Top Data Science Tools that Every Data Scientist Must Know

07-Dec-2020

Introduction -

Careerera’s Post graduate program in Data Science is a very popular and carefully designed data science certification course. Its course curriculum has been created with the latest data science practices and developments in mind. It includes most of the current state-of-the-art technologies of the field of data science within it. The instructors of the course are all highly trained and experienced faculty, with decades of experience from working in the industry. They follow the most current and contemporary teaching methodologies and techniques to educate the learners in the course.

Also Read: Top Data Science Interview Questions and Answers

The PGP in Data science course contains many highly relevant tools and technologies within its curriculum. As the field of Data science has expanded, the process of work of data scientists has grown more and more refined. Today, Data science has become the most popular field of computer science with the largest number of opportunities present within it. All the trending topics of the technological world come from the field of Data science. All the most exciting breakthroughs in the software industry have their origin in the field of Data science. Thus it is not strange or bizarre at all that the field of data science has a number of sophisticated and even exotic tools present in it. Data scientists today have a vast array of Data analytics tools available at their fingertips.

Careerera’s PGP in Data science course teaches the learners how to operate and wield many of the tools effectively to achieve their ends. Although the list of Data science analytics tools available in the market for free and for a price is too large to list in a single blog post, we present below the tools which are specifically used in the Post Graduate Program in data Science course. Some of them are open source and free, some of them are proprietary but free, and some of them are proprietary and are available after paying a price.

1. Microsoft Excel

This is a piece of software which most computer savvy people have encountered at least once during their lives. It is a very common and familiar software application. When it was launched, it proved to be a revolutionary software application. It was Microsoft Excel which introduced the public to the concepts of spreadsheets and tabulation of data. It popularized many data science archiving and accounting concepts in the public such as spreadsheets, tables, rows, columns, macros, and mathematical formulae.

It the most common tool for spreadsheet analysis since it has had many years of development revolving around and focusing on spreadsheets. It is very well suited to analyze and manipulate structured data which is stored in spreadsheets. It can also produce graphs but the output is very basic and not interactive at all. There is unfortunately a limit of 1 million rows in it and thus it is not very suitable for big data analysis.

2. Python or R -

Python and R are programming languages. They are very well suited for writing scripts and large programs. Data scientists use them very commonly to perform data analytics operations on both small and large data sets. The advantage of using programming languages as data analytics tools is that they can be customized to create specialized applications or end to end products. These applications can contain the niche functions that the data scientists require to perform their analysis on the data sets. Python in particular is very good at data modelling. There are many libraries available in the programming communities for Python. Many of them deal with data analytics and provide all kinds of - data analytics functions, from large scale operations to small scale minute adjustments. R is very good at handling data transformations.

3. Tableau or Power BI -

Tableau and Power BI are both very handy tools for data analytics. Both are very rich with features and options for various functions, both large scale to minute. They can perform the actions of visualization, dash boarding, data analytics, and even generate reports. They are available on both the desktop and mobile platforms, with web backup, syncing, and cloud computing capabilities. The core backend for the purpose of making queries in Tableau is VizQL.

They are fully equipped with Machine learning technologies and Power BI even provides many features of generating and building automated Machine learning models. One drawback of Power BI is that it is quite slow compared to Tableau. The difference in speed arises from the difference in their backend. Power BI uses SQL as its backend whereas Tableau uses VizQL which is a custom query language.

4. SQL -

SQL is a programming language which was designed in the early 1970s. From its inception to its design it was always modelled as a language whose function domain would revolve around data, data sets, and databases. It is the most common programming language used for managing databases in the world. Even though it is a programming language used for software development revolving around databases, it is rapidly becoming a required tool for Data scientists for the purpose of data analysis.

This is because SQL bundles with it all the capabilities of a full fledged programming language. A data scientist can use it to make customized queries which can retrieve any particular data set according to the requirements of the data scientist. It also contains data visualization and data processing capabilities. Many database software use it as their query languages, such as PostgreSQL, MsSQL, OracleDB etc. One can perform joins on multiple tables with it and do data aggregation of large data sets.

5. SAS -

SAS is a programming environment which is centered around data modelling and data manipulation. It was created by the SAS Institute as far back as in 1966. Since then it has undergone continuous development and many improvements have been added to it. It is chockfull of features related to statistical analysis and data visualization. SAS offers a full suite of software revolving around Data analysis and each of its components is very helpful and essential tool for Data scientists. The SAS suite provides features such as text analytics, machine learning, forecasting, and descriptive analytics.

One thing which should be noted is the SAS suite of software is one of the costliest data analytics tools software in the market. This is because of the long duration of development which has been put into upgrading and improving it.

Conclusion -

The above are some of the most common and important tools used by Data scientist to perform evaluation and analysis of data. They are also included in the PGP in Data science certification training course which Careerera provides. From this small sample one can imagine what a well designed and productivity-enhancing course it is. Please enroll now to learn how to use these and many more tools!

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