In the previous article, we have seen what Data Science is; this article will take you to the deep explanation of the tools used in Data Science. Data Science is exactly the process of mining, cleansing, dividing and using the data (structured, unstructured or semi-structured). In the process of discovering the data, tools need to be used as finding the exact data, storing, ceasing and the whole process is not as easy as it is!
Since there are an extensive category of open source tools available from data-mining platforms to programming languages, we put collectively a blend of technology that data scientists could include into the data science toolkit.
Benefits of Data Science Tools in Business
The actual benefits that Data Science can do for a business are marvelous. It depends on the company’s goal and the strategies. It affects the benefits and works in Sales and Marketing departments.
- Through this one can mine the data and enhance the illegal activities
- Can know the customer’s pace and create the personalized recommendations
- Best mode and time of delivery
- Empowering management to take better decisions
- Identifying the current market opportunities and testing them
- Identifying and refining the Target Audience
Applications of Data Science
Data Science and Big Data are applied widely. Some of them are
- Banking and Securities
- Communication, Media and Management
- Health Care Providers
- Education
- Manufacturing and Natural resources
- Government
- Insurance
- Retail and Wholesale Trade
- Transportation
- Energy and Utilities
What are the tools used in Data Science?
R Programming
R was originated in 1995. As it is open source version it has been widely used by Data Scientists. It is mainly used for Data manipulation and graphics. As per the Data Scientists, it is stated that R is one of the simpler languages to learn as it has numerous packages and guides available for users.
Python Coding
Python is a versatile, universally used language. It is commonly termed a general-purpose programming language, which focuses mainly on readability and simplicity. It’s the simplest language where even a non-programmer can easily learn If you are not a programmer but are looking to learn, this is a great language to start with.
Scala
Scala is another general-purpose programming language that runs on the Java platform. It is mainly used to handle the large sets of Data, the same as Hadoop in Big data. The exceptional functional capabilities have made many companies adopting this language slowly in the modern-day businesses.
Weka
Weka is also machine learning software written in Java. This language is widely used in data mining. This allows users to work with large sets of data easily. Preprocessing, analysis, regression, clustering, experiments, workflow and visualization are also possible with this language. It doesn’t match with the advanced features that R and Python have.
SQL
SQL(Structured Query Language) is a special-purpose programming language used for data stored in relational databases. This is generally used for more basic data analysis and can perform tasks so as organizing and manipulating data or retrieving data from a database. This is not a new tool, many companies have been using it widely for decades. It ranks top amongst all the data science tools.
RapidMiner
The main tool used for Data visualization is RapidMiner. It is the predictive analytics tool used for statistical modelling capabilities. This is an open-source platform which is used widely by enterprises.
Scikit-learn
Scikit-learn is a machine learning library, extensively written in the Python and built on the SciPy library. As a part of Google’s summer code project, it was developed and it was able to be valuable open source software. It supports the features of data classification, regression, clustering, dimensionality reduction, model selection and preprocessing.
Apache Hadoop
Apache Hadoop software library is a framework, written in Java. It is mainly used for treating large and complex datasets. It does the same operation as on Big data. This includes Apache Hadoop framework. Hadoop Distributed File System (HDFS), Hadoop Yarn and Hadoop MapReduce.
Apache Spark
Apache Spark is a cluster-computing framework which is used for data analysis. The speed and the capabilities of it made the organizations to use it broadly. The University of California named it as Spark after its inception and later the source code was donated to the Apache Foundation and hence when it has been named to Apache Spark. This has the highest speed when compared with all other big data tool speed and that’s why it is most preferred.
SciPi
The SciPi or Scientific Python is a computing ecosystem based on the Python programming language. It offers a huge amount of core segments including NumPy for numerical estimation,
Data Melt
Data Melt is mathematical software which promotes data scientists to work with ease as it has the advanced mathematical computations, statistical analysis and data mining capabilities.
Apache Storm
Apache Storm is generally compared to Apache Spark and it is a computational platform for real-time analytics. It is also known for its better streaming Spark.
MongoDB
MongoDB is a NoSQL database which is meant for its scalability and high performance. It is mainly used for large-scale web apps it plays an integral part in data science toolkit.
TensorFlow
TensorFlow is a software library for numerical computation and created for the use of it in various fields like hacking, researching and business innovation. It ranks as the best one of the data science tools as it supports programmers to access the power of deep learning without needing to understand some of the complicated principles.
These are the large set of tools sued in Data Science Toolkit and if you want to learn the complete spectrum of Data Science, Digital Nest, the leading training institute catering the needs of students, corporates and professionals. It trains on the complete spectrum of Data Science and Machine Learning with certifications and Placement assistance.
