Data science is a popular and lucrative profession, and despite pandemic-era slowdowns, it’s still one of the appealing jobs around. As businesses seek to employ the power of data to increasingly digital commerce, companies across industries are on the lookout for data scientists and vice versa. So the demand for data scientist in the market keeps rapidly increasing day by day.

Data Science includes obtaining the value from data. It is all about understanding the data and processing it to extract the value out of it. Data Scientists are the data professionals who can organize and analyse the huge amount of data.

The functions that data scientists perform include identifying relevant questions, collecting data from different data sources, data organization, transforming data to the solution, generating predictions out of the data and communicating these findings for better business decisions. In order to do so, he requires various statistical tools and programming languages.

Top tools for data science:

#Programming languages:

As a data scientist, Python should probably be the first tool you should master. Other languages include R, SQL, JavaScript, Scala, Julia. Importantly languages like Python, SQL and R are the top performers and act as foundations for many data science or analytics roles, others are useful for career paths in areas such as data systems development or better suited specifically for aspiring data scientists.

Libraries of python used in Data science:

Matplotlib is essential plotting library that gives you a wide range of aesthetic graphs. With matplotlib, you can perform image plots, contour plots, scatter plots, line plot etc.

Scikit-learn is a library based in Python that is used for implementing Machine Learning Algorithms. It is simple and easy to implement a tool that is widely used for analysis and data science

Pandas is an important library in Python using which you can manipulate data and implement various functions like filtering, sorting, merging, joining, pivoting and reshaping the data.

Numpy is a python library that is mostly used for scientific computing. It consists of powerful features and can perform computationally heavy tasks like linear algebra.

R :

R is a popular programming language that is used for statistical modelling. It is useful for performing analysis on large scale data and visualizing information. R is a must know the language for a data scientist, as it contains the core statistical packages. R can also offer a steep learning curve to the beginners who are newbies in data science. The availability of mass packages and its open-source support has made it a popular choice for data science, analytics and data mining.

SAS:

SAS stands for Statistical Analytical System. It is a tool developed for advanced analytics and complex statistical operations. It is used by large scale organizations and professionals due to its high reliability. Furthermore, there are several libraries and packages in SAS that are not available in the base pack and can require an expensive upgradation.

Apache spark:

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing. 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.

MATLAB:

MATLAB is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modelling of data. MATLAB is most widely used in several scientific disciplines. It is also used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing.

D3.js:

JavaScript is mainly used as a client-side scripting language. D3.js, a JavaScript library allows you to make interactive visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser. Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.

Microsoft Excel:

Excel is one of the best tools for Data Science beginners. It also helps in understanding the basics of Data Science before moving into high-end analytics. It is one of the essential tools used by data scientists for data visualization. Excel represents the data in a simple way using rows and columns to be understood even by non-technical users. Excel also offers various formulas for Data Science calculations like concatenation, find average data, summation, etc. Its ability to process large data sets makes it one of the critical tools used for Data Science.

Tableau:

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence. You can represent data visually in less time by Tableau so that everyone can understand it. Advanced data analytics problems can be solved in less time using Tableau. You don’t have to worry about setting up the data while using Tableau and can stay focused on rich insights.

Jupyter:

Jupyter Notebook provides you with an easy-to-use, interactive data science environment across many programming languages that doesn't only work as an IDE, but also as a presentation or education tool. It's perfect for those who are just starting out with data science.

DataRobot:

DataRobot is one of the valuable tools required for Data Science operations integrated with ML and Artificial Intelligence. You can drag and drop a dataset quickly on the DataRobot user interface. Its easy-to-use GUI makes data analytics possible for beginners as well as expert data scientists.You can build and deploy more than 100 Data Science models at once via DataRobot and can get rich insights.

Microstrategy:

MicroStrategy is used by data scientists who are also into business intelligence. Besides enhanced data visualizations and discovery, MicroStrategy offers a wide range of data analytics capabilities. You can connect MicroStrategy to various data warehouses and relational systems to access data, thus adding to its data accessibility/discovery capabilities.

Conclusion:

Data scientists use a lot of tools to reduce latency and errors while analyzing big data. The above Data Science tools list includes some of the most widely used tools in the industry. The tools for data science are for analyzing data, creating aesthetic and interactive visualizations and creating powerful predictive models using machine learning algorithms.

Most of the data science tools deliver complex data science operations in one place. This makes it easier for the user to implement functionalities of data science without having to write their code from scratch.

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