In recent times, I am sure everyone regardless of their stream has heard about topics like Artificial Intelligence, Machine Learning, and Data Science. At first, it does sound like buzz words, but they are being recognized as top tier technologies across the globe.
Around the year 2013, a study reported that 90% of the data was created in just two years of time. As the years are passing in a jiffy, the speed of collecting data is also increasing. But the question is, why do we need this data?
A key feature of every organization as it helps make decisions based on facts, statistics, and trends. This is why the scope has increased massively. Data Science uses programming skills, domain expertise, scientific approaches, procedure, algorithms, framework, mathematics & statistics. This is further used for extracting knowledge and insight from loads of data. The extracted data can be either structured or unstructured.
Data science aims to bring together data examination, Machine Learning & ideas. So this is an extension of various other fields like predictive analysis, data mining, statistics, etc. The field includes visualization, pattern recognition, ML, probability model, signal processing, data engineering, etc.
A field of study that involves giving computers the ability to learn without being programmed in detail. This is one of the most interesting and exciting technologies with a lot of prospects. Machine Learning aims on making a machine learn similar to that of humans. It is a type of artificial intelligence that makes software applications more accurate at predicting outcomes without explicit programming. ML uses old data for predicting new output i.e. the values which were historically present.
Types of machine learning
Machine Learning is categorized on the ability of the software to learn without programming much, here are the types listed below-
Here you have an input variable as (x) and an output variable (Y). Now with the help of an algorithm, you learn mapping functions from the input to the output i.e. Y = f(X). The aim is to map the functions so well that when new data are entered, the output can be predicted.
Here the input variable (x) does not have a corresponding output (Y). This involves training the machine to learn on unlabeled data. The algorithm scans through datasets in search of a connection. Basically, this involves letting algorithms train by themselves without external help.
It has a mix of two preceding types where the data scientist may feed an algorithm. Here the model is free to explore all the data by itself and develop corresponding learning sets of data. Here the input data are of large amounts and only have some labeled output.
Making machines think like humans is a simplified explanation for AI. It refers to simulating human intelligence in machines by programming. The term can also be used for any machine that has similar traits to that of a human mind.
This refers to mimicking & executing all tasks given to a machine effectively. Artificial Intelligence aims to include learning, reasoning, and perception.
AI is important because it automates repetitive learning & discovery through data it even adapts through progressive learning algorithms. It helps machines develop amazing capability, by adding intelligence. The study aims on learning and analyzing the depths of data and making the most out of it by constant innovation.
How are the 3 related?
Everything starts with data, and the data are slowly gathered and the useful information is understood by data science. Now after the data, the next step is to train the machine to adapt accordingly. Artificial Intelligence is applied after the machine is trained to learn from the data gathered. For a much simpler explanation, data science is an all-inclusive term that consists of ML. And ML is an element of artificial intelligence. So one can say, data science merges both artificial intelligence & machine learning.
In today’s day, every company is realizing the importance of data science, AI, and machine learning. The industry might be small or big, regardless everyone wants to stay a part of the competition. Now the company’s take things into their own hands and efficiently develop and implement data science capabilities because this is how they stay current.
Plenty of doors open if you even want to enter any one of the fields. Listed below are all the jobs available in the streams discussed above:-
- Data Scientist
Data scientists help in gathering relevant data from multiple sources and later assess them to draw constructive conclusions. For pursuing this, you need a master’s degree in Comp-Science or Mathematics. They are also responsible for building machine learning models for developing accurate predictions. A data scientist comparatively has more freedom to pursue their own ideas. They even get to experiment with methods that help in finding interesting patterns and trends.
Other jobs include Data Analyst, Data Engineer, Quantitative Analyst, Data Warehouse Architect, Business Intelligence Analyst, Statistician, Business Analyst, Systems Analyst, Marketing Analyst, Operations Analyst.
2. Machine Learning Engineer
When we talk about data science and ML, the terms are often interchangeable. There are some companies where data scientists are mentioned in your profile; it implies you have specialized in machine learning. And then at other companies, machine learning engineers are into the software engineering roles that include taking an analysis. This analysis is made by a data scientist and converting it into software. Since both the terms are deeply correlated all machine learning engineers will require some background in data science. A good understanding of advanced knowledge of machine learning techniques is also essential.
The job prospects include but are not limited to Natural language processing, Business Intelligence Developer, Human-Centered Machine Learning Designer, Software Developer/Engineer (AI/ML), and more.
3. AI Engineer
You can become an AI engineer if you love solving problems. In this, you are supposed to develop, test, apply different methods of the subject. The job profile involves you handling AI infrastructure. It even makes use of ML algorithms and understanding of the neural network for developing functional AI models.
With the help of AI, you can get business insights which later help make effective business decisions. An additional certification on ML or data science adds to the advantage.
With AI you can secure jobs as a Robotics Scientist, Big Data Engineer, Business Intelligence Developer, Data Scientist, Research Scientist and many more!
By learning these fields you really have a bright future, because this is the future. The jobs available will only increase as the industry is constantly developing. And the opportunities won’t be limited as the field is only about innovations.