Top 10 Data scientists tools in 2021
As without saying, Data science is the best growing field and in demand for the past couple of years, and this curve keeps to grow for the upcoming years. Here go the top 10 tools, that you should know to kick start your career in this field. Of course, knowing only about the tools doesn’t do, a lot of practice and projects should be taken off to have an overall view of what the field is. But first, let’s discover the top toolkits available to discover the insights about the data. Let’s go from the bare minimum and basics tool to the high end one.
1) Microsoft Excel
As you have guessed, it’s a well-known excel. Almost about 50 to 60 percent of data analysis problems can be solved with excel. Microsoft also adds on various features to Excel to make our tasks and problems solved easily and quickly. With the advent of VBA (Visual basics of applications), it has become much popular and powerful. This allows users to create macros, record them, and playback number of times, and execute data processing functions. Excel’s pivot table is also the best and easy to use the feature as well.
2. Structured Programming Language (SQL)
This is an important skill to be developed if your aiming a career in data analysis and data science. SQL in basic terms is used to communicate or talk to our databases. As data would normally be stored in databases, we should extract it or manipulate it with SQL language. Developing this skill is not that difficult since it evaluates our English language. For example, say for instance I want to retrieve the data from the customers table in ABC database. it would normally be
USE abcdatabase;
SELECT * FROM customers;
There are numerous youtube videos and courses available online so that you can learn this as quickly as possible.
3. Python
Python has emerged to be the best general-purpose programming language from the past decade because of its easy syntax to read and write. Python has also become the best language for doing data analysis stuffs as well, due to its large community support and plenty of open source frameworks and libraries. Developers add newer frameworks and libraries at frequent intervals to enrich python so the data analysis works are simplified with it. Some well-known python libraries to use in data science works are pandas, numpy, scikit learn, matplotlib, scipy, etc..
4. R
R has been in use before the advent of python in the data analysis field. While python is a general-purpose programming language and used for regular data analysis stuff, R is primarily meant for statistical analysis. Developers of R have developed it to be the best statistical software programming language, with rich collections of libraries in it. Approximately it is equipped with 16000+ libraries in its central repository, CRAN (Comprehensive R Archive Network). But getting used to R and learning its syntax and semantics is quite hard when comparing with python, but once mastered will provide an upper hand. Some of the popular R libraries used in data science are dplyr, plotly, ggplot2, lubridate, stringr etc..
5.Tableau
While Python and R have themselves been equipped with visualization libraries, Tableau provides and easy to interface for data visualization without writing explicit codes and performs well with larger datasets. It is just a drag and drop tool so that is easy to learn and use. If your analysis is to compare the visuals by grouping them with various categories, you should definitely give a try with Tableau. Other alternatives to tableau are Qlikview, RapidMiner, Sisence, etc..
6. Jupyter Notebook
This is an IDE (Integrated Development Environment) for python programming language and ideally used by the majority of the data scientists all over the world. This allows the users to execute python codes not in big chunks but in separate code cells so that we perform one at a given step to get its output. This is one of the prominent features of it. It can be downloaded for free from Anaconda, open-source package downloader tool. In fact Google gives us online google colabs which can be considered as an higher-end version of a jupyter notebook with an easy-to-use interface. Some alternatives are Spyder, pyCharm etc..
7. Orange
Orange is primarily a data mining and machine learning algorithms enriched software for nonprogrammers. This is an open-source free to use software for performing much of the data analysis stuff. Its drag and drop interface lets you quickly see the results and compare against the standards within no time. It builds like a hierarchical tree, with a certain task carried out in separate blocks. If you want to do Text mining and other related tasks, just give Orange a try.
8. TensorFlow
TensorFlow is a Google’s opensource deep learning framework, programmed in python that has all within it for developing easy to complex machine learning and deep learning models. Of course, you could build different models and come with the best one that fits your data well, Since model performances vary drastically with different sizes and types of data. You can integrate your model on the web using TensorFlow.js that makes helps your model to train online. For mobile platforms like Android, it has TensorFlow lite. Google has kept it up to date with all the different deep learning models coming out with an easier to read and write syntax and methods. It has excellent community support and really good documentation as well. Some alternatives to TensorFlow are Caffe, Theano, DL4j, etc..
9. Apache Spark
Of course, big data skills are also essential to become a data analyst or scientist nowadays, since data are being generated at a faster pace. Spark provides solutions to handle, manage, and analyze big data, coupled with Hadoop in a parallel distributed computing manner. Initially, Hadoop map-reduce service was popular for doing big data analysis stuff, but the advent of Spark has pulled it out of it because of its in-memory storage mechanisms and RDD’s(Resilient Distributed Datasets). Even though, it internally relies on HDFS(Hadoop distributed File Systems) to access data in a parallel distributed manner, it can also execute alone on its inbuilt cluster manager. So gaining Spark skills will definitely give you an additional tool in your data science toolbox, as it is the best big data analyzing platform.
10.Scala
Scala is actually a functional style programming language to perform tasks in Spark. Though Spark supports python, Java, and recently R as well, scala is the best language when working with spark since it is faster than the former languages. Almost scala is roughly 17 times as fast as python when used in Spark applications. Since Spark is written in scala, new libraries and packages would be available to it earlier than other languages as well.
So, we come to the end here, the above are the best tools for data analyze and data science tasks and almost all of them are free and open-source tools so you can download them quickly and use it. There are various alternatives available for each other but these stand out of them. Hope so, you gained a good knowledge and some of the best data science tools to add to your toolkit.
