Data analysis is a complex subject, with many different tools for exploring data in new and interesting ways. Business analyst jobs might also require you to have the knowledge of these tools. The sheer number of software packages can be overwhelming – and so can the nuances about which package does what best. With this in mind, we’ve put together five pieces of advice that you must listen to before studying data analysis tools.
1) Big Data Analysis Tools:
You will need to analyze large databases to find out trends and correlations between different variables in order to make accurate predictions. You are probably already aware of this fact and so you may be looking for a data analysis tool that can help you do this. However, there are many factors to consider when choosing an existing tool for big data analysis. First, the size of your data: a tool that can handle 100GB/s may not scale well if you only have 200MB/s or less. Second, the type of data: will it be a large pile of numerical inputs (like an Excel file) or a small amount of textual files (like a CSV file)? Thirdly, the quality and performance you require.
And this is where the previous advice comes in handy – because the tools themselves vary in these characteristics.
2) Data Visualization Tools:
The data that you collect shouldn’t always be analyzed in absolute terms. It’s important to visualize your data points and look for patterns and correlations. You should know from the start what kinds of visualizations are appropriate for the type of data that you’re collecting. There are three main categories of data visualization tools: basic, advanced and comprehensive.
Basic visualization tools are best for visualizing numerical data sets that contain no more than two or three variables. This type of visualization works best for graphs, histograms and scatter plots. However, keep in mind that the more variables you have, the harder it will be to choose a visual mapping between them (for example: Which variable should the x-axis represent?).
Advanced Visualization Tools are best for visualizing large amounts of data that either includes non-numerical variables or has multiple levels of numerical variables (for example: A table with all the Facebook users with their gender and age). These tools typically offer the same visualizations as Basic Visualization Tools, but are designed to handle more complex features.
Comprehensive Visualization Tools are best for visualizing complex systems related to social media and telecommunications. These tools tend to include elaborate system simulations and detailed charts – among other features.
3) Descriptive Tools:
Don’t just collect data, analyze it! These days, many standard statistics packages have added features that are designed to help with descriptive analysis as well – including visualizations like bar charts, pie charts, and scatter plots. These tools can quickly summarize and interpret your data, but they don’t really help you interpret the deeper meaning of the results. For example: if you’re studying a stock market, you can glean some interesting information from the graphs and charts that you create. However, they won’t tell you whether or not the stock is underperforming or outperforming compared to its competitors.
This is where statistical software like JMP comes in handy – because JMP offers advanced descriptive analysis tools that can help answer more complicated questions about your data. For example: Suppose we have three hypothetical stocks: X, Y, and Z.
4) Predictive Tools:
A lot of data analysis is about making predictions based on past results. You might want to predict customer traffic or an election outcome. You might even want to predict the success of a company’s new product! If you’re making a prediction, you should know the different ways that a predictive tool can make the process easier for you. One way that a predictive analysis tool can make the process easier is by helping you find patterns in your data. For example: suppose you want to predict the stock price of company ABC. The software can automatically find relationships between factors like Finances, Assets, and Performance.
Another way that the software can help you is by taking care of tricky tasks like statistical modeling and data manipulation. For example: Suppose we want to predict a company’s share value based on its total production costs and market share. We can use a statistical model that helps us find mathematical relationships between variables.
Often, these models require a lot of manual input. But with these tools, you can tell the software to perform part of the calculations for you – and then leave the rest of them up to you! This is good when dealing with very large datasets – it allows you to focus more on the analysis problems at hand rather than managing your data.
5) Statistical Tools:
Statistics has become an indispensable part of data analysis because it gives analysts a set of tools for interpreting their results and drawing conclusions from them in a compelling way. Most modern data analysis tools include a statistical function that can make those conclusions easy to verify. For example: Suppose we have a table that looks something like this:
This is an example of a scatter plot where the horizontal axis represents the age of the test subjects and the vertical axis represents their scores on a test. This type of data is best analyzed by using tools like JMP’s regression tool. It can automatically create estimates for your dependent and independent variables – making it easier to check your results!
Conclusion
As you can see, there are many different types of data analysis tools out there – but it’s important to keep in mind which ones will work best for your particular needs. You may even have to combine several tools. For example: If you want to find relationships between two sets of numbers, Medium and Shiny are both a good platform and good choice for this purpose because they allow you to create interactive visualizations that push your data into the open source ecosystem – where others can verify your results and make improvements.
However, JMP is more powerful in certain aspects of data analysis than Shiny. So if you’re looking at basic statistical functions (like linear regression), JMP is probably the better choice. The great thing about data analysis tools is that, nowadays, you don’t have to be a programmer to create interactive visualizations that can push your data into the open source ecosystem – where others can verify your results and make improvements!
