Tools to make you an expert data analyst

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Dr. Dhanya Pramod

Indian analytics market will grow at 26 per cent CAGR to $16 billion market by 2025. The global outlook is equally positive, with research highlighting that it will grow at 29.7 per cent to $40.6 billion market by 2023. These figures clearly indicate that data analysts can look forward to a fast-track career path.

The demand for data analysts has grown significantly in the past two years across banking and financial services, energy and utilities, telecommunication, e-commerce, pharma, healthcare, retail, startups and many other sectors. According to a report, India witnessed a 45 per cent jump in analytics job opening last year from 2017. Currently, there are around 97,000 open positions in data analytics and science domain, and the number is expected to rise phenomenally in the coming years.

Now, if you are aspiring to pursue a rewarding career as a data analyst and become an expert, then knowledge of analytics tools is a pre-requisite. There are several data analytics tools that exist today, but the following five are the most essential to know:

SAS

SAS is a statistical software suite that has found its application in predictive modelling, data mining and management, reporting and business intelligence. The fact that it is easily accessible and manageable, and provides comprehensive reports to the decision-makers makes it a widely used data analytics tool.

It can analyse massive amounts of data from any source without any issues.

The key features of SAS comprise visual data exploration, insightful analytics, integration with MS Office tools and self-service ad-hoc reporting, among many others.

SAS is one of the oldest programming tools and has evolved into a wide range of products and modules.

R Programming

R programming is often compared with Python and SAS, but it has its own merits when you want a reliable tool for statistical computing and graphical methods.

Some of the leading tech giants in the world use R as their programming language. It provides a diverse range of techniques such as linear and non-linear modelling, machine learning algorithms, time-series analysis, classical statistical tests and many more.  Moreover, it is an open-source and free to experiment tool. It is compatible with Windows, macOS, UNIX and many other platforms.

There are more than 10,000 R packages in CRAN, the global repository of open-source packages and in other public domain which makes it easier for data analysts to customise their solutions.

SQL

SQL (Structured Query Language) is the ubiquitous, special-purpose programming language that is leveraged by data analysts and software developers to story, query and access data, especially in RDBMS or RDSMS.

All major RDBMS/RDSMS have SQL integrations, which makes it easy and quick to navigate between different systems.

The main advantage that works in its favour is its ability to handle most taxing problems in spite of its simplicity of use. It can pull information in a matter of seconds, which can be critical when there is need to make quick but informed decision. It is also an open-source tool and can easily integrate with Python, R and other database management systems.

Microsoft Excel

Microsoft Excel is a go-to-resource for people and businessmen, who want to record, maintain and analyse any data. However, when it comes to big data analysis, this spreadsheet program takes an entirely new dimension with its powerful functions and tricks. It can sort, organise, visualise, manipulate and analyse the most complex and voluminous data using various Excel formatting, arithmetic functions and pivot tables. Whether it is mathematical, statistical, finance, accounting, engineering or any operations,

Excel has a wide range of modelling capabilities. While Python, R and many other languages can handle large database, Excel can be used in parallel to enhance the overall result of analytics.

These five tools are akin to the foundation of data analytics. Once you master them, you can easily acquire proficiency in other data analytics tools and make your resume stand out from the crowd.  It is also equally important to have strong academic qualification to pursue a career as a data analyst expert. An MBA in data science and analytics can equip you with required knowledge and skills to excel in this field.

— The writer is Director, Symbiosis

Centre for Information Technology (SCIT)

Python

Python is the most popular, versatile and open source data analytics tool. It is an object-oriented scripting language that is easy to learn, read, write and maintain due to its similarity with PHP, JavaScript and Ruby. It has only fewer lines of code. It is compatible nearly with every system architecture and applications. Since it is an open-source tool, it gives you extensive access to frameworks, development tools and machine learning libraries such as Pandas, Keras, LightGBM, etc. This in turn considerably reduces the software development cost and time, a reason why most companies prefer data analytics professionals with Python knowledge.