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An Untapped Resource for Machine Learning

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Machine Learning: Data science combines several fields, including statistics, scientific methods, artificial intelligence (AI), and data analytics, to extract value from data. People who practice data science are called data scientists and combine various skills to analyze information collected from the web and other media sources to derive change and maximize revenue.

Data science is an exciting field these days. Why? Because companies have a treasure trove of data. Modern technology enabled the creation and storage of a gigantic amount of information, which has caused the volume of data to skyrocket. It is estimated that 90% of data in the world was generated in the last two years.

This data is often found in databases and data lakes without being exploited. The wealth of data collected and stored by these technologies can bring transformative benefits to businesses and societies worldwide, but only if we can interpret it. This is where data science comes in.

Data science reveals trends and provides information for companies to use, make better decisions, and create more innovative products and services. And perhaps most importantly, it allows machine learning models to learn from data rather than trusting professionals to see what they can make from the data.

Data is innovation’s proverbial food, but its value comes from the insights data scientists can tap into and then act on. Businesses use data science to improve business products and services, thus giving them a competitive advantage.

Use cases for data science may include:

  • Calculate customer churn by analyzing data collected from call centers, so marketing can take action to retain them
  • Boost efficiency by analyzing trends
  • Improve diagnosis by analyzing data from medical tests and symptoms so doctors can detect diseases earlier and treat them more effectively
  • Equipment failure predictions
  • Find wrong-doing in financial services by identifying abnormal behavior
  • Improve sales by creating recommendations based on previous purchases

Many companies prioritized data science invested heavily in this area. IT managers usually consider these technologies the most strategic for their business and are investing accordingly.

Creating, evaluating, deploying, and monitoring machine learning models can be complex. This is why the number of data science tools has grown. Data science uses many tools, but most commonly open source notebooks, web-based applications for writing and executing code, visualizing data, and viewing results, all in the same environment.

Jupyter, RStudio, and Zeppelin are some of the most popular software. Notebook software is beneficial for performing analysis, but it has limitations when data scientists have to work in teams. Data science platforms have been developed to solve this problem.

To sum up

To help you find which data science tool is right for you, it is essential to ask yourself the following questions: What types of languages do your data scientists use? What methods do they prefer? What kind of data sources do they use? Some customers prefer an independent data source service with open source libraries. Others like the speed that machine learning algorithms provide the database.

 

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