Recently I saw a WhatsApp forward going around regarding pre-screening for CoronaVirus using AI technology. I was baffled to know that AI has made so many advancements, that it could detect a potential positive or negative based on some questions to detect symptoms without a person going to the hospital.
How is this possible?
How could a system know what to ask each individual and how does it ask all that right questions based on previous answers by the user?
The answer is machine learning. Through ML algorithms, the machine can interact with humans using speech recognition and voice modulation and with the data and facts it already has, asks relevant questions to the users.
How do we get the data and facts?
The data is collected using many sources, like surveys, databases, questionnaires and so on, and then has to be processed to make it usable. The processed data is then analyzed for patterns, trends, and behavior to conclude. For example, if people answered a ‘no’ for an itchy throat, but a ‘yes’ for cough, it could be a normal flu and more questions to confirm whether it’s a normal flu will be asked. However, if both answers are yes, then the next question about the next symptom can be asked.
This entire process of data collection, processing, analysis and making conclusions is called Data Science.
What is data science
As we learned above, data science is a vast process having many steps. The most popular and one of the highest-paying jobs in the industry, a Data Scientist always has a lot on his plate, because the world is digital and hence a lot of data is generated every minute. All data science does is to separate the useful data from the not-so-useful data and then process the useful ones to get more useful insights. A simple process diagram of the data science lifecycle is as shown below –
What is machine learning
Machine learning is a technique where machines are trained to perform analysis on huge sets of data. Machines can be trained using various algorithms and learn through a combination of data and experience.
Machine learning is one type of Artificial intelligence which aims to make machines think and make decisions, thus reducing the common errors that humans can otherwise make and making business processes faster and easier.
There are 3 main types of machine learning. These 3 can be further classified into many more, but the basic three are –
- Supervised learning
- Unsupervised learning
- Reinforcement learning
There are many ML algorithms, the simplest being Linear regression, where the output can be expressed as a linear function of the input, with the coefficient and slope. The linear plot is derived after marking all the data points and finding the best-fit (the line that crosses the maximum number of data points). With this linear equation, the input-output mapping function can be formed, and a model can be built. This model can be trained and tested by using huge datasets. Once the model is ready, it can give accurate results for making better business decisions.
Some applications of machine learning are – movie recommendations, Virtual voice assistants like Alexa, Cortana, Google Translate, etc… Learn more about machine learning from our introduction blog.
Definitely, with both data science and machine learning dealing with data, the questions pop up –
How are data science and machine learning related?
What is the difference between data science and machine learning?
Remember that while data science focuses on data, machine learning focuses on learning and data both. Data science is more from a business perspective while machine learning is purely technical.
More importantly, out of the many stages in the data science lifecycle, machine learning plays a vital role in the data modeling and analysis stage. Machine learning algorithms are much preferred rather than traditional statistical analysis and algorithms, hence reducing much of human coding work and producing accurate results. The below steps show the machine learning process –
If you notice carefully, these are the steps that we follow during data analysis too!
Is machine learning required for data science?
Machine learning is that phase of data science that helps businesses make bold future decisions with confidence. The predictions made by machine learning models have been “good enough” so far as to use them in solving more complex business problems. All this without much human intervention. Further, ML is constantly innovating and in the future, likely to grow. Hence, ML is becoming an essential component for data science.
What should you choose as your career option – ML or data science?
Before we move on to a head to head comparison, we would like to make one last point – if you are confused which one to choose between the two, there is no right answer. If you are a purely technical person and love mathematics and statistics, ML is certainly the way to go.
However, if you like to explore various domains, work with business analysts and have a flair to always look at the bigger picture, data science will be more suitable for you. That said, once you get into data science, you will need some basic understanding of ML as well, though not too deep.
Career-wise, both ML and data science are innovation-oriented and well-paid jobs with the demand only increasing every day. In about a decade, there will be more demands for both data scientists as well as ML engineers.
So, you can get both work satisfaction and good pay!
Data Science vs Machine Learning: Head to head comparison
We already covered how both data science and ML are related and are great as a career choice, however, there are a lot of differences when it comes to the skillset, actual salary paid, roles and responsibilities and the basic definition itself. In this section, we will touch upon all the above.
Let us have a look at the differences –
|Data Science||Machine Learning|
|Data science involves a lot of processes like the collection of raw data, preparing and transforming it, modeling the data, visualizing, reporting and concluding insights from the data.||Machine learning is concerned with the data modeling phase. That also involves some data preparation to create and split datasets.|
|The scope of data science is very huge||Scope of ML is limited|
|ML algorithms are preferred, but data science can be done with traditional algorithms and statistical methods, that require more effort and time.||There is no use of data modeling and ML if there is no data science.|
|Data science is an independent, interdisciplinary field, which is related to AI through ML.||ML is a branch of AI, which has other branches like deep learning, neural networks, etc… ML is used in data science to reduce human errors.|
|In data science, the problem is first identified and then various techniques are used to find the most appropriate solution||The problem is already known, and the focus is on building an algorithm to find the best solution|
|Data science is all about data and just a little about learning||For machine learning, data and learning both are important|
|The average salary of a data scientist is around $95k for entry-level and $185,000 for managerial level||The typical salary range for an ML engineer is about $125000 to $176000|
|Almost all data scientists need to be familiar with a relational database, SQL, Hadoop and write simple queries for data manipulation||Machine learning engineers don’t have to know SQL. They work on programming languages like Python, R, Java, C++, etc..|
|Other than domain expertise and technical skills like knowledge of SQL, Python, big data, ML algorithms, statistics and visualization tools, data scientists are required to possess certain non-technical skills like identifying business opportunities, problem-solving skills, business acumen, creative thinking, good communication, collaboration with teams.||Machine learning is a purely technical job. A machine learning engineer should be extremely skilled in programming languages as mentioned in the previous point, write algorithms, understand signal processing techniques, be able to perform statistical analysis and fine-tune the model they build, and use latest libraries and frameworks|
|Data scientist jobs are exploding and in high demand in every part of the world||ML jobs are quite popular, and the demand is going to increase, a lot of research is underway for the entire AI spectrum|
|Some popular data science applications are in health care, targeted content marketing, fraud detection, etc…||ML applications are NLP (Alexa, Cortana etc…), email filtering, product recommendations, medical diagnosis, etc…|
In this article, we explored data science and ML in brief and understood the important differences between both. A more research-based field, ML is yet to realize its full potential as AI, from which ML branches out is still in initial stages. Data science is fully utilized to solve various business problems and provide predictions and solutions to complex programs in an easier manner. If you are wondering where to start, our suggestion will be to start with basic machine learning algorithms and as you go along, eventually get the knack of the bigger picture – how to define problems, prepare relevant data sets and use the inference by models to make workable business decisions. If you know ML, you can move into the broader data science field easily.
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