Many concepts are closely related and typically interconnected to one another, so might even cause a confusion. There are many misconceptions about these technologies. Here we would like to focus on data science and Machine learning and their differences. Data Science and machine learning are developing rapidly and companies are now looking for experts who can filter the information and help them drive quick business decisions effectively.
What is Data Science?
Data science is the field that covers scientific approaches of processing and structuring data in order to extract knowledge. This is a more extensive term that brings together various strategies for gathering information for different associations, organizations, and governments. In short , it applies methods and approaches from mathematics, computer science, statistics and information science.
As the large amount of data considerably grows, the main difficulty is not storage anymore, but the right ways of processing and receiving the most most important. A lot of scientists direct their attention toward this issue. Therefore data science is a special approach that applies a number of processes for describing, predicting, interfering, and extracting insights from huge data. The main task is to discover productive solutions by applying capable hardware, simple and complex systems as well as data science algorithms. The concept relies on creatively binding different expertise such as mathematics, technical and business skills.
Working of Data Science-
Firstly, the quantitative techniques used in mathematics help to find solutions for data utilization. It allows data insights and the creation of data products. The data is expressed mathematically through its texture, dimension, and correlation. Data science for business deals not only with statistics but analytical functions also.
Secondly, technology is an essential part of the overall process. Data scientists apply digital tools to organize a large amount of data, handle the complex tasks and ease the process flows. Their technical skills concentrates on building, recomposing and finally receiving the final products. Python, R, SQL, SAS are used for coding. In short, the data scientists have to be capable of complexity and finding cohesive approaches.
Thirdly, Applying mathematical and technical expertise data scientists are determined to learn from it, share observations and make conclusions. This makes them be good business assistants. Therefore data science for business is the valuable tool to guide the process and provide the business consultations.
What is machine learning?
Machine learning uses algorithms to extract data, learn from it and then forecast future trends for that topic. Traditional machine learning involves statistical analysis and predictive analysis. These analysis is used to spot patterns and catch concealed bits of information which is dependent on perceived data. One of the best examples of machine learning implementation is Facebook. Facebook’s machine learning algorithms collect behavioral information for every user on the social platform. Based on past behavior of individual, the algorithm predicts interests and suggests articles and notifications on the news feed. For eg., Amazon recommends products, or when Netflix recommends movies based on past behaviors, machine learning is at work.
Machine learning will not exist without data science as long as it applies data science algorithms and techniques for its performance. Theory and methods used here are conveyed from a mathematical study of optimization, predictive analytics, computational statistics, etc. Machine learning is a great option to reproduce patterns and make the most of its experience. The algorithms perform automatically, while human experts concentrate on the better and harder solution. Machine learning experts should obtain programming, data modeling, and evaluation skills also with the knowledge of statistics and Probability theory.
Furthermore, it is time-saving because machine learning analyzes and delivers valuable solutions faster. It requires less time to create the data analyzing model as compared to the time required by human experts so it manually. So this approach has proved its effectiveness by AI and human cooperation. It has greatly contributed to different spheres and easier processing and analyzing the data. You can use machine learning for web search, advertising, marketing, data security, healthcare, fraud detection, image and speech recognition etc. You can see the difference between Artificial intelligence and Machine learning at- Artificial Intelligence and Machine Learning: A Comparison.
Key Difference between Data Science vs Machine Learning-
- Data science creates insights from data dealing with all real world complexities. It includes task like understand requirements, extracting data etc. Whereas Machine learning accurately classify or predict outcome for new data point by learning patterns from historical data.
- Most of the input data is generated as human consumable data which is to be read or analyzed by humans like tabular data or images. specifically for algorithms ,Input data for ML will be transformed. Feature scaling, Word embedding or adding polynomial features are some examples
- Complexity for data science is with components for handling unstructured raw data. Whereas complexity in machine learning is with algorithms and mathematical concepts behind it.
- In data science lot of moving components are scheduled by an orchestration layer to synchronize independent jobs, and with machine learning, Ensemble models will have more than one ML model and each will have weighted contribution on final output.
- To be the expert of data science one must posses the following skillset- ETL and data profiling, Domain expertise, Strong SQL, NoSQL systems, Standard reporting/ visualization. Ans to be the machine learning expert one must have Strong Maths understanding, Python/R programming, Data wrangling with SQL, Model-specific visualization.
Due to the continuous development of technologies and the increasing amount of data, a lot of specialists are searching for better approaches for organizing, utilizing and gaining from it. Data science manages various processes to receive reasonable solutions whereas machine learning as its subdivision deals with data science algorithm. The two methodologies have effectively suggested themselves at the market and are typically utilized in various circles.
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