Data science & startup – It’s a match

5Feb - by aiuniverse - 0 - In Data Science


Data Science has taken the world by storm in the past decade. It is high time for everyone else to catch up or be left in the dust. Data science has become an important tool for the majority of top global organisations. Irrespective of the type of industry they belong to, Data Science is crucial for all the organisations and companies right now, from Google or Amazon.

Another popular trend in the past few years is startups. Startups have been popping up with increasing frequency in the past couple of years and these trends don’t seem to be slowing down any time soon. Establishing and running a startup business is becoming a more and more commonplace dream.

Can one of these trends help the other? The answer is a resounding yes of course. Data science plays an important role in any company’s success these days and the same goes for small startup industries as well.

  • How to maximize the output of a small data science team

Why data science?

We know what is Data Science. It helps in understanding how to run your business. It helps in strategizing and planning your business venture’s path. It also helps in improving products by collecting data about customer behaviour. Deploying data pipelines & Machine Learning models in the production process results in the improved running of the business.

How data science can nourish startups? (Data science lifecycle for a startup)

  • Data extraction & tracking

The relevancy and accuracy of the collected data are highly important to get relevant and correct insights using data science. Various parameters need to be considered when collecting the data. Collecting the correct data is of utmost importance. So, collecting the right data is the key.

  • Building data pipelines

After data collection, proper cleaning and then the analysis is required. The results will help immensely in decision making and in making improvements to your products and services. A data pipeline, when implemented and used properly, can be a powerful insight generator.

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  • Analysing health of the product

The health of a product is a measure of its performance. Analysing data metrics related to the health of the product is the most important step in establishing your data pipeline. The results of such analysis help to communicate the idea about product performance and also gives ideas on improving it at the same time.

  • Exploratory data analysis for your product

Exploratory data analysis gives you a deeper comprehension of your data and enlightens the hidden relationships and patterns between different features and variables in the data. It is the next step after establishing your data pipeline. It shows what factors may or may not be affecting your product’s performance.

  • Developing predictive models

Predictive models help in forecasting user behaviour. This enables you to customize your product and services based on your users’ behaviour and preferences. Predictive models are developed using machine learning techniques and can significantly improve business decision making.

  • Experimentation to make better products

Predictions are not always accurate and deriving the correct meaning from different patterns is a skill that will improve with experience. This just means that you need to experiment with different techniques data and combinations to find out the perfectly suited configuration for your needs.

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  • Netflix ― A case study

Netflix is a name that everyone has heard of all over the world. It is the most popular content streaming service. The biggest reason behind its success is its media recommendation system. Netflix keeps a record of who watches what at what time. This helps them find patterns in a person’s viewing history. This helps their recommendation system decide what media should be recommended to which user. But Netflix wasn’t always at this point. It was also a startup once.

Netflix started as a DVD rental service. They would take orders via telephone and deliver physical DVDs by post. This model was rapidly failing and was going to take the company down.

In the early 2000s, Netflix started investing in data science research and built an online streaming service that was released in 2007. The convenience of watching any movie and TV series at your home whenever you want was and still is their biggest selling point. But, the lead start of the show, was their recommendation engine that would suggest similar media or even give recommendations based on a user’s viewing history.

  • Twitter- A case study

Twitter invests in and researches the field of data science quite extensively. Twitter uses data science in two different ways. These are:

  • Analysing: This is a static approach to data science. It relies on descriptive statistics to analyse and describe data and its features more thoroughly.
  • Building: This type deals with programming and fundamentals of software development. This service can be used to make interactive outputs to users.

Twitter’s analysis team classifies tweets and posted media correctly. While the building team develops machine learning models to train on these collections tweets. This also helps them in recommendations, improving their search results, etc..


For all the people out there, who plan to start a business of their own, data science is a must-have tool and a possible key to success for your future venture. Data science is sure to ease your burden and make your journey as an entrepreneur much easier.

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