In 2019 the market size of Machine Learning has been found to be $2.40 B and is continuing to grow with the 44.9%. While the Data Science platform has expected to grow from $ 37.9 B in 2019 with a CAGR of 30%.
Wherever see, we find data existence everywhere. To make the sense of the real world the data is a crucial notch to consider.
Data is a valuable asset for any organization. To take decisions or predict future trends, the data helps the organization to do wise transformations.
Know more: AI vs. Machine Learning vs. Data Science
Data Science, followed by Machine Learning, poised the importance of data that we can’t deny in 2021. Data Science (DS) and Machine Learning (ML) both hold a sistership relation.
In this exclusive guide, we brief you more with a basic understanding of data science and machine learning solutions. And then introduces you to the differences between them.
So, Let’s begin!
As far as data become more intricate, so does the definition of data science. Data science is the study of unsorted data which is of no relevance to anything.
Data is in bulk in the industries but with no value. There is no hierarchy in the data that means it is unstructured and useless. Industries like machine learning service providers obtained enormous amounts of such data on daily basis an urge for storage solutions and simplification. Like;
- Google process 20 Petabytes of data per day.
- Facebook has processed 15 Terabytes of data per day.
- eBay process 15 Petabytes of data per day.
To simplify such large data, here comes the role of data science.
The notion of data science tells us that it is the curation of data that is widely available. The data scientists select the data, prepare it and do the analysis. They have studied such data, to get a greater extent of valuable information about the businesses or market patterns to give an edge to the competitive market.
Learn more: What are the best programming languages for AI?
For analysis and accuracy, data science tools are specially used.
In short, Data is in raw format, data science help to break into the simpler and structured form using predictive modeling.
Use of Data Science:
- Weather prediction
- Security prediction
- Chatbots and voice assistants like Alexa or Siri
- Analyze the suggestions on YouTube, Facebook, and other popular platforms
- Face recognition
Machine Learning is the subset of AI (artificial intelligence). With data science, we get useful information using data analysis. Now, with machine learning, we examine the correct information with it.
The machine learning solution providers usually estimate the accuracy of predictions can be done with various analytical tools. They analyze the chunks of data and train the machines or artificial brains using the automated process of modeling in the study of machine learning.
The main goal of machine learning is to allow the systems to learn automatically without any human intervention, learn from the processed data.
For example: we have to distinguish between an object say a cat or dog, machine learning algorithms help the artificial brain of the machine. Supervised learning help to retrieve and trace the pixels or peak points of the object and pass it into the algorithms to find out the exact result. This can’t be done in a single day. The predictive models are trained a number of times to determine the correct result.
Another example: voice assistants can understand and distinguish among a number of different voices using NLP (natural learning programming). YouTube, Wynk, Amazon, and Google all have their voice searching tool to understand the voices.
Netflix uses machine learning to understand the preference of their users to improve their recommendation lists and serve better suggestions to the users.
Data Science Vs Machine Learning
Data science and machine learning have different roles while working on data. We need to understand how do they differ and do they have any mutual dependency? Let’s pin-out something more!
- Focus on extracting the data to perform decision-making and analysis.
- Deal with Large datasets and make their subsets.
- Use algorithms, statistics, maths, data wrangling, and data analysis.
- Useful for the larger organizations for training their different projects.
- What is the use of data comes after the modeling or analysis? This is an important aspect for a machine learning solutions company to consider.
- Various algorithms examine the data sets using the tools.
- Handle the AI applications using the tools like model interpretability, and bias detection.
Let us understand more with a simple example of a self-driving car.
Machine Learning: a self-driving car has millions of datasets of images with streetside objects like stop signs and other objects. The car has no brain, but using machine learning techniques the car can now able to make decisions. The model has been trained using different algorithms.
Data Science: is the technique used for predicting whether the decision is taken in the right way or not. Once the data is analyzed the scientists get to know the results. In the normal condition, failure of modeling is happening at the night not in the daytime. Add some night images to stop the running wheels in the night and back the data for testing.
We can’t deny the mutual importance of both data science and machine learning. Both are super dependent.
In the era of the 21st century, names Data Science and Machine Learning have globally searched terms. Most of the high-tech companies Netflix, Facebook, Amazon, etc. are driving their resources and services under these two.
In the future, many more new sources of data are kept on adding and expanding the fields of data science and machine learning. The global brand, Tesla is working extensively well with the support of machine learning and artificial intelligence.
If you’re a statistics master or bachelor’s in computer or a tech prodigy, it’s high time for you to learn these two fields and embark your knowledge to more level.
For the organization, you can take the advantage of a DS and ML development company to align your product’s generosity with the future.
Keith Laurance is a technical content writer who has been working with the machine learning solution providers at Octal IT Solution. Over the years she has researched about machine learning solutions services and promises to deliver the most reliable solutions. Other than researching tech-related queries, she loves to eat and read books. You can always find her in the nearby market buying quirky elements for her super cozy place.