7 Expert Tips On How To Push Forward In Data Science

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We generate massive amounts of data each day – some estimates put the amount of data created in the world at 2.5 quintillion bytes of data per day! The job of data scientists, then, is the seemingly insurmountable task of making sense of all these data. The data scientist’s job is to take these data, conduct analyses, and use them to come up with useful information that can help organisations improve their operations, or make new developments.

However, data science is not about blindly applying statistical procedures to any set of metrics. To gain a better idea of how to do data science, here are some tips from experts in the field.

1. State the problem you want to answer

Data science is about problem-solving. The first step to approaching data is to ask yourself what questions you want your data to answer. For example, in an e-commerce company, you might want to find out what are the differences in buying trends across people of different countries and demographics. Once you set a direction, you will find it easier to pick out the right metrics and analysis tools to help you answer the problem.

2. Use the right metrics 

Metrics are the numbers you want to be interested in to answer your problem. For example, to answer our above example, we might choose metrics like the number of sales and the amount of money spent by consumers to investigate buying trends. It might sound like common sense, but it is a common mistake to pick the wrong metrics when one is overwhelmed by a vast amount of data. Sometimes, data scientists get carried away by significant trends, and report these despite them not being the correct metrics for what they are supposed to be measuring.

3. Correlation does not mean causation

A fundamental principle in any statistics class, this is still sometimes forgotten by data scientists who give in to the temptation of sensationalising their findings. Finding that two parameters are statistically correlated does not mean that one causes another – rather, it could be a coincidence, or there could be an intermediate cause, or they have a common overarching cause. Data scientists should not jump to conclusions when reporting their findings, and instead find other means to explain the trends.

4. Not all data has to be stored permanently 

In the age of big data, you might feel like you need to keep every single piece of data. However, this only adds to the data clutter that you have, especially if these data are not useful for your purposes. The way to go about this is to store only essential data permanently. Non-essential data can be compressed and stored as statistical summaries rather than as raw data.

5. You can use the data for various purposes

Don’t get stuck in one area of data science. If you feel like you’re in a rut, remember that data science can be used for a multitude of purposes. For example, it is used in quality assurance, machine-to-machine communication, decision and process optimisation, and predictions, amongst many others. This is also helpful to remember if you are exploring specific areas of data science you want to specialise in, or if you are looking at job openings for data scientists in various industries.

6. Protect your data

For a data scientist, data is your livelihood, so guard it well. Especially for sensitive data, measures should be put in place to prevent unauthorised access and tampering. Criminal and business hackers are experts at stealing data and algorithms, but you should stay on top of the game by implementing secure systems so that you don’t suffer from a massive loss of revenue.

7. Use several sources for the same data 

Wherever possible, obtain multiple sources for the same metric. Differences in tracking methods across vendors can result in discrepancies between sources, even if it measures supposedly the same thing. Thus, to avoid skewing your data, having multiple sources will give you a better gauge of the actual numbers.

Apart from a burning passion for all things data, these tips will help you become a better data scientist. To learn even more about data science methods, you can enrol in a data science course in Singapore.