Data analysis can be daunting and time-consuming. But with a little knowledge and some simple tips, it can be much easier. Even if you’re not an analyst by trade, these seven simple tricks will help you get the most from your data.

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**Make sense of your data with descriptive statistics**

Descriptive statistics summarise your data in a few key graphs to give you an overview of the whole. They can help you understand how the data is distributed, and whether there are any unusual patterns or outliers. You can also see how much data you have so that you can plan your research accurately.

If you have too much data to fit on a graph, you can use basic metrics like the mean, median, and standard deviation to see where the majority of data lies. These are useful metrics for both quantitative and qualitative data, and you’ll see them used throughout the rest of this article.

**Visualize your data**

A visualisation is a picture, or a series of pictures, that explains or shows your data. You can show how many observations there were, how they were distributed, and even show the relationships between different variables. But you don’t have to show visualisations on paper.

Visualisations can be done in many different ways, including graphs, charts, and maps. You can even create your own custom visualisation software if you want to get creative! Visualisations can be very useful when you’re trying to understand what your data looks like.

They can show different patterns or relationships you might not have noticed before. You can also use them to explore how your data compares to other sources, or compare your data with a standard like a population or a standard distribution.

**Understand the difference between discrete and continuous variables**

Depending on the kind of analysis you’re doing, you’ll be dealing with discrete or continuous variables. Continuous variables usually have a numeric value, like the height of a person (measured in feet and inches) or the speed of a car (measured in miles per hour).

Discrete variables are unchangeable, like whether someone is alive or dead. Therefore, you cannot add 1 to a person’s age, and you cannot add 1 mile per hour to a car’s speed.

If you have data that is only a mix of continuous and discrete variables, it can be tricky to figure out what you can really do with it. A statistician can help you untangle these variables.

**Calculate common metrics**

If you want to see how your data compares to other datasets, or how it compares to a standard like the population, you can calculate common metrics. Common metrics are important because they can give you a good idea of your data’s value by comparing it to something else.

You can calculate a metric like the mean, which gives you the average value and is a common metric that’s used in statistics. You can also calculate standard deviations, which show you how far your data is from the mean, and tell you whether it’s likely to be a typical value. You can also calculate other metrics like the 95% confidence interval, and the variance, which shows you how much each value is different from the mean.

**Use conditional probability to answer questions about uncertainty**

If you’re trying to understand the likelihood of certain outcomes, conditional probability is an important method for calculating it. Conditional probability is when you are given a certain fact about your data, and then you have to calculate the likelihood of other outcomes.

For example, let’s say you have a question like, “What is the likelihood that Jack will get a 95% confidence interval?” In order to calculate the likelihood of a certain outcome, you need to know certain information about your data.

You need to know the sample size, the proportion of data that was in each outcome, and the value of the confidence interval.

**Ask new questions with simple programming**

If you’re trying to answer questions like, “How many unique people visited our website?” or “What are the most popular products sold?” you’ll need to extract data from your website.

There are many ways to collect this data, and some of the more common ways include scraping, and AJAX. Scraping is gathering data from websites, such as Google Analytics. This is fine if you only have a few pages on your website that you want to collect data from.

However, it’s better to have a more structured way to collect this data, like an API. Another important factor in data analysis is, of course, your data’s quality. High-quality data is sure to yield more accurate results. It’s important to clean your data so that you can understand the data accurately and get the most out of it.

**Wrapping up**

Data analysis is important. By using descriptive statistics and visualisations, you can get an overview of your data and understand how it is distributed. You can also see if there are any unusual patterns or outliers.

Once you have an overview of your data, you can use common metrics and conditional probability to understand the likelihood of certain outcomes. You can then ask new questions with simple programming.

High-quality data will yield more accurate results, and it’s important to clean your data so that you can understand it accurately. Data analysis can be daunting, but with a little knowledge and some simple tricks, it can be much easier.