Difference Between Machine Learning and Data Science
Machine learning is the process of training computers to learn how to perform tasks automatically. The computer learns through experience and feedback.
Data science is the application of statistics and mathematics to analyze large amounts of data. Data scientists use statistical methods to extract useful patterns and trends from data.
What Is Machine Learning?
Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed.
This means that machines can analyze massive amounts of data and draw conclusions based on patterns found in the data.
For example, a computer may be trained to recognize faces in images. Once the computer learns to identify faces, it can then be taught to identify other objects such as cars and buildings.
How Does Machine Learning Work?
Machine learning algorithms are designed to find patterns in data. These algorithms are often referred to as “neural networks.”
Neural networks are made up of nodes that perform simple operations on data. For example, one node may add two numbers together, while another node may subtract a number from another.
These nodes are connected to each other via links called “synapses.” Each synapse controls the flow of data between two nodes. The strength of a synapse determines how strongly it connects its two nodes.
When data flows into a node, the node performs an operation on the data and sends the result back out along the link. This process repeats until the desired results are achieved.
The beauty of neural networks is that they can adapt to different situations. They can learn from examples and use that knowledge to predict future outcomes.
Why Use Machine Learning?
There are many reasons why companies choose to implement machine learning.
Some of the most common uses include:
Customer service – A chatbot could answer customer questions about products or services.
Fraud detection – Credit card fraudsters sometimes try to trick credit card processors by creating fraudulent transactions.
Image recognition – An algorithm could detect whether an image contains a person or object.
Stock trading – Algorithms could automatically buy and sell stocks at the right times.
Translation – An algorithm could translate text from one language to another.
Voice recognition – An algorithm could transcribe audio recordings into written words.
Video games – An algorithm could play a game against human players.
Recommendations – An algorithm could recommend movies, music, books, or restaurants to users.
Weather forecasting – An algorithm could forecast weather conditions.
Speech recognition – An algorithm could convert speech into text.
Text mining – An algorithm could analyze large amounts of unstructured data (like social media posts) to find useful information.
User profiling – Companies could create profiles of their customers’ interests and preferences.
Web search – Search engines could return relevant web pages instead of just a list of websites.
Word processing – An algorithm could correct spelling mistakes or grammar errors in documents.
Writing – An algorithm could write news articles, novels, or poetry.
Translation – Translators could use machine translation software to translate text from one language into another.
Audio transcription – An algorithm could transcribed audio recordings into written words
Speech synthesis – An algorithm could synthesize speech from text.
Speaker identification – An algorithm could identify who is speaking in a recording.
Face recognition – An algorithm could identify people in photos or videos.
Facial expression recognition – An algorithm could recognize facial expressions like happiness, sadness, anger, surprise, disgust, fear, etc.
An Example of Machine Learning Application
Machine learning is about training computers to perform tasks based on past experience.
For example, let’s say we want our car to drive itself. We could program our car to follow a set route and avoid obstacles along the way.
But this would require us to write code that tells the car every single step of its journey.
Instead, we can use machine learning to teach our car to navigate on its own.
We can give our car a map of the city and tell it to go to a specific destination.
The car will then use machine learning to learn how to navigate from point A to point B.
Once the car learns how to navigate, it won’t need explicit instructions anymore.
It will simply follow the path laid out before it.
Data science is an umbrella term that refers to any activity related to extracting knowledge from data. This includes everything from analyzing data to building predictive models.
In contrast, machine learning is a subset of data science where algorithms are applied to extract knowledge from data.
Data science is about extracting knowledge from data.
It involves collecting data, cleaning the data, transforming the data into useful information, and then applying various techniques to analyze the data.
This may include using statistical methods, mathematical formulas, or visualizations to understand patterns within the data.
A data scientist is someone who combines statistics, mathematics, programming, and business skills to solve complex problems using data.
An example of data science application
Let’s say, you want to know whether customers prefer buying products online or offline. To do this, you first collect data about the number of sales made online versus offline. Then, you combine this data with the percentage of purchases made online versus offline.
