Better known as the startup nation, India is in the middle of a massive digital transformation, fueled by the phenomenal growth of tech startups. India’s startup story has seen an Up & Up phase for the last six years. When the use of data in business tends to increase, organizations are muddled to prepare data. And here comes the job of a data scientist.

What is data science?

Before we start, it is very crucial to know what is data science and different fields associated with it. Data Science is a science, is a pretty huge field.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured similar to data mining. However, no distinction was seen between data science and statistics by some critical academics and journalists. According to applied statistician Nate silver “data-scientist is a term for a statistician” .the future of data science not only exceeds the boundary of statistical theories in scale and strategies, but all current academia and research paradigms will be revolutionize by data science.

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Why Data Science?

One of the first questions to ask when hiring a data scientist for your startup is

How will data science improve our product?

Well simply our product is data, and therefore the goal of data science aligns well with the goal of the company, to build the most accurate model for estimating net worth. However, it will be very valuable to collect data of several facts about customer’s like their behavior, taste, etc. which further helps in improving products in the future. Building the right product is the key. To have a successful run in an area as new and evolving as analytics, it is important to have a clear anticipation of the acceptance and success of the product in the given demographic.

Some of the benefits of using data science at a startup are:

  • Identifying key business metrics to track and forecast.
  • Building predictive models of customer behavior.
  • Running experiments to test product changes.
  • Building data products that enable new product features.

How to learn data science?

While a fluent command over statistics, programming, ML models, etc. also plays an important role in being a competitive data scientists.

So to learn data science, one has to pick an end to end problem that is interesting and solve by learning the tools one would need on the way.

If you want to learn data science as a beginner you have to follow some steps:

  • Select the language in which you can do data analysis. It can be R or Python. Python is more demand able as well as trending as compared to R as one can also go for software development with the help of this but R can only used for Data Analysis,
  • After selecting the language you have to hone your statistical skills which you can easily find in any “Data Science Specialization”.
  • MATLAB can also be used for machine learning.
  • You are now ready for your first task and Kaggle: YOUR HOME FOR DATA SCIENCE is one of the best platforms for improving your skills as a Data Scientist.

The job of a data scientist?

A data engineer’s job is to help implement the data infrastructure. This involves logging and storing data, and writing and scheduling the batch jobs. The data engineer makes important technical decisions early on, such as whether to implement third-party solutions or build their own technology. Only when the data infrastructure is stable will hiring a data scientist to be productive.

A good DS must know about everything related to this field which also involves data cleaning and wrangling. Data cleaning involves quite a bit of judgment; it isn’t just a mechanical task. It also involves understanding how to replace dirty data, deal with missing data, and whether to omit outliers.

What makes a data scientist really effective is the ability to apply their domain expertise and leverage the tools to solve a real problem that impacts the business. They are able to break down a problem and ask the right questions in addition to answering them.

How does data science help in startups?

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Data science is responsible for helping financial measure, refine, and improve both the core technology and processes that we use to deliver the services to our customers. It will help to better understand our users and drive quality interactions and growth. In a startup, the infrastructure is built to ship data-driven features, and which features are best to build is what data science should tell. When decisions have to be made within several days, ideally, several hours, maintaining two teams and organizing smooth communication between them is unheard of luxury. Below are some ways that uses Big Data which helps your startup growth:-

  • Use Big Data to know your industry:-There are multiple sources of publicly-available data on the state of every market one can imagine. Companies post their quarterly reports, analytical agencies aggregate numbers into neat tables and indicate trends — there is an avalanche of information on anything you might want to ask. Once trained, this tool will be able to provide an on-demand snapshot of the current industry situation using server less computing services.
  • Use Big Data to know your competitors:-Your competitors offer their products and services and highlight their features, their customers leave feedback and highlight the flaws.
  • Use Big Data to know your customers:-Loyal customers are often included in major assets of the company. New approaches should be invented to win customer trust, which will prove a powerful support for brand and verbal promotion with time.

Conclusion

Although, one can allocate resources and efficient marketing moves, the Big Data provides an extreme help in adapting the rapidly changing environment. Data Science proves to be ultimate as and when you are being able to relate with real life situations. When those circumstances arises, this field will yield extremely. So ultimately, it brings a strong and comparative advantage over colleagues, new opportunities, and most importantly — more knowledge.

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