Introduction,One of the trending word all over the industry arises in this latest technology like Artificial intelligence, Machine Learning, Deep Learning, Data Science, Big Data.
In this article am going to give clear knowledge on Machine Learning and Data Science.
Technology careers of Machine Learning and Data Science very well, but the difference between Machine Learning engineer and data scientist is highly important to distinguish.
What isMachine Learning?In early days of Machine Learning,it’s considered as Machine Learning is a implementation of Artificial Intelligence. Machine Learning use different statistics and algorithms for data generated and extracted from multiple resources. Complex algorithms and statistics are used here for system to learn and process the data without human action.
For Example, Netflix is one of the best site for Machine learning, here based on your data , Netflix provides the recommending movies and series from your searching history.
Methods of Machine Learning:
- Supervised learning
- Non-supervised learning
- Semi-supervised learning
- Reinforced machine learning
What Exactly Data Science.?
For example, when you signed in Online website like Amazon, Flip kart for purchasing the product, here you can generate the datas. The data are your history like Product and categories which is your searching product.
In the behind the Data Scientist use you history and retargeting for you from their business. This is one of the simplest explanations of Data Science.
The more and more complex data are having different levels of processing,
- Data extraction
- Data Cleansing
Data Science - Skills
- Strong knowledge on Statistics, SQL databases
- Must have the knowledge on Data mining and cleaning
- Understanding Data visualization
- Unstructured data management techniques
- Experience in any Programming languages
- Tool knowledge on Big data such as Hadoop, Hive , etc.
To know more about the Data Science - Skills, please read our Article Must Have Skills for a Data Scientist
Machine Learning - Skills
- Fundamentals of Computer science
- Statistical modelling and Data evaluation
- Experience for application of algorithms
- Must have the knowledge on Natural language processing
- Working experience in Data architecture design
Data Science vs Machine Learning
|Data Science||Machine Learning|
|The scope of data science is to create a useful insight using different algorithms, methods, processes and analysis.||The scope of Machine Learning is to predict the outcome of a new input same way as humans by learning from the previous data sets|
|Data Science is a multidisciplinary field which uses different techniques to get a useful information which are algorithms, mathematical expression, data processing, analyzing.||Machine learning uses three different types of algorithms which are supervised, unsupervised and reinforced.|
|The main focus of Data Science lies in Data||On the other hand the main focus of Machine Learning lies in Learning|
|Data Science is considered as a complete process in creating the valid information from the data we have.||Machine Learning is a part of data science or a single step in the whole process. To be clearer Machine Learning cannot exists without Data Science.|
|Data Scientist are recommended to understand and know the concepts of Machine Learning to work in the large set of data’s created every single day.||It is not necessary for a Machine Learning developer to know about Data Scientist until unless he works in Data Analysis part|
|Data Science multidisciplinary field having a lot of interrelated and interconnected techniques such as algorithms, mathematics, statistics and process methodology.||Machine Learning is not multidisciplinary and fits within Data Science.|
|Data Science scope is defined as predicative causal analytics and prescriptive analysis.||The scope of machine learning is for Predictive reporting and pattern discovery.|
|Data Science as mentioned above works by processing the data to get a useful information.||Machine Learning works by using data to self-learn without being manual interference.|
|Data Science uses different techniques and tools, some of the popular tools are Tableau, Big Data Hadoop, Apache Spark, Python.||Machine Learning on the other hand uses different tools and techniques which are Machine Learning Studio, Microsoft Azure and AWS if required.|
|Top applications that are developed using Data Science techniques are Healthcare analysis, Ecommerce, Banking.||Machine Learning techniques been used in various fields right now some of them are listed below autonomous driving, facial recognition.|
Bottom LineData Science, Machine and Artificial Intelligence are currently considered as the hottest technologies of the current era. The method of data analysis in machine learning and model management recently is listed as the next big thing in 2020. All the three are also been listed as the top trending technologies in 2023.
Data Science as career is been reported as one of the top position in the organization and also the latest report says the recession doesn’t affect the role of data scientist and data analyst. Indian IT firms are expected to have a strong growth in the coming years and the number of startups, IT firms might increase in the result of good growth in data science and machine learning jobs.
Data Science course in Credo Systemz is been specially designed by experts which assist you in developing the required skill set. The course program covers the required tools, techniques, methods and more to make you a professional Data Scientist.
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