Data-driven innovation: Machine Learning & Data Analysis

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Data-driven innovation is trending. Data-driven innovation forms a key pillar in this century. The confluence of several trends, including the increasing migration of socio-economic activities to the Internet and the decline in the cost of data collection, storage and processing, are leading to the generation and use of large data sets that are becoming a core asset in the economy, fostering new industries, processes and products and creating significant competitive advantages.

Many of these data-driven solutions have shown benefits such as product quality, customer satisfaction, better financial performance, reducing environmental impact and increasing workforce diversity.

One of the key aspects to solve a data-driven problem is related to the operational challenge that will constitute the hypothesis for using data. At Apiumhub we believe in a data-driven approach that discover what companies really need to help make strategic decisions, so we would like to present a few techniques.

Machine Learning Algorithms

Machine Learning (ML) algorithms improve automatically through experience and by the use of data, in order to make predictions or decisions without being explicitly programmed to do so. In its application across business problems, machine learning can be also referred to as predictive analytics.

Machine Learning can help you:

  • Identify trends and patterns
  • Automate algorithms
  • Gain algorithm accuracy and efficiency
  • Handling Multi-dimensional and multi-variety data
  • Build wide applications

Here you can find a Machine Learning challenge proposal, based on an occupation prediction model aiming to solve the question ‘How much days might my AirBNB property be occupied based on the price I might charge per night as a host?

  Scoping Data Science Projects
Screenshot 2021 07 07 at 22.33.59

Data Analysis Methods

Data analysis cover a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Data analysis can help you:

  • Anticipating needs
  • Mitigating risk
  • Delivering relevant features
  • Delivering personalized information
  • Improving user experience

Here you can find a Data Analysis challenge proposal, based on COVID-19 herd immunity model using Prophet aiming to solve the question ‘When will the country reach herd immunity?

Screenshot 2021 07 07 at 22.38.23

Helping to build a Data-Driven Culture

At Apiumhub we believe in data science projects that discover what companies really need to help make strategic decisions based on data analysis. We believe that companies can leverage data-driven product strategies that lead to differentiation and competitive advantage.

I hope you found this article useful, if you have any questions regarding data-driven projects, send us a message, we would be happy to help, as this is our expertise!

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