Data Science vs. Machine Learning
Data Science
Data Science vs. Machine Learning It is the complex study of the massive amounts of data in a company or organization’s repository. This study includes where data has originated from, the actual study of its content matter, and how it can be useful for its growth in the future.
The data related to an organization is available in two forms: Structured or unstructured. When we study the data, we get valuable information about business or market patterns that help the business edge over the other competitors since they have increased their effectiveness by recognizing the patterns in the data set.
Data scientists are the specialists who excel in converting the raw data into critical business matters. These scientists are skilled in algorithmic coding and concepts like data mining, machine learning, and statistics.
Companies like Amazon, Netflix, the healthcare sector, the fraud detection sector, internet search, airlines, etc., use data science extensively.
Machine Learning
Machine Learning is a study that provides computers the capability to learn without being explicitly programmed. Machine learning is applied using the Algorithms to process the data and be trained for delivering future predictions without human intervention. The inputs in Machine Learning is the set of instructions or data or observations.
Companies like Facebook, Google, etc. use machine learning extensively.
The below table shows the major difference between Data Science and Machine Learning:
| Data Science | Machine Learning |
| It is the field that handles big data-by-data cleansing, analysis, and visualization. | It deals with creating algorithms to understand the data, learn from it, and make future predictions. |
| It requires knowledge of data modeling and analyzing. | It requires problem-solving skills and a strong ability to understand analytics. |
| It uses Python, Statistics, R, and Probability for data analysis and visualization. | It requires data structures, linear and vector algebra, calculus, and differential equations for creating algorithms, and it uses Python or R for programming. |
| It helps to prepare data and provides it to Machine Learning algorithms. | Machine Learning algorithms use data and extract useful insights from it to make predictions. |
| It also helps organizations understand the business and market trends. | Machine Learning algorithms help improve businesses with automation. |
| Data Science is a field about the processes and systems that extract data from structured and semi-structured data. | Machine Learning is a study that provides computers the capability to learn without being explicitly programmed. |
| Need the entire analytics universe. | Combination of Machine and Data Science. |
| It is a branch that deals with data. | Machine Learning utilizes data science techniques to learn about the data. |
| Data in the Data Science may or may not evolve from a machine or mechanical process. | It uses various techniques such as regression and supervised clustering. |
| Data Science not only focuses on algorithms statistics but it also takes care of the data processing. | It is only focused on algorithms statistics. |
| It is a broad term used for multiple disciplines. | It fits within the data science. |
| Many operations of data science that is, data gathering, data cleaning, data manipulation, etc. | It is three types: Unsupervised learning, Reinforcement learning, Supervised learning. |
| Example: Facebook uses Machine Learning technology. | Example: Netflix uses Data Science technology. |
| Some of the tools used by Data Science are SAS, Tableau, Apache Spark, MATLAB | The popular tools that Machine Learning makes use of are Amazon Lex, IBM Watson Studio, Microsoft Azure ML Studio |
| It has a vast scope. | It comes only in the data modeling stage of data science. |
| Data science can also work with manual methods though they are not as efficient as the machine algorithms. | Machine learning cannot exist without data science as data must be first prepared to create, train, and test the model. |
| Data science helps in defining new problems that can be solved using machine learning techniques and statistical analysis. | The problem is already known in machine learning, and tools and techniques are used to find an intelligent solution. |
| Knowledge of SQL is necessary to perform the operations on data. | Knowledge of SQL is not necessary. Programs are written in languages such as R, Python, Java, Lisp, etc. |
| Data science is a complete step process. | Machine learning is a single-stepprocess, that uses data science to create the best suitable predictive analysis algorithm. |
| Data science is not a subset of AI. | Machine learning is a subset of AI. |
| The data science technique helps you to create insights from data dealing with all real-world complexities. | The machine learning method helps you predict and the outcome for new databases from historical data with mathematical models’ help. |
| Nearly all input data is generated in a human-readable format that is read or analyzed by humans. | Input data for the Machine learning will be transformed, especially for algorithms used. |
| In Data Science, high RAM and SSD are used, which helps you overcome I/O bottleneck problems. | In Machine Learning, GPUs are used for intensive vector operations. |
Which is better?
The machine learning method is ideal to analyze, understand, and identify a pattern in the data. You can use this model to train a machine to automate tasks that would be exhaustive or impossible for a human being. Moreover, machine learning can make decisions with minimal human intervention.
On the other hand, data science can help you to detect fraud using advanced machine learning algorithms. It also helps you in preventing any significant monetary losses. It helps you to perform sentiment analysis to gauge customer brand loyalty.
