The Julio Blog

Data science is a field that uses scientific methods, algorithms, and data to gain insights for business. But that can be daunting! So I will explain it in the form of making the perfect juice! The process of data science can involve three (or more) stages.

What is Data Science?

• Stage 1: Gather the Data
• Stage 2: Cleaning and Preparation
• Stage 3: Modeling and Conclusions

There is a lot of advancements in the way we use data. With the rise of public AI tools, now is an amazing time to learn simple ways you too can use data.

Stage 1: Gather the Data

Starting with data collection, pretend data points are like the ingredients that we need for the recipe. The type of data you need depends on your goal. If your goal is to understand customer sentiment on your Instagram Business Account, then it makes sense to gather data on your customers such as demographics, geolocation, and their comments per post.

Data can be collected from public sources or your own company analytics. With statistics, mathematics, and expertise in our area, we will be able to draw meaningful conclusions… or in our example case, a delicious juice!

Stage 2: Cleaning and Preparation
Next is cleaning and preparation. This rids of irrelevant parts of the foods that you may not want to include in the final recipe(No one likes munching on seeds unless you’re a psychopath). Sometimes data scientists want to combine datasets, fill in missing values, and perform exploratory analysis to ensure data in accurate, consistent, and there are no outliers that will affect the final product.

Stage 3: Modeling and Conclusions

Finally follows feature engineering, modeling, and communication of insights. This can mean changing up data ingredients and tasting the juice to make the perfect combo. Besides technical skills, data scientists also need to present insights from the data. Having the soft-skills of critical thinking and creativity helps. When presenting conclusions, you want to make numbers appealing. Put the data into charts or graphs. No one will buy a juice that looks the color brown. That is why we only see greens, reds, and other bright color juices out there. There is also validation. Testing your models can prove an incorrect or successful insight. Successful insights then build decision strategies and a successful business. 

Data science has many field applications such as business, sports, and social media. For example, businesses can analyze customer behavior, predict demand, and optimize finances. In sports, data science analyzes player performance and game strategies. Good data scientists will learn new trends, techniques, and tools to stay effective.