Do It Without A Data Scientist

For those who don’t know, data science is multidisciplinary, having a blend of statistical (data) inference, algorithm development, and technology. Today, data science has been used to solve a wide range of analytically complex issues.

Numerous businesses and companies across the globe have adopted data science to leverage robust mathematical techniques and improve decision-making skills in the workplace.

The brands like Airbnb and Amazon are good examples that assimilated data science into their services and products. It’s worth mentioning that data science helped them get a competitive advantage. There has been so much achievement that now every industry and department leveraging data science to help make the right decisions.

In fact, it would not be wrong to say that data science is going to be the best career of the 21st century. At Strata Scratch, we help you build a career in data science. We help you enhance your analytical skills by offering you over 40 + problem sets of SQL and Python. We have more than 80 datasets and guides to improve your analytical skills.

Although there a number of tools available in the market that promise to help you with analytically complex issues, data science is something that you can’t buy as software. You may use this software to assist you but you must have a sound knowledge of data science as well.

Strata Scratch is an ideal platform to learn data analytical skills and become a data scientist. Whether you are an aspiring data scientist, student or professional who has to deal with a large amount of data every day, we can help you learn basic data programming.

It is true that the use of software reduces the need for hiring in-house analytical experts. But it is not going to be as effective as it should be for organizations where data science is the competitive advantage of the product.

Here is why it’s not possible to automate data science completely and still expect a positive ROI. The reason is data science is 80% data cleansing and 20% model building.

The initial steps of data cleansing are quite easy and can easily be automated. However, the next steps are more complicated and need deep domain knowledge of your industry. After all, it often involves dealing with feature section and formation of new proxy metrics that characterize your business.

The rest 20% of data science is building the model using mathematical equations. It is you who choose the building model according to your business’s issues. But once you have implemented your model, how would you know that if the output is good or not in comparison to other approaches?

Consequently, the human component is important in data science. After all, it ensures that the data model best represents your business.

You should invest in a product that helps you save time on low-value tasks but it is also true that you shouldn't’ completely rely on them to make high-impact decisions.

Strata Scratch lets you practice basic and advanced SQL and Python and improve your marketing skills. If you want to pursue a career as a data scientist, you may signup for Strata Scratch and start learning today!