Data Science Tools Everyone Should Know
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Data Science is amongst the most popular fields of the 21st Century and nothing to wonder if I tell you that it is the key methodology behind revolutionary technologies like Artificial Intelligence and Machine Learning. With voluminous data at the disposal of global organizations,
Data Scientists need to deploy and ensure the effective use of numerous data science tools to help organizations manage and analyze large structured and unstructured data sets for lending support in making strategic business decisions.
The article brings you the most popular data science tools that Data Scientists and business analysts use in their work life. First of all, you need to understand that Data Science tools can be categorized into those used by:
1. Non-Programmers
2. Programmers
Perhaps, this signs relief to those even who do not have in-depth knowledge of programming languages. This means that Data Science is a technology that can be learned by those who do not have an upper hand in programming capabilities.
The Data Science tools that can be used by non-programmers include:
On the flip side, programmers have access to an array of Data Science tools that help them create predictive models, and visualize and analyze diverse data patterns in the favour of businesses. The Top Data Science tools used by Data Science professionals are:
Tableau:
Tableau is a robust Data Visualization software packed with compelling graphics making it the most interactive Data Science tool of the era. The most important aspect of Tableau is its ability to integrate data from diverse resources, OLAP (Online Analytical Processing) cubes, geographical data visualization, and much more.
TensorFlow:
TensorFlow has become a standard tool for Machine Learning and is extensively used for advanced machine learning algorithms like Deep Learning. Known for high performance and computational capabilities, TensorFlow is an open-source and ever-evolving toolkit used by programmers. It runs on both CPUs and GPUs and has recently emerged on more powerful TPU platforms, giving an unparalleled edge in terms of the processing power of advanced machine learning algorithms.
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SAS:
SAS- a popular data science tool designed for statistical operations, is used by major global enterprises to analyze data and draw insights. SAS uses base SAS programming language for performing statistical modeling. Offering expert statistical capabilities of creating a predictive and analytical model and organizing data, SAS is expensive and is trusted by large industries.
Python:
Both, a popular and powerful programming language, Python is the perfect Data Science programming tool for developing custom algorithms used by Data Scientists in analyzing huge raw data sets efficiently. As Python is used in Data Science and back-end development, it provides a solid platform for developing machine learning models too, leveraging the utmost security of the implemented tools.
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EXCEL:
Developed by Microsoft, Excel is a popular Data Science tool used by data analysts and business intelligence experts as it features strong inbuilt capabilities of spreadsheet calculations, data processing, visualizations, complex sorting, and lookup functions. Excel comes with various formulae, tables, filters, slicers, etc. You can create custom functions and complex formulas in Excel to efficiently analyze the given data. Embedded with the power of data visualization and graphs, Excel is by far an ideal choice for all Data Scientists. Seamless integration with SQL makes Excel easy to use and analyze data on the go. A lot of Data Scientists use Excel for data cleaning as it provides an interactable GUI environment to pre-process information easily.
R-Language:
Similar to Python but more specifically built for Data Science, R is yet another preferred programming language for Data Scientists. An authentic poll revealed that 48.5% of respondents voted it to be one of the leading data science tools. With machine learning, statistics, and prominent data analytics features, data science professionals preferably use it in the enterprise environment. Some of the statistics used of R include calculating mean, median, and quartiles, building hypotheses, applying data tests, and exploring it to the core to help management base their business decisions on it.
Hadoop and Apache Spark:
Specifically designed to handle and process data in batches and execute stream processing, Apache Spark is an all-powerful analytics engine that facilitates Data Scientists associated with global organizations.
It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help Data Scientists to make impactful predictions with the given data.
Conclusion
We recapitulate that data scientists require a vast array of tools to draw inferences from unstructured data sets and provide support in making the right business decisions. The mentioned popular data science tools are widely being optimized for analyzing data, creating aesthetic and interactive visualizations, and creating powerful predictive models using machine learning algorithms. Alongside easing complex data science functions, these data analytics tools help in implementing data science functionalities without having to write their code from scratch.
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