Without data science tools, it is very difficult to perform complex computing, data analysis, etc. You have gone through different questions such as, What are the best tools to perform data science tasks? And many more.

Data science tools are useful to analyze the data and for the extraction of complex data. There are many data science tools available in 2020. Skyfi Labs help you to find the best data science tools for beginner and professionals too. Let’s begin the journey with top data science tools with Skyfi Labs.

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A lot of skills and knowledge required to become a data scientist. Skills that include:

**1. Mathematics and Statistics- **Mathematical and statistical operations play an important role in Data Science. It consists of most of the probability, linear algebra and basic calculus concepts. Most of the algorithm are basic building blocks of statistics. Linear algebra gives the output in the matrix form. Calculation of the gradients are very important in neural networks, so it is an indivisible part of the data science. Especially calculus is part of deep learning in data science.

**2. Algorithms-** With the algorithmic knowledge, data scientists can easily write efficient pseudo codes which saves much more time. Data scientists are nothing but programmers so they must have the basic and advanced knowledge of algorithms. Algorithms are specially used for the training of models with datasets.

**3. Programming Language- **Python and R are widely used languages in data science. So data scientist needs mastery in one of these languages. As these are open source languages, they have huge community support. A data scientist cannot apply the algorithms without programming knowledge.

**4. Libraries and Framework-** Machine Learning is a part of data science and it involves many libraries and frameworks. Following are libraries and frameworks used in data science:

- Numpy- Numpy is a python library basically used for data manipulation and linear algebra. It is used for complex mathematical equations.
- Pandas- Pandas library used for combining, grouping and filtering of data. It supports operations like aggregation, sorting, indexing etc.
- SKlearn- Sklearn is one of the most popular libraries used for working on complex data.
- Matplotlib- It is specially used for the 2D graphs.
- TensorFlow- It is developed by Google and used for writing algorithms for the neural network.
- Pytorch- Pytorch is deep learning framework specially used for computation.
- Django- Django is a popular web development framework written in python.

**5. SQL- **Database is the most important part of data science for storing the data. Databases like MySql, PostgreSql, MongoDB are specially used. Along with this Oracle database, IBM DB2 etc are the most popular database infrastructures in use.

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Data science tools ease the work like data analysis, extraction and processing of data by going in-depth into complex data. Most of the data science tools are free and have huge community support. We can do faster and effective operations on data by using such tools. These tools literally help to maintain the workflow and perform powerful operations.

Let’s discuss in depth about these tools in the next point.

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**1. Apache spark- **Apache spark is commonly known as spark. Spark is specially developed for handling of stream and batch processing. It also provides access to SQL for storage and Machine Learning. It has APIs for accurate prediction. It also has some powerful features over Hadoop and can perform faster operations.

**2. Jupyter-** It supports many languages like R and python etc. It is an open-source tool that helps to perform interactive computing. Jupyter is the most popular open-source tool for data science. Jupyter notebook is easily share-able and can do the cleaning of data, statistical computing.

**3. Matplotlib- **Matplotlib uses analyzed data to plot the graphs. We can create bar plots, histograms etc with the use of this tool. Specially pyplot module is used. It is an open-source tool with MATLAB like interface. It specially used for data visualization.

**4. SAS-** It is closed source software used to analyze the data by large organizations. It is specially developed for statistical operations. Base SAS programming language is used for statistical modelling. It is a costly tool and only used by large organizations. SAS provides features like SAS studio, management, data encryption algorithm etc.

**5. BigML-** It provides cloud-based GUI which is used for Machine Learning Algorithm. It helps to provide standard software for industries using cloud computing. It is specially used in the development of the software like risk analysis, sales forecasting etc. It includes machine learning algorithms like classification and clustering.

**6. Qlik-** It offers data visualization for both small and large scale industries. It offers a centralized hub for similar data analysis. It provides the following features:

- Associative modelling
- Interactive analysis
- Interactive storytelling and reporting
- Robust security
- Big and small data integration
- Centralized sharing and collaboration
- Hybrid multiple cloud architecture

**7. D3.js- **It is a javascript library for interactive visualization. It is used to create an animated transition. It is specially used for the client-side interaction in IoT tech. The main advantage is that it is open source for developers. It can combine with CSS for visualization.

**8. MATLAB-** It is a computing environment specially designed for processing mathematical information. It provides facilities for Statistical modelling, matrix function and implementation of algorithms. It is a closed source software. Matlab is specially used for neural networks. It is also used in signal and image processing.

**9. Excel-** Excel is a widely used data science tool. Microsoft designed these tools for spreadsheets, complex computing. It is a powerful analytical tool. It can do complex computing, visualization and data processing.

**10. NLTK-** It is a natural language toolkit. NLP is the most popular field in data science. It helps to understand human language. This tool is specially used for Stemming, tokenization, parsing and many others. It is used to develop speech recognition applications etc.

**11. Weka-** Waikato Environment for Knowledge Analysis is commonly known as Weka. It is ML software written in java. This software is also used for data mining. It consists of various tools like clustering, classification, visualization and regression.

**12. Tableau-** Tableau has powerful graphics which helps for visualization Mostly the companies working on business intelligence uses this software.

**13. Ggplot2-** It is a package for the R programming language. It is the most popular library that data scientist used for data analysis and visualization. It is the most popular data science tool.

Data science plays an important role in this tech-driven world and data science tools helps data scientists to make the process with more efficient. Thus get a hands-on experience on the abovementioned data science tools to develop a successful career in data science.

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