Top Data Analytics Tools Every Data Scientist Should Know About

“Data is the oil of the 21st century” is a saying that we hear a lot. Today, most organisations emphasize data to drive business decisions. We are currently in a revolution in which we are surrounded with computers, smart phones, smart devices which are constantly connected to some network of some sort.

Data generation has risen exponentially and will continue to grow in the coming decade. Data Analytics hence plays a major role to uncover the patterns beneath the data. Data can not only help companies, but can also help the government and several organisations to overcome challenges using a Analytics driven solution. There are various types of Analytics solutions:

• Descriptive Analytics: Analysing the past data and understanding what happened.
• Diagnostic Analytics: Analysing the past data and understanding why it happened.
• Predictive Analytics: Predicting what will happen in the future, using Machine Learning modelling.
• Prescriptive Analytics: Suggest recommendations on actions that can be taken to affect the outcome.

As we can see there are majorly 4 types of analytics that can be done. There are various tools that can help one achieve the desired analytics required.

Data Analytics Tools

Microsoft Excel

Excel is the most common tool for analysing spreadsheets. Over the time and over a decade of developments, Excel can perform standard analytics using VIsual Basics coding. There’s a limit of 1 million rows though. Excel is good for analysing structured data. The graph output is quick, but the output is very basic and non-interactive.

It can easily be connected with other data sources (access, sql). But the very common drawback is that it is less sophisticated and doesn’t dive deep into a specific niche. The formula option comes very handy for modifying the data, but performing high level transformations can be a little difficult. The biggest drawback is that it is not suitable for big-data analysis. 

Python or R 

Both python and R are leading Analytic tools used in the market. While R is more focussed towards statistics and data modelling, Python is known for its Machine Learning libraries. Nevertheless, both languages are more than capable of performing data transformations and handle large amounts of data.

As both of them are open source softwares there are a wide range of libraries available that can act as a niche for specific analysis. Natural Language processing and Computer Vision comes into picture here. Python is highly regarded for NLP and CV. As the support of deep learning is also available in the form of libraries like Theano, Keras, Tensorflow, Pytorch.

The benefits of using programming languages for creating analytics solutions are immense. One can create products which take in data and does all the analytics on it and return the desired result. Same integrated with proper UI and UX can help build an end to end product, with integrated machine learning models.

One of the biggest drawbacks of Python is its speed. There’s no support of parallel processing as in Apache Spark. Sometimes ML models take hours to run. Although it performs better with Deep learning models if a GPU is provided. 

Tableau or Power BI

Tableau and Power BI are very powerful tools for data analytics, dashboarding, visualisations and reports. These can be shared over desktop and mobile browsers (in case of tableau) and mobile apps (in case of PowerBI). Tableau uses VizQL as its core query backend.

These tools can be categorised as Business Intelligence tools which are ideally responsible for descriptive and diagnostic analytics. Due to the recent innovations in ML technologies, there are options of building some automated Machine Learning models in Power BI that are integrated with Azure Machine Learning.

Both softwares provide an option of on-premise or cloud deployment. Although these softwares are very much related to each other, the major difference is power and speed. Tableau is more powerful and fast compared to PowerBI. This difference comes from the fact that PowerBI used SQL language as it’s backend which is a tad slower compared to VizQL that is homemade by Tableau.

Nevertheless both the tools are very dynamic and flexible when it comes to connecting with the data source. They also support real time data updates (in the database).

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SQL

SQL (Structured Query Language) is not actually a tool but a programming language which was originally designed for managing data in a relational database. It’s one of the most commonly used languages to access databases today, even though it has been around since 1970.

SQL is commonly used for Software Development, but it’s becoming a mandatory skill to have for Data Analysts. The programming on SQL is easy to understand and learn. SQL is integrated with various visualisation tools too, for example redash uses SQL queries to extract data and perform visualisations on it.

There are so many database softwares which uses some specific versions of SQL language to access data. For example, OracleDB, MsSQL server, PostGreSQL etc. Hence SQL is very highly regarded in the world of data analysis. SQL is great for performing joins on several tables and extracting the desired data. Aggregations after using Group By can be used on a much larger dataset, compared to pivot tables in spreadsheets. 

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SAS

SAS institute is a software company and the developer of SAS analytics software which uses SAS programming. The products offered by SAS are very versatile. SAS initially was used for statistical analysis and Data visualisation.

