Data Science For Startups – 7 Ways to Boost your Data Science Startups

Data Science is highly in demand. There are a large number of Data Science Startups in the market. If you are planning to have one, then you have landed on the right place. You should have the proper knowledge of the various data science technologies that will help you to utilize the power of data. In this tutorial, we will give you a brief introduction to the different Data Science activities that you will need to perform for the management and growth of your startup. This will guide you how you can boost your startup with these tips for data science for startups.

Data science for startups

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Importance of Data Science for Startups

Data is an integral part of almost all the industries whether it be technical or non-technical. Starting from the healthcare industry to the manufacturing industry, Data Science is quite popular nowadays. Many large scales, as well as small scale industries, are using predictive modeling for planning their business strategies. There is an abundance of data available to large scale industries but in startups, Data Scientists have to develop the architecture from scratch.

Let us have a look at the various responsibilities that should be fulfilled in a Startups in Data Science.

1. Selecting the right team

A data scientist should have a number of skills. You need to have highly skilled professionals who have core knowledge of the field. A good team of Data Scientists will help you to get the most out of your data. Having experienced people will definitely help you but this is not always mandatory. The individuals in your team should be dedicated to their job. Always look for talented individuals who can play an important role in the growth of your startup with their innovative ideas.

2. Extracting the right data

Data is the main element in Data Science. For building any product related to data, you must first collect the right data. This is so important because if you do not have the right data to work with, even the best data science practices will not help you. Making sure that you have the data relevant to your problem is the key to your success. Let us understand this by an example.
Suppose you have a startup developing animated video games. You will need to estimate some parameters like:

  • How many users will open your website?
  • How many active users will use your website in one session?
  • What services do you need to provide them?
  • The type of customer support services any user may need, etc.

For understanding all these aspects, you will need the right data for analysis and this depends on the kind of startup you have. For performing the analysis, you can use event tracking on your website. Event tracking is a method of google analytics that helps you to keep a record of the user interactions with your website which helps you to analyze their behavior. The most common problem in the early stages of a Startup is the lack of data. This will help you to develop better products.

3. Building data pipelines

After the collection of data, you will need to process the data for extracting actionable insights and generating meaningful results. Efficient data processing is very important in the analysis of data. It is the most important part of your startup. Data pipelining is one of the important aspects of data processing. Data Scientists need to build a data pipeline to process and analyze the collected data. Databases like Hadoop and SQL are used to connect the data pipeline. All the data processing takes place in the database. The different properties that your data pipeline should have are:

  • Support the real-time processing and analysis of data.
  • Support versatile querying.
  • Able to handle large amounts of data.
  • Give alerts in case of any errors.
  • Should be scalable.

4. Evaluating the health of the product

Measuring the health of the product is also an important task of a Data Scientist. Analyzing the health of your product is very important in a Startup. The key measurements for evaluating the performance of your product are:

  • KPIs
    KPIs stands for Key Performance Indicators and measures the performance of data science products and startups. It evaluates how the changes made to your product or startup has affected its engagement, retention, and growth.
  • R for preparing reports
    You need to prepare regular performance reports for your product. R is the most popular language which helps in creating plots, etc. It also supports automatic report generation. This helps in reducing the workload of Data Scientists by eliminating manual report generation.
  • ETLs for transforming data
    ETLs means to extract, transform, and load. This will help you to transform the data from one form to another. The ETL processors enable you to process the raw data.

5. Exploratory data analysis (EDA)

After you set up your data pipeline, you will need to go thoroughly through your data to discover the insights to decide how you can improve your product. EDA will help you to understand your data, identify relationships in the data, and gain insights from it. Various methods of EDA are:

  • Summary Statistics
    It includes mean, mode, median, etc and will help you to understand the data better.
  • Data Plotting
    It provides you with a graphical overview of the data by making use of pie charts, line charts, histograms, bar-plots, etc. In this technique, normalized data will help you to produce better results.
  • Identifying the correlation of features
    You can compare the various features of your dataset to detect the correlated features.
  • Identifying relevant features
    Identifying important features is an important aspect of data analysis. How a feature affects some other feature, how it affects the result, how much information it will give, all these factors decide the importance of any feature.

6. Predictive Modeling

The development of predictive models helps you to predict customer behavior. The recommendation system is the biggest example of predictive modeling. As a startup, predictive modeling will help you to identify what kind of products your users will like. It will help you to make optimal choices for the success of your startup.

7. Search for Supportive Investors

Investors play an important role in the growth of your startup. Supportive investors can motivate you and guide you in various ways. Some investors might prefer an experienced team of Data Scientists but you need to find the investors who understand you and your startup.

Now when you have learnt ways to boost your data science startup, check real time use cases of data science to get more details.


In this tutorial, we have seen the various aspects of Data Science for startups. If you are also an aspiring Data Scientist and planning for a startup, then this tutorial will definitely help you.

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