As our planet invades into the era of big data, the need for ever-expanding storage also needs to be widened up. It was the main reason for dispute and worry for all the Information Technology industries till mid-2010. It was then when Hadoop and other cloud-based storage software stepped out. But now the focus had been shifted towards processing of this data. The stage was set for Data Science to step out.


So, what is Data Science?


In layman words, Data Science is something which involves some programming skills, some statistical appearance, and some visualization techniques but yes, a lot of business sense. Data Science is a whisk of various tools, algorithms, and machine learning principles with a objective to discover hidden patterns from the raw data.

  • Data science is not just a software engineering or development piece of work. That is, data science is not about constructing products or systems or any related artistry commodities.
  • Data science is not only a visualization piece of work. Creating some cool visual is neither the end goal nor the beginning part of how a data scientist works.
  • Data science is not only about statistics. Statistical knowledge alone doesn’t make a person qualified to be a Data Scientist.

But yes, It is a perfect blend of all the above-mentioned skills. A Data Scientist is a person who connects all the dots between the business world and the data world scientifically.

Data Science Lifecycle_2 Data Science Lifecycle_1


But, Why Do We Need Data Science?

  • Traditionally, the data that we had was mostly structured and small in size, which could be analyzed easily by simple tools. Unlike data in the traditional systems which was mostly structured, today most of the data are unstructured. By 2022, more than 75 % of the data will be encrypted and unstructured.
  • Doesn’t it sound great if we can understand the requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age, and income? No doubt we had all this data earlier too, but now with the vast amount and variety of data, we can train models more effectively and recommend the product to our customers with more precision.
  • Let’s take a different scenario to make it more clear. How about if our car had the intelligence to drive us to our home? The self-driving vehicles collect live raw data from sensors, which includes radars, cameras, and lasers to generate a map of its surroundings. Based upon this data, it takes its own decisions like when to increase speed , when to decrease speed , when to overtake other vehicles, where to take a turn – making accurate use of machine learning algorithms.


So, what is Machine Learning?



Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion in a synchronized way, by providing them data and information in raw format and allowing them to interpret and parse the data intellectual way meaningful manner.

Machine Learning is the practice of using algorithms to interpret data, learn from it, and then make a diagnosis or prediction about something in the world.

Machine Learning enables the machines or computers to act and make data-driven decisions rather than being straight-forward programmed in order to carry out some certain task. These programs or algorithms are designed in a way to learn and improve over time when exposed to new set of data.


Looking around us, we may find various examples or implementations of machine learning, such as Tesla’s self-driving cars, Apple’s Siri, Sophia AI Robot and many more. The iOS developers at Let’s Nurture have developed Artificial Intelligence skills on Ride-sharing App using Apple’s Siri.


Data Science and Machine Learning:



Machine Learning is extremely important for data science. Many data products in day to day life are predictive based on past raw data values. These are clear tasks for Machine Learning. Machine Learning is a analytic part of data science, asking about the importance of Machine Learning in Data Science is a bit like asking how important Mumbai is to the state of Maharashtra.

Machine Learning has such as high rank in data science that the common view of a data scientist is someone that uses big data technologies to create pipelines that feed machine learning algorithms.

Data science take it further with machine learning. You build a predictive model so that when we have new data with an unknown outcome, we can predict the likely outcome and automate the downstream process.

Let’s say we work for a credit card company. We analyze credit card fraud in the historical data and report your findings to senior management.

That’s data analysis in Data Science.

We can also build a predictive model from the data you analyzed with machine learning, which flags likely fraudulent transactions as they come in and route them to call center for follow-up.

That’s data science with machine learning.

DS and ML


Our Contributions:


The Python Developers, Embedded Engineers along with the professional Mobile Application Developers at Let’s Nurture are giving their best every day on implementing Artificial Intelligence and enhancing Machine Learning on their built products. Professional by skills yet curious by nature they learn, they implement and they conquer whatever comes on their way.

Want some information on our built products where we have implemented Machine Learning with data analytics and data science, feel free to drop us an inquiry. We are always ready to help : )

“Curiosity leads to certainty, and certainty leads to Development!”

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