and every picture and video you take, your digital footprint is growing at an incredible speed.
With all of this content floating around, how do you make sense of it? You use analytics to analyze the patterns in the data to gain valuable insights. Analytics can be done manually or automatically using software programs.
Data analysts work with large amounts of data so they often need strong quantitative skills that include mathematics, computing, statistics, and logic. They also must be able to interpret the results of their analysis and relate them to other sources of information.
This article will talk about what data analyst jobs exist, what education is needed to become a data analyst and some data analyst careers that are getting popular. But first, let’s look at what people call themselves when they refer to themselves as “data scientists.
Compares different data sets
As a data analyst, you will be tasked with comparing different types of information to determine what factors contribute to positive changes in sales or performance.
You may be asked to compare two different departments’ efficiency levels, find patterns in customer behavior that lead to improved service or product offerings, or evaluate whether certain strategies are working for your company as it implements new initiatives.
Data analysts use statistical tools to quantify how much impact each factor has on business outcomes. For example, if one department is performing better than another, we can calculate what part of the difference comes from luck and what parts come directly from the organization doing the performance.
By using analytical techniques like regression and correlation, data analysts assess the degree to which one variable influences (or “correlates with”) another. This helps us identify important drivers of success so that we can influence these variables to achieve our goals.
Identifies patterns
As a data analyst, you will be asked to identify trends in large amounts of information. You may also be asked to make predictions using these trends as clues.
By learning how to analyze different types of data, you can determine what changes need to happen at your workplace or outside the organization. For example, if there is a lot of traffic coming into an area during work hours, it may indicate that people are hungry and have nothing to do after work. Or, if employees are having trouble getting out of bed in the morning, it may signal that they don’t feel like going to work.
You can use this knowledge to improve employee morale or find ways to incentivize them so that they want to go to work more. If someone already goes to work every day, try finding other things that they can add to their routine to see what effect it has on their productivity.
Your job as a data analyst isn’t just to look for patterns but also to determine whether those patterns represent chance or design. For example, if there was a sudden increase in foot traffic around one particular building, it might mean that something big happened there, such as a fire. However, it could also be because it was raining and some people were seeking shelter.
There is no surefire way to tell unless you have additional pieces of evidence.
Reviews the quality of the data
As a data analyst, you will be tasked with reviewing the quality of existing datasets and determining whether they are good enough to use or if more thorough testing is needed to ensure their accuracy.
You may also need to make sure that the data being used for analysis has no obvious errors such as numbers that look wrong. For example, say there’s an article about how eating chicken can help increase your bone density. The article uses statistics to show that people who eat at least 2 pounds of chicken per week have higher bone densities than those who don’t.
However, what it doesn’t tell you is that most restaurants count skin as part of the “chicken” weight when calculating this statistic. Because fat tissue contains lower levels of collagen, which helps bones grow, having extra skin actually lowers bone mineral content. By only counting meat as part of the diet calculation, the writer may be obscuring the true effect of the food on bone health.
Data analysts review these types of details to make sure that the data they use to create reports and conclusions is accurate. This is important because even small mistakes can lead to misleading results.
Alternatively, looking at low-quality data may not provide you with the full picture of what you want to know. If the writers of the article in question didn’t check the math carefully, then you might assume that their findings were solid, but they could be missing relevant information.
Reviews the processes that the company uses
As mentioned before, data analysts use various tools and techniques to gather information. These include software such as SAS or SPSS for statistical analysis, Google Analytics to review website performance, and spreadsheets and documents to review processes and steps.
Data analysts also look into how the departments in an organization organize and manage their files and records. This is done to determine if everything is organized properly and efficiently so that you can help improve efficiency by altering how things are run.
They also evaluate whether there are any potential safety hazards present at work and whether anyone was given proper training on using equipment or performing tasks.
Identifies gaps in processes
As a data analyst, you will be tasked with ensuring that your department or company has all of the tools it needs to perform its jobs effectively. You can help ensure this by identifying any potential weaknesses in existing procedures and systems.
Many companies have internal software they use for various tasks. They may not fully utilize these programs because there is no one assigned to make sure everything works together. It would be your job to find out which applications are used most frequently and what functions they have.
It’s also important to know how well these apps work together. For example, if a certain type of information is gathered in one app, then why not create an automatic link between those two? This could save time and money for your organization.
Data analysts also look into whether employees are performing their duties properly. There might be something going unnoticed that is keeping your company from running at full efficiency.
The more aware you are of the things happening around you, the better chance your team has of finding problems early. In turn, your organization will have a higher chance of surviving difficult times.
Data analysis is a highly specialized field that requires lots of different skills. But being a good analyst isn’t just about knowing advanced mathematics and statistics, it's about understanding other disciplines as well.
Reviews results to determine if there are any biases
Another important role of data analysts is reviewing their work to see if there are any systematic biases.
Systematic bias happens when something in your model or analysis tool produces biased results. For example, using income as a predictor for whether someone will go into debt may lead to different conclusions depending on where you live.
In some areas of the country, people with higher incomes tend to take on more loans so this predictor would give different predictions based on where you use it. This makes sense because they can afford to be less strict about keeping up with payments and other obligations while having enough money for food and bills.
Alternatively, individuals in poorer areas may need additional help paying off debts, making them riskier candidates for credit card companies. Systematic differences like these make statistical models that rely heavily on predictors prone to misleading findings.
Data analysts look at how accurate each element of a predictive model is by testing its accuracy on separate datasets. They also test different versions of an element to see which one gives the best prediction.
Identifies weaknesses and strengths
As a data analyst, your role is to identify what qualities you have as a person and apply them toward achieving others’ goals. You will be tasked with identifying their weaknesses so that you can improve upon them, and helping them achieve their goal by improving or replacing those weaknesses.
This is not only important for yourself, but also for the people around you who depend on you. For example, if someone else in your team does not do his/her job well, you should inform him/her of the problem and offer help in fixing it. If there are no signs of improvement after repeated attempts, then maybe he/she is not the right fit for this position or this company.
Data analysts must be able to analyze large amounts of information to determine patterns, relationships, and insights. They must be good at math and logic to process all the numbers.
They must be articulate and communicative to discuss findings with other departments and individuals. Beyond just having these skills, an aspiring data analyst needs to be self-motivated and organized.
It is very easy to fall into a rut when performing analysis tasks, so they need to take initiative to keep up with new projects.
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