The Role of Machine Learning in Data Science

The Role of Machine Learning in Data Science

Summary

Data Science is a field that studies data and employs a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. That knowledge then gets applied to businesses, government, and other bodies to assist in driving profits, innovating products and services, building better infrastructure, public systems, and more. 
 
But, what is the role of Machine Learning in Data Science? How are these domains related? Keep reading to find out.

Data Science is a field that studies data and employs a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. That knowledge then gets applied to businesses, government, and other bodies to assist in driving profits, innovating products and services, building better infrastructure, public systems, and more.

Data Science combines multiple fields like scientific methods, statistics, data analysis, and AI to extract intelligence from data. Data scientists and data engineers combine a variety of skills to analyze and collect data from the web and other sources, such as customers and smartphones to derive actionable insights.

Importance of Data Science

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In the current scenario, data is valuable, and it needs to be read and analyzed. Data Science deals with a tremendous amount of data to detect different and unseen patterns, derive information, and make business decisions. 

Below are some reasons that show the importance of Data Science:

1. Discover unknown meaningful patterns

Data Science allows businesses to find new patterns and relationships. With this, we  can reveal new insights from it. These insights will allow us to make meaningful inferences and decisions.

2. Helps in developing new products and solutions

With the help of Data Science, we can reveal gaps and problems that would otherwise go unnoticed. It helps in acquiring greater insight into purchase decisions, customer feedback, and business processes that can drive innovation in internal operations and external solutions.

3. Real-time prediction and optimization

Real-time prediction can be made with the help of Data Science. The importance of Data Science lies everywhere.

What is Machine Learning (ML)?

Machine learning (ML) is a field of artificial intelligence (AI) that allows the software to learn from data to identify patterns and make predictions automatically with minimal human intervention. Machine learning derives meaningful information from large volumes of data by using several algorithms to identify patterns. 

Data Science vs. Machine Learning
 

DATA SCIENCE MACHINE LEARNING

Data Science is all about processes and systems to extract data from structured and semi-structured data.

Machine learning (ML) is a field of artificial intelligence (AI) that allows the software to learn from data to identify patterns and make predictions automatically with minimal human intervention.

Data Science deals with data. Machine learning utilizes past experiences to learn about the data.
Data gathering, manipulation, Data cleaning, etc.,  are data science operations. ML is of three types: Supervised learning, Unsupervised learning, and Reinforcement learning.

Example: Netflix using data Science is an example of this technology. 

With the help of Data Science, Netflix can provide users with personalized recommendations on movies and shows. It can also predict the original content’s popularity with trailers and thumbnail images. 

Example: Facebook using ML is an example of this technology. 

With the help of Machine learning, Facebook can produce the estimated action rate and the ad quality score, which is used for the total equation. ML features such as facial recognition, targeted advertising, language translation, and news ed are also used in many real-case scenarios. 

 

Why is Machine Learning Important?

Data is meaningless until it's converted into valuable information. Machine learning can be used as the key to unlock the value of corporate and customer data and enact decisions that keep a company ahead of the competition.

Machine learning and Data Science are hence two sides of a coin without which Data Science operations are unachievable. Data Scientists must grasp Machine Learning knowledge for accurate forecasts and estimates.

There are a lot of instances where machine learning can be applied in order to cut costs, mitigate risks, improve the overall quality of life, including recommending products, detecting cybersecurity breaches, and enabling self-driving cars. 

The Role of Machine Learning in Data Science

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Data Science is all about generating insights from raw data. This can be achieved by exploring data at a very granular level and understanding the trends. Machine learning finds hidden patterns in the data and generates insights that help organizations solve the problem.

The role of Machine learning in Data Science comes into play when we want to make accurate estimates about a given set of data, such as predicting whether a patient has cancer or not.

The role of machine learning in Data Science occurs in 9 steps:

1. Understanding the Business Problem

To build a successful business model, it’s very important to understand the business problem that the client is facing. Suppose the client wants to predict whether the patient has cancer or not. In such scenario, domain experts understand the underlying problems that are present in the system.

2. Data Collection

After understanding the problem statement, you have to collect relevant data. As per the business problem, machine learning helps collect and analyze structured, unstructured, and semi-structured data from any database across systems.

3. Data Preparation

The first step of data preparation is data cleaning. It is an essential step for preparing the data. In data preparation, you eliminate duplicates and null values, inconsistent data types, invalid entries, missing data, and improper formatting. 

4. Exploratory Data Analysis (EDA)

Exploratory Data Analysis lets you uncover valuable insights that will be useful in the next phase of the Data Science lifecycle. EDA is important because, through EDA, you can find outliers, anomalies, and trends in the dataset. These insights can be helpful in identifying the optimal number of features to be used for model building.

5. Feature Engineering

Feature engineering is one of the important steps in a Data Science Project. It helps in creating new features, transforming and scaling the features. In this domain, expertise plays a key role in generating new insights from the data exploration step. 

6. Model Training

In Model training, we fit the training data; this is where “learning” starts. We train the model on training data and test the performance on testing data i.e., unseen data.

7. Model Evaluation

Once Model Training is done, it’s time to evaluate its performance. So, evaluating your Model on a new dataset will give you an idea of how your Model is going to perform in future data.

8. Hyperparameter Tuning

After the Model is trained and evaluated, the performance of the Model can be again improved by tuning its parameter. Hyperparameter tuning of the model is important to improve the overall performance of the model.

9. Making Predictions and Ready to be Deployed

This is the final stage of machine learning. Here, the machine answers each of your questions by its learning.  After making accurate predictions, the Data Model is deployed into production.

Future of Data Science

Data scientists are responsible for analyzing large volumes of data to extract insights that can be used to solve problems. Data Science is everywhere, including  retail, health care, business, and education. Data quality plays an important role in maintaining the relevance of machine learning. As the world becomes more digital and data is generated at a fast pace, the demand for Data Science is growing. Artificial Intelligence is another essential aspect in the future of Data Science. In real-world situations, artificial intelligence will use automated solutions to screen through enormous amounts of data to uncover patterns that aid businesses in making better decisions.

Conclusion

Data is critical for us. While considering why Data Science is becoming more essential, we must consider the fact that the value of data is increasing.

Data Science and Machine learning work together to give valuable insights in some real-life scenarios — online recommendation engines, speech recognition (in Siri and Google Assistant), and detecting fraud in all online transactions. Thus, Data Science and Machine Learning will emerge as two of the most essential technologies in the near future.

In essence, there will be a surge in the number of Data Science job openings across the globe. That is, it is the right time to get started in the field and learn the in-demand skills. If you are looking to learn Data Science hands-on, and maximize your chances of getting a high-paying Data Science job, apply to OdinSchool's Data Science Bootcamp!

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