It is one of the most widely used tools by various organisations for Data Analysis. Over the period the SAS suite has grown with time. Now there are many other options rather than just descriptive analysis. SAS offers forecasting, Machine Learning and also text analytics.

This gives SAS a major boost in the market of Data Analysis. But with such versatility comes higher costs. SAS has one of the costliest products because of the huge amount of development that goes behind in building the product. SAS is definitely one of the best and easy to use softwares out there for Analytics Solutions.

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Google Data Studio

Google data studio is a free Dashboarding and Visualisation tool offered by google. It can easily be connected to Google Analytics, Google Ads, and Google BigQuery for building data pipelines easily.

BigQuery on the other hand supports various Machine Learning models. Hence it gives an upper hand of using various models on the cloud. There’s an upcoming support for Auto-ML that looks promising and could revolutionize the world of Data Science. Data Studio can work with data from a variety of other sources as well, given that the data is first replicated to BigQuery using a data pipeline like Stitch.

Data Studio is a 100% managed and cloud based service. There’s no requirement to install or maintain infrastructures. All the servers are set up by google itself. Although Data Studio is easy to use, it fails while creating more sophisticated dashboards. Complex visualisations aren’t possible.

There isn’t an option to modify or customise visualisations as provided by Tableau. Hence the dashboards might sometimes look very simple. One consistent feedback about Data Studio is that loading the dashboard becomes exponentially slow with the increase in complexity of functions that are part of the view.

This is a side effect of the live connection mechanism and the workaround is to use a scheduled extract in cases where performance is critical. Data Studio can be used when an organisation is using the google ecosystem for storing the data and moderate analysis is required on the data.

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Jupyter Notebook

It is an open-source application useful for creating interactive documents. These documents can blend together equations, live code, narrative text, and visualizations. It is one of the most interactive data analytics tools, quite similar to Microsoft Word. 

Jupyter Notebook is compatible with different browsers. It can support multiple languages, including R and Python. Jupyter Notebook can integrate with different data analytics tools like Apache Spark. Moreover, it supports different outputs, from HTML to videos, images, and more. 

But the version control of Jupyter Notebook is not up to the mark, which makes tracking changes difficult. Therefore, it isn’t very suitable for collaboration. However, Jupyter Notebook is invaluable when it comes to presentations and tutorials. 

KNIME

KNIME is a cloud-based, open-source platform for data integration. A few software engineers at Konstanz University in Germany developed the data analytics tool in 2004. The tool was primarily introduced for the pharmaceutical industry. But the tool has gradually become quite popular in other industries. 

The good thing about KNIME is that it collects data from multiple sources and stores them in one specific system. Some popular areas where the tool is used include business intelligence, customer analysis, and machine learning. Apart from being free, the smooth usability of the tool also makes it immensely popular. 

KNIME is ideal for visual programming because of its graphical user interface. Therefore, users don’t need much technical expertise to build data workflows. KNIME is one of the best statistical tools for data analysis. But it is also quite efficient for data mining.  

Users will need to possess in-depth statistical analysis knowledge to leverage this tool. Additionally, users will also benefit from having familiarity with R and Python. However, the open-source nature of KNIME makes it customizable as per the needs of an organization. Furthermore, the low costs of KNIME make it immensely popular among small businesses with limited budgets.  

Apache Spark

Apache Spark has a software framework to help data analysts and scientists quickly process vast data sets. It was developed in 2012 and later donated to the Apache Software Foundation. The primary purpose of introducing Apache Spark was to evaluate unstructured big data. Spark holds the potential to distribute computationally heavy analytics tasks across various computers. 

The framework of Apache Spark might not be entirely unique. You will come across multiple frameworks that are similar to Apache Spark, and Apache Hadoop is an example of that. But Spark is faster than all frameworks that are similar to it. 

Apache Spark uses RAM instead of local memory. Therefore, it is 100 times faster than Apache Hadoop. Due to its fast nature, Spark is often used for developing machine learning models that are too heavy on data. 

One of the most valuable data analytics tools in research, Spark also comes with an extensive library of machine learning algorithms called the MLib. It includes regression, classification, clustering algorithms, and more. But the tool is a little on the expensive side because it consumes too much memory. 

Another downside is that Spark does not have any file management system. However, the issue can be solved by integrating it with other software like Hadoop. 

Conclusion

We had a quick look on the various tools used in the field of data analytics. Each tool has its pros and cons. But one can make sure to find the right tool that will be suitable to the requirements. The world of data analysis has evolved a lot and it has given rise to developments of many tools. Hence there’s a lot to choose from.