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Data Science: Fundamental tools for data analysis

Data Science is a recent science, which is conquering the digital world. Many companies use Data Scientist which produces reports on market trends on customer needs, to improve their products. Data Scientists manage large amounts of data, more or less structured and make use of tools and programming languages ​​for Data Science. In this article, we will share some of the Data Science tools most used by Data Scientists to carry out their data operations.


 Why use a Data Science tool?

The advantages of using Data Science tools are that they are the tools that generally do not need programming and provide GUI (Graphical User Interface). So anyone with a minimum knowledge of algorithms can use these tools to create high-quality machine learning models.


Many companies have recently launched GUI-based Data Science tools. These tools simplify various aspects of data science such as data archiving, manipulation and modeling. The tools facilitate the work of data strategists, gaining speed, quality and process management.

 

The best tools for Data Science

 

1. Apache Spark

 

Apache Spark, or simply Spark, is a powerful analysis engine and is among the most used tools in the field of Data Science. It is software that facilitates the writing of executable programs on computers, called workers. Each worker is entrusted with the task of retrieving data from a source, processing it and relating it to other workers, extracting a set of data deriving from processing.


Spark is specifically designed to manage the processing of data on a database, or in streaming; continuously processing the data at the time it is collected. It comes with many APIs that facilitate Data Scientist repeated access to data for Machine Learning, Storage in SQL and can perform predictive analysis. The tool is very valid for the ability to manage Big Data streams in real-time compared to other analytical tools that only process historical data. Spark also offers various programmable APIs in Python, Java, and R.

 

2. SAS

 

 

SAS is one of those tools for data science that are specifically designed for statistical operations. SAS is proprietary closed source software used by large organizations to analyze data. SAS uses a basic programming language that allows you to perform statistical modeling. It also offers numerous statistical libraries and tools to use for data modeling. SAS is reliable and has excellent customer support, but is not inexpensive and has libraries and SAS packages that must be purchased separately. SAS is also statistical programming software that simplifies code writing operations. Its market is mainly geared towards larger companies. However, there are open-source tools that have the same characteristics.

 

 

3. BigML

 

 

BigML is another popular Data Analytics tool. BigML provides a fully interconnected cloud-based GUI environment that can be used for processing machine learning algorithms. BigML provides standardized software that uses cloud computing for data management. Through this, Data Science specialists can analyze data from various company departments. The software can be used to make sales forecasts, risk analysis, and product innovation, as it is programmed for predictive modeling. It widely uses algorithms that perform clustering, classification and time series forecasting.

 

4. MATLAB

 

MATLAB is a numerical computing and statistical analysis environment written in C language and allows us to process numerical information. It is closed-source software that allows manipulation of matrices, algorithmic implementation and statistical modeling of data. MATLAB is widely used in different scientific fields. In relation to Data Science, MATLAB is used to simulate neural networks and fuzzy logic. Through the proprietary graphics library, it is possible to create very valid visualizations. MATLAB is also used in image processing using computer algorithms to create process, transmit and display digital images. 

 

5. Excel

 

Excel is a powerful analytical tool for Data Science. Excel comes with various formulas, tables, filters, and tools. You can also directly create custom functions and formulas. Although Excel cannot handle large amounts of data, it is still an ideal choice for creating visualizations. You can also link Excel with SQL, the well-known database management language, and use it to manipulate and analyze data. Many Data Scientists use Excel for data cleaning, as it provides an interface that can be interfaced with a graphical interface to easily pre-process information.

 

6. Tableau

 

Data is transformed into value only if presented in an easily understandable way. Tableau is leading data visualization software in the field of Data Science, equipped with powerful graphics to create interactive dashboards. It is widely used in the representation of statistical data and the field of Business Intelligence. The most important aspect of Tableau is its ability to interface with databases, spreadsheets, and OLAP (Online Analytical Processing) cubes. In addition to these features, Tableau can display geographic data and draw maps with longitudes and latitudes. Tableau also has a data analysis tool. Tableau uses an active community and you can share your creations on the online platform. Tableau is paid software but has a free version called Tableau Public.

 

7. Jupyter

 

Project Jupyter is an open-source IPython-based tool that allows you to program open-source software and experiment with interactive processing. Jupyter supports multiple languages, including Julia, Python, and R. It is a Web application tool used to write real-time code, visualizations, and presentations and is very common in those dealing with Data Science. The tool presents an interactive environment through which Data Scientists can perform all their activities. It is also a powerful narrative tool as there are several presentation features. Jupyter takes care of executing functions such as data cleaning, statistical calculation, visualization and creation of predictive models based on Machine Learning. 

 

8. Matplotlib

 

Matplotlib is a plotting and display library developed for Python. It is a tool widely used for the generation of complex graphs, derived from the analyzed data, using simple lines of code. Matplotlib facilitates the generation of bar charts, histograms, scatter charts and other forms of visualization. Matplotlib has several essential modules, one of the most used is pyplot, which can be interfaced with MATLAB and is, therefore, an open-source alternative to the graphic modules of MATLAB.

The fact that it is a Python library makes Maptplotlib easily integrated with the most well-known programming software, widely used in data science. It is, therefore, an ideal tool for beginners in learning data visualization in a Python environment.

 

9. TensorFlow

 

TensorFlow is a machine learning tool that provides optimized modules for programming algorithms with different functions, both perceptual and language comprehension. It has APIs that interface it to Google products like speech recognition, Google Photo, Gmail, and the search engine. The tool is widely used to develop advanced machine learning algorithms and AI, used mainly in Deep Learning. It is an open-source and constantly evolving toolkit, known for its performance and high computational capabilities. TensorFlow can work on both CPU and GPU. This feature gives a great advantage in terms of the processing power of advanced machine learning algorithms. For Data Scientists specializing in Machine Learning, Tensorflow is an indispensable tool.

 

10. Weka

 

Weka is the automatic learning software written in Java. It is a collection of various Machine Learning algorithms for data mining. Weka includes various machine learning tools that operate in classification, clustering, regression, visualization and data preparation.

Weka is open-source GUI software that allows easy implementation of machine learning algorithms through the platform and allows activating the machine learning activity without having to write a line of code. It is ideal for less experienced Data Scientists.

 

Conclusions


Data science requires a wide range of tools, which perform data analysis, allow you to create pleasing and interactive visualizations and powerful predictive models using machine learning algorithms. Most Data Science tools offer different data analysis functions with a single tool. This makes it easier for the user to implement data science features without having to write the code from scratch. We presented the most popular Data Science toolstools for data analysis, aware of the fact that every week many other tools are created that allows performing data analysis functions.


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