Everyone must be aware of the terms ‘machine learning’ and ‘data science’, and most of them tend to confuse one with the other. Machine learning and data science are two immeasurable, dissimilar domains, with each domain being home to a large amount of knowledge and expertise.

Data science is the massive container that holds innumerable, fundamental data operations. Whereas, machine learning can be understood as one of the prime data operations that form this content of data science.

Let us now do a brief survey on both of these disciplines.

Data Science: What is It?

The rapid growth in the quantity of data has led to the invention of a new discipline known as Data Science. Data has become an integral part of industries and many organisations. It has brought about the fourth industrial revolution. Data science has been the most sought job of the 21st century.

Data science acts as the huge umbrella that unites every single underlying data operation, statistical modelling, and mathematical analysis. The enormous outburst and exponential proliferation of data have created an opportunity for businesses and organisations to capitalize on.

By using this data, industries are making more careful data-driven decisions and implementing more useful business strategies. Cell phones, electronic gadgets, sensors, etc. generate quintillion bytes of data every day. It has become a rich source of energy that every sector in every society utilises.

A data scientist must be efficient in various fields, such as mathematics, statistics, and computer programming. To be extremely efficient in data science, you have to understand several different trends and patterns in data with the help of statistics. Data science courses hold a huge learning curve for beginners for them to master it, but once they acquire that mastery, there is no looking back. A data scientist course fee in India is not too expensive either, so you can chase your goals without hesitation. A data scientist is also expected to be comfortable working with both structured and unstructured data.

There are several different steps and procedures involved in data science, such as data extraction, data manipulation, and data visualisation. After these steps, a data scientist is required to implement predictive models and optimize them for better and improved performance and accuracy.

Here are a few examples of the steps and procedures that are involved within the span of data science —

• Data gathering
• Data processing
• Data analysis
• Originating predictions
• Model optimizing
• R
• Python
• SAS
• Apache spark
• D3.js

Machine Learning: What is It?

The methodical study and investigation through statistical modelling and computing algorithms help systems to create self-directed assessments without any explicit interference is called Machine Learning. There are two chief operations in machine learning: (a)classification, and (b)regression. Based on these two operations, machine learning uses predictive models that estimate the probability of the occurrence of actions. To put this in simpler words, machine learning enables computers to learn without overtly feeding commands and instructions.

For instance, we give standardized instructions to computers for them to function. These instructions can either be in a high-level programming language or a low-level machine language. Based on these inputs, a computer delivers the right output. Thus, there is a continual interchange of inputs and outputs. Now, what if the machine could be trained to provide you with all the necessary outputs based on inputs you give to it? This way, you would not have to provide inputs time and over again.

Well, this method of training a machine on chronological data to provide the user with the right output is called machine learning. Data is the primary source for machine learning algorithms that seeks and detects fundamental patterns within the data.

Some machine learning algorithms under supervised learning are —
• Linear and multivariate regression
• Decision trees
• Naive Bayes
• Logistic regression
• K-nearest neighbour
• Artificial neural networks
• Linear discriminant analysis

Some machine learning algorithms under unsupervised learning are —
• Clustering analysis
• Anomaly detection
• Hierarchical clustering
• Principal component analysis

Furthermore, there are two widely used reinforcement machine learning algorithms —
• SARSA (State-Action-Reward-State-Action)
• Q-learning

Industries and sectors, such as banking, finance, health-care, transportation, manufacturing, etc. use machine learning algorithms extensively.

Some of the highly used machine learning tools and packages are —
• Scikit-learn
• Tensorflow
• mlpack
• CARET
• Weka
• Shogun

Shogun is a well-known, open-source software used in machine learning. Algorithms that are supported by Shogun are —
i. Dimensionality reduction
ii. Support vector machines
iii. Clustering algorithms
iv. Hidden Markov models
v. Linear discriminant analysis

In this overview of data science and machine learning, we observed that machine learning is a tool used by data scientists to execute vigorous predictions.