The Most Important Lessons Learned From Data Science Projects

An overwhelming expansion of data archives posed a challenge to various industries, as these are now struggling to make use of such enormous amount of information. Almost 90% of all data ever recorded worldwide has been created in the last decade alone. This clearly suggests that administrations and companies that adopt contemporary data science analysis can expect business expansion and higher revenues.

For instance, data scientists calculated that the U.S. healthcare system could save $300 billion annually through an adequate implementation of data science principles. Today, it is the mandatory precondition for all stakeholders to analyze data and use those conclusions to generate growth. Data science is attracting more and more young minds, so we've decided to give you a first-hand overview of the most important lessons learned from data science projects.  

Data Science in Practice

The biggest advantage of data science over traditional statistics is that it can draw conclusions from a junk pile of the supposedly unrelated information. That’s how sports analysts were able to find the most precise performance indicators and help teams prepare for their games. For example, the Strategic Innovations Group utilizes game stats to calculate and predict the MLB pitchers’ throws at almost 80% accuracy. At the same time, attractive tourism destinations use data science tools to improve their offers, while Trip Advisor calculates all sorts of more or less relevant factors (hospitality, climate, beds, welcome notes, decoration, etc.) to set the prices. 

Lessons Learned in Data Science Projects

John Fetcher, an experienced analyst at EssayGeeks, states that once you find yourself in a position to apply data science knowledge in real-life industries, it can catch you underprepared.

Here is what you need to know before the project starts:

Understand Business Goals

You, as the data scientist, will play with algorithms and use your knowledge creatively to develop the statistical model. However, different businesses demand alternative approaches and different results, eventually. So, your task here is to understand the nature of this business: What are the targeted goals? Do they aim at purchasers’ engagement, at cost optimization, or simply at higher revenues? Your work will depend on these variables and the overall result is good only if you got them all correctly.

Big Data Doesn’t Mean All Data

Every now and then, clients want you to gather all kinds of information from all types of data sources. In their opinion, more data must lead analysts to better and more precise business indicators. That’s why they are delighted to allocate huge budgets to data infrastructure. Though the logic is clear, it is wrong more often than not! Don’t bother with huge datasets simply to indulge your clients. Use relevant data samples instead – results will be the same, while costs will be much lower.

Collaboration

You cannot conduct big data analysis without knowing the essence of the business. The same applies to company executives, managers, and marketers, as they cannot maintain high performance without data science. Therefore, don’t work alone. Be cooperative and collaborate with all of the parties involved to achieve the best results. 

Practice Makes Perfect

Some projects will burden you more than others. If you find yourself stuck in uncertainty and struggling to find inspiration, practice with inspiring data science projects to improve your skills. We suggest projects like the Black Friday, Census Income, or Chicago Crime Data Set.

Conclusion

No one can deny the importance of data science anymore. It is only a matter of time when all renowned companies and even state administrations will embrace it to boost efficiency and productivity. In such circumstances, data science profession will flourish, so make sure to keep up with the latest trends. It will bring you the comparative advantage over colleagues, new opportunities, and most importantly — more knowledge.