Data Science for Startups

2023-11-13

Article by Cactus.cloud based on Ben Weber

Introduction to Data and Science as Such.

As a startup, we have discovered and learned about the power of data, the importance of its management, and the opportunities provided by the cloud. We found this article on data science by Ben Weber, Director of Applied Data Science at Zynga. We have translated and adapted the article in relation to Cactus.cloud because we believe it is crucial to understand data science and its scope, especially in startups. We plan to create a series of articles related to the use of data in startups.

“Why Data Science and How to Build Your Technology Team?”

One of the first questions when hiring a data scientist for your startup is: how will data science improve our product? In many companies, the product is data, and therefore the goal of data science aligns well with the company's goal: to build the most accurate model for overall success.

"In the early stages, it is often beneficial to start collecting data on customer behavior to improve products in the future" (Weber, 2018). Data says it all; it shows the behavior of our customers, and in a startup, effective management is essential. With Cactus.cloud, you find a strategic ally to improve your productivity and the effective handling of your resources.

Some advantages of using data science in a startup include:

• Identifying key business metrics for tracking and forecasting.
• Creating predictive models of customer behavior.
• Conducting experiments to test changes in products.
• Building data products that enable new product features.

Many organizations get stuck in the first two or three stages of data utilization and do not leverage the full potential of this tool. The goal of this article is to show how managed services can be used so that small teams go beyond data pipelines to simply calculate performance metrics for the company and transition to an organization where data science provides key information for product development (Weber, 2018).

This article provides Weber's motivation for using data science in a startup and encourages an overview of what can be achieved with cloud data and what can be accomplished with Cactus.cloud. In subsequent articles in the series, we will address topics related to:

• Data tracking: Analyzing the motivation to capture data from applications and websites, proposing different methods for collecting tracking data, introducing concerns such as privacy and fraud, and presenting an example with Google PubSub.
• Data pipelines: Presenting different approaches to collecting data for use by an analysis and data science team, analyzing approaches with flat files, databases, and data lakes, and presenting an implementation using PubSub, DataFlow, and BigQuery. Other similar articles include a scalable analysis pipeline and the evolution of game analysis platforms.
• Business intelligence: Identifying common practices for ETLs, automated reports/dashboards, and calculating run-the-business metrics and KPIs. Presenting an example with R Shiny and Data Studio.
• Exploratory analysis: Covering common analyzes used to delve into data, such as building histograms and cumulative distribution functions, correlation analysis, and feature importance for linear models. Presenting an analysis example with the Natality public dataset. Other similar articles are clustering the top 1% and 10 years of data science visualizations.
• Predictive modeling: Analyzing approaches for supervised and unsupervised learning, presenting predictive models of turnover and cross-promotion, as well as methods for evaluating model performance offline.
• Model production: Showing how to scale offline models to score millions of records and analyzing batch and online approaches for model deployment. Other similar articles include Producing Data Science at Twitch and Producing Models with DataFlow.
• Providing an introduction to A/B testing for products, explaining how to set up an experimentation framework to run experiments, and presenting an analysis example with R and bootstrapping. Other similar posts include A/B testing with staged rollouts.
• Recommendation systems: Introducing the basics of recommendation systems and providing an example of expanding a recommender for a production system. Similar entries include prototyping a recommender.
• Deep learning: Providing a brief introduction to data science problems best addressed with deep learning, such as labeling chat messages as offensive. Offering examples of prototyping models with the R interface for Keras and production with the R interface for CloudML.

To make data-driven decisions in a startup, you must collect data on how your products are used to understand. You must also be able to measure the impact of making changes to your product and the effectiveness of running campaigns, such as implementing a custom audience for social media marketing (Weber, 2018). Once again, data collection is necessary to achieve these goals, a topic we will touch on in the next article, as we discuss the importance of data tracking.

REFERENCES:

https://towardsdatascience.com/data-science-for-startups-introduction-80d022a18aec