How to Get Started with Data Science on a Shoestring

To sustain a competitive edge in modern markets, enterprises of every scale and domain now acknowledge the significance of adopting a data-centric approach in their business operations. This can entail leveraging data from various sources by analysts to reveal actionable insights, or integrating machine learning (ML) technologies and intelligent automation solutions built on corporate data by developers, and deploying business intelligence (BI) dashboards by CEOs to monitor overall company performance.

Given the constantly advancing nature of data science, enterprises are now compelled to recognise its immense potential and proactively embrace it. A well-thought-out financial strategy is crucial to laying the foundation for an effective data science stack. Implementing machine learning techniques is fundamental to building the data science stack, as it enables businesses to augment customer service and gain valuable insights for senior management.

Given the symbiotic relationship between data science and business operations, it is crucial to identify the most suitable stack for data architecture. The right choice of tools and equipment can help businesses to curtail expenditure on advertising, development and infrastructure, while facilitating management of the platform in its entirety.

Data scientists lean on a comprehensive set of tools, known as the “tech stack”, which incorporates a modelling framework and a runtime environment for inference operations. This tech stack encompasses a wide range of technologies and processes related to data engineering, including business intelligence and model deployment.

This article will delve into the key factors to contemplate while building a cost-efficient data science tech stack. We will start by briefly outlining the elements that make up a data science stack.

An Overview of the Data Science Technology Stack

In a conventional corporate setting, data is acquired from diverse departments and systems, and stored centrally in a data lake. The data lake is a massive pool that preserves data in its native format, regardless of its origin. The data is subsequently processed and migrated to a data warehouse for further scrutiny.

Data scientists and business analysts perform data warehousing tasks by fabricating analytics modules and reports that can be repurposed. In specific scenarios, the data warehouse serves as a cornerstone for a module that generates extensive descriptive insights. Furthermore, a set of ingredients are integrated with transactional systems to yield real-time outcomes. Both models are frequently accessible via web interfaces, allowing for independent scalability and deployment.

Constructing the Optimal Data Science Toolkit

While selecting components for an analytics and data science stack, it is essential to consider various factors and comprehensively examine all possible alternatives. Before putting together a data science toolkit, the following aspects should be taken into consideration:

  • Which do you prefer, on-premise or cloud services, and why?
  • Do you possess the capability to design your own models and analytics tools, or do you require assistance from someone with programming experience?
  • Have you considered investing in a cloud service provider?
  • Do you believe that continuous data collection and analysis is necessary?

After examining the components in depth, it is crucial to bear certain factors in mind while picking the stack during pivotal stages of the process.

  1. Information Repository

    The selection of a data warehouse primarily depends on whether an on-premise or cloud-based solution is chosen. Cloud-based software-as-a-service (SaaS) solutions are highly beneficial as they require minimal maintenance and can aid in focusing on the core analytics obstacle without distractions.

    The most commonly employed on-premises solution consists of an execution engine like Apache Spark or Apache TEZ together with a querying layer such as Apache Hive or Presto. The primary benefit of this architecture is that organisations have total control over their data. Additionally, Apache Spark provides the ability to construct custom analytics and machine learning applications. Furthermore, some querying engines, like Presto, already integrate fundamental machine learning features.

    Cloud-based services such as Redshift, Azure Data Warehouse and BigQuery are often preferable over on-premises systems, particularly if organisations lack the necessary coding expertise to manage on-premises systems. These cloud-based packages include pre-built machine learning (ML) modules which can be easily accessed and employed.

    For several years, Google BigQuery Machine Learning (ML) has been available for use, while Amazon Web Services (AWS)’s Redshift ML has just recently been introduced. As such, those interested in constructing Machine Learning models directly from their cloud data warehouse may find Google BigQuery and Microsoft Azure ML to be more dependable options than AWS.
  2. ETL (Extract, Transform, Load)

    The precision and potency of any analytics or machine learning module is dependent on the quality of the characteristics used to train it. These input features are generated by the Extract, Transform, Load (ETL) tool. When hosting a Spark-based transformation function locally, users can choose to write their own code in Python or Scala, or utilise the Spark SQL language.

    To guarantee reliable feature development, it is necessary to create schedules and structures. Pentaho Data Integration is an open-source option that can be used, though it is not as customisable as a bespoke solution.

    Cloud Dataflow from Google, Databricks from Microsoft Azure, and Glue from Amazon Web Services are all excellent choices for Software-as-a-Service (SaaS) implementations. These solutions provide native data science capabilities and code automation through graphical user interfaces. However, they may be more specialised towards their respective stacks (e.g. Glue for AWS, Databricks for Azure); and may not offer support for external cloud-based data sources.
  3. Business Intelligence and Visualisation Tools

    Exploratory data analysis (EDA) heavily relies on business intelligence and visualisation tools, which makes them an integral part of a data scientist’s technology stack. Popular on-premises applications include Tableau and Microsoft’s Power BI. For those requiring code-based solutions for data visualisation, Python libraries such as Seaborn and Matplotlib are also useful choices.

    Amazon Elastic Compute Cloud (AWS) QuickSight, Google Data Studio, and Microsoft’s Azure Data Explorer are all excellent Software-as-a-Service (SaaS) solutions for data visualisation and analysis. AWS QuickSight goes a step further with basic machine learning capabilities that can be utilised to generate insights, such as identifying outliers and making predictions. Moreover, self-generating dashboards can be constructed using AWS QuickSight. If you are already using Amazon’s cloud stack and have not fully integrated data from other sources, leveraging their services can be advantageous.
  4. Methodology Architectures for Data Analytics and Machine Learning

    For the past few years, Python has been the unrivalled leader in constructing custom machine learning and analytics applications. Among the most widely used libraries for statistical analysis and modelling are Scikit-learn and Statsmodels. Additionally, due to its comprehensive set of statistical modelling capabilities, R is extensively employed in the manufacturing industry. Additionally, other deep learning frameworks such as TensorFlow, MXNet, and Pytorch can also be utilised.