Top Analytics tools Every Data Scientist must Learn

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Becoming a successful data scientist is not just about knowing statistical concepts, data analysis techniques, algorithms and data model very well. Data scientists also need to master the right set of analytics tools in order to carry out their tasks efficiently. These tools fall in various categories based on the data scientist’s role in data analytics, such as:

  • Data capture and tracking tools
  • Data processing, cleansing, and data transformation tools
  • Data visualization tools
  • Data modeling and analytics tools

In this article, we are going to discuss a few most important tools every data scientist must learn in order to sharpen their analytic skills and deliver impressive results with optimum efficiency.

Tableau

Tableau is one of the markets leading data visualization tools. It allows you to transform data into a vast range of compelling visuals, charts, and dashboards. This, in turn, allows you to discover the insights your data consists of. It supports integrations with numerous data sources and various mechanisms of importing data points, along with an ability to massage data to suit your chosen visualization.

Available as a desktop application, an on-premise server application, a public cloud hosted as well as fully hosted by Tableau, it fulfills the need of almost every I.T. setup, ranging from an individual data scientist, and start-up to a very large enterprise.

Pros:

  • Widest range of visualizations supported.
  • A large variety of support data formats and data import mechanisms.
  • Wide range of deployment and hosting options available.
  • Great customer support.

Cons:

  • Expensive.
  • No free version available.
  • Limited ability to version control the visualizations.
  • No support for scheduling reports and notifications.

Pricing

There is a one-month free trial available. The pricing starts from $70 per user/month and includes several pricing plans.

Heap Analytics

Heap is a modern tool for tracking users’ behavior in the web and mobile applications. In addition to any web and mobile application, it is able to automatically obtain data from a number of SaaS services (CRMs, marketing automation tools, payment processors, etc.) to attach user-level properties and event data seamlessly to your users in Heap. It is available for installation on many different types of applications, including hybrid mobile apps, native mobile apps for Android and iOS, Kony, React Native, Javascript based apps, and also has support for popular CMS and e-commerce applications, such as WordPress, Shopify, etc.

Pros:

  • A minimal amount of coding required.
  • Automatic tracking of most events, unlike most other analytics tools.
  • More control with the Heap user instead of the developer on how to define visualizations and reports.
  • Support for a range of application types.
  • Great documentation (API and product documentation, both).

Cons:

  • A relatively newer player in the market, maturity is yet to be understood
  • No API to export data.
  • User is the topmost entity in its data model. This disallows creating funnels of any other entities than users.
  • Basic HTML skills required for tagging.

Pricing

Heap’s pricing starts with a free trial having limitation of a single user and a limited number of data points, which is great news for websites/apps with lower traffic, maintained by a single user. For other plans, it is worth visiting https://heap.io/pricing.

Alteryx

Alteryx is a great tool specializing in self-service data analytics. It provides a great user interface to collect and process data from multiple sources, followed by performing analytics on the refined data sets. In addition to offering a great ETL (Extract, Transform, Load) capability to perform the data transformations and transfers, it also is equipped with the complex analytics capabilities, including predictive, spatial, and statistical. A highly popular feature of Alteryx is to obtain the data from multiple data sets, visually clean and blend it and then, share it with the data visualization applications, such as Tableau or PowerBI.

Pros:

  • Highly scalable.
  • Supports many data sources, such as spreadsheets, cloud or on-premise data stores, AWS, and Salesforce.
  • Excellent analytics capabilities.
  • Great user experience.
  • Ability to export datasets directly to popular data visualization applications.

Cons:

  • Expensive.
  • Not great at data visualizations.
  • Occasional glitches and errors in reading and updating data.

Pricing

Despite a limited free trial, Alteryx is an expensive product and starts with $5,195

/user per year. Other pricing plans can be seen at https://www.alteryx.com/products/platform-details/pricing.

MixPanel

Similar to Heap Analytics, MixPanel is also a web and mobile analytics tool and excels at a few important aspects of analytics. In addition to event tracking and capturing of user-related data points, it allows A/B testing, predictive analytics, and can identify behaviours that correlate to high retention. In addition, it includes features related to messaging with efficient targeting and also, provides analysis on target recipients of the messages. It also offers a machine learning interface to help discover the insights in a data set.

Pros:

  • Excellent customer support
  • Custom event-driven data model
  • A/B testing capability
  • Funnels and events

Cons:

  • Doesn’t include attribution to support advertising analysis.
  • Tag management is not available.
  • Gets expensive with an increase in data points.

Pricing:

It includes a free trial which works very well for start-ups or individual webmasters. On the paid plans, its pricing starts $999 per year and can be easily calculated using their pricing tool available at, https://mixpanel.com/pricing/. It also offers a monthly plan in addition to a yearly plan.

Google Analytics

Google Analytics is one of the most popular tools that allow you to track and capture user behaviors from the web and mobile apps. With the widest follower-ship, Google Analytics is almost a benchmark all other solutions are often compared against. In addition to capabilities like tracking, attribution, tag management and visualizations, it integrates very well into the large ecosystem of tools powered by Google. Also, it offers a free tier which suffices for most of the needs a typical website or mobile app has. 

Pros:

  • Integrates well with the remaining components of the Google ecosystem.
  • Attribution
  • Funnels and custom events.
  • Tag management using Google Tag Manager.
  • Free for the majority of use cases.
  • Very intuitive user interface.

Cons:

  • No ability to track PII (Personally Identifiable Information) and that can be a major showstopper for a business.
  • Limited ability to download datasets. Only the data sets visible on a screen can be downloaded, not the entire dataset matching a condition.
  • No A/B testing or custom analytics capabilities.

Pricing:

Standard plan of Google Analytics is completely free to use. However, for advanced and high data-centric usages, Google also offers a commercial product, Google Analytics 360, which supports a much larger number of data points, premium support, custom metrics, etc. While its pricing is not publicly available on Google’s website, it can be found here.

Amplitude

Falling in the family of Google Analytics and Heap Analytics, Amplitude is also a challenger with impressive tracking capabilities. In addition to the analytics related features, Amplitude has a distinctive edge with its product packaging, compliance, performance, and scalability it offers. It offers one of the most feature-rich free tiers that includes unlimited data retentions, ability to track up to 10 million data points and unlimited user seats per free license.

Pros:

  • Great performance of data retrieval.
  • Support for SQL queries.
  • Compliant with GDPR, SOC Type 2, ISO 27001, EU Privacy Shield.
  • Highly scalable and thus, suited for high traffic applications too.
  • Powerful free plan with unlimited data retention.

 Cons:

  • A relatively complex user experience that involves a learning curve for beginners in web analytics.
  • Report customization requires a short learning curve.
  • Ability to track an omnichannel experience of a user requires some training.

Pricing:

Although Amplitude offers a great free plan, which is sufficient for many use cases, their pricing for the Growth and Enterprise plans is available upon contacting their sales team.

Conclusion

In addition to the above tools, a data scientist should be well versed with other technical skills, such as programming using R and Python, SAS, and Microsoft Excel. A combination of skills such as statistics, modelling techniques, algorithms, data science-related programming and expertise in using the analytics tools can ensure a bright and shiny career as a data scientist.

Jigsaw Academy’s certification programs can give you a head start towards your journey of becoming a successful data scientist. In addition, Jigsaw Academy offers an excellent array of training and courses to choose from. These programs are designed to give you an in-depth understanding and 360-degree analysis of data science.