This gives you insight into the preference of customers for purchasing products online or offline.
Now, let’s say, there is no data available about the number of sales online versus offline. How would you go about finding this information?
To find this information, you need to perform statistical analysis on collected data. The results of the analysis help you draw conclusions about the data.
How Does It Work?
Let’s see how data science works with real world examples.
1. Collecting Data
First, you need to collect data about the problem you want to solve. For example, if you want to know whether people prefer buying products online or off line, then you need to collect data.
2. Analyzing Data
Next, you need to analyze the collected data. In our previous example, we analyzed the data to find out whether people prefer buying products offline or online.
3. Drawing Conclusions
After analyzing the data, you need to draw conclusions. Based on these conclusions, you can take actions such as marketing strategies, product development, etc.
4. Predicting Future Trends
Finally, you can use the insights gained from analyzing the data to predict future trends.
For example, you may notice that most of your customers buy products online. So, you can conclude that most of your customers prefer buying products online.
Based on this conclusion, you can develop a strategy to increase the number of online transactions.
5. Improving Operations
Lastly, you can use the data to improve operations. For example, you can use the collected data to identify which areas of your business require improvement.
6. Using Big Data Analytics
Big data analytics refers to the use of advanced tools to analyze large amounts of data quickly.
The term ‘big data’ was coined after the 2010 Google MapReduce paper by Jeff Dean and Sanjay Ghemawat. They described a way to process huge volumes of data using parallel processing.
How Data Science extracts insights from big data sets?
Data science is an emerging field that uses data analysis techniques to extract insights from big data sets.
It has become increasingly popular over the past decade due to its ability to solve complex problems. However, there are two types of data – structured and unstructured.
Structured data is organized into rows and columns. Examples include tables, spreadsheets, databases, and XML files.
Unstructured data is free form text, images, audio, video, etc. Examples include emails, tweets, blog posts, and web pages.
Data Science Vs Machine Learning
|#||Data Science||Machine Learning|
|1.||Data science is a field about processes for extracting data from structured and semi structured data.||Machine learning is a field of study which allows computers to learn without being explicitly programed.|
|2.||Data Science is about understanding patterns in data.||Machine Learning is about creating models that can make predictions based on those patterns.|
|3.||Data Science is about making sense of data.||Machine Learning is a way of building models that can make predictions from data.|
|4.||Data Science is about finding insights in data.||Machine Learning learning and developing new paths from data.|
|5.||Data Science is about modeling.||Machine Learning is about automation.|
|6.||Data science uses machine learning techniques to analyze data.||Machine learning uses data sets from data science to create automated data models.|
|7.||Focuses on developing analytical tools to process data.||Focuses on building predictive models using data.|
|8.||Data science is used to understand past events.||Machine learning is used to make predictions about future events.|
|9.||Deals with data.||Uses Data sets from data science to create models.|
|10.||Focuses on algorithms, data processing and statistics.||Focuses only on algorithms.|
|11.||Broad term comprising of various fields.||ML is a subset of data science.|
|12.||Host of operations involving data.||Usually only three types of learning processes: supervised learning, unsupervised learning, and reinforced learning.|
|13.||Example: Netflix uses data science||Example: Facebook uses Machine Learning Technology|
In conclusion, data science and machine learning are two very similar fields, but they each focus on different aspects of the same problem. Data scientists use statistics and probability theory to analyze large amounts of data, while machine learners use algorithms to create models based on their experiences. In short, data science focuses on the analysis of data, while machine learning focuses on the creation of models.
The main difference between these two fields is that data science involves analyzing data from real-world situations, while machine learning uses simulated data. This means that data scientists must collect data from actual events, while machine learners can generate data using mathematical equations. However, both fields require a high level of creativity and imagination, which is where the similarities end.
Check our blogs on Data Science and Machine Learning:
Mohan Sangli, MD-India, Intuceo.