Data science is the process of extracting knowledge from various structured and unstructured data scientifically. It is a multi-disciplinary field that uses different kinds of algorithms and techniques for identifying the true purpose and meaning of the data.
Data Scientist needed to be highly skilled to interpret data and extract the meaning. Data scientist needed to become an expert on different data science tools such as Analytics Tools, Data Visualization Tools, Data Base Tools, and Others. Data Science includes the following components,Exploration & Analysis of Data:
Related course: Python Machine Learning Course
Data Science mainly starts with exploration and analysis. Data Scientist explores the data and process it into the micro level.
Before starting the data analysis, common data are identified and categorized featuring different data set. KNIME, OpenRefin, Orange, RapidMiner, Pentaho, Talend, are some of the Data Exploration and Data Analysis Tools used for this kind of works.
Visualization in Data Science means presenting data in a more easy and understandable way through various visual content.
It is mainly done for the regular reader who doesn’t understand the technical representation of data. Visualization of data is very effective in representing the data to the end users.
Some data visualization tools are Tableau, Infogram, ChartBlocks, Datawrapper, Plotly, RAW, Visual.ly etc.
In data science, the computer learns how to compute different data using different algorithm and statistics all by itself.
The technique was very time consuming and complex. But with the passes of time, it has now become faster.
These types of computing are termed as machine learning or artificial intelligence.
It automatically learns from the work and system without the help of a programmer. These types of software application learn on the basis of their computing experiences.
Some Machine Learning tools are Google ML Kit, OpenNN, Apache Mahout, HPE Haven OnDemand, HPE Haven OnDemand, etc.
Deep Structured learning or Deep Learning is actually the part of machine learning. It works on the basis of data representation and algorithms.
This deep learning technique is essential for Data Science. ylearn2, Theano, Caffe, Torch, Cuda-convent, Deeplearning4j these are some tools used for deep learning in data science.
Data is the core and primary component of the data science process. Corporation stores data in big infrastructures and set different frameworks for the stored data.
All the data are stored in a very well organized way so the user can access and process data easily. It makes it easy for the data scientist to analysis, explore, access and process to the enormous data.
The initial threats over data science these days are, difficulty over reading some natural languages, data process, and image manipulation.
Though various applications and software are developed for limiting these threats, new problems are arising.
Data Science is the next big thing in computer science. The requirement of new data scientist is expanding rapidly and the sector is growing very quickly.
If you are new to Machine Learning, then I highly recommend this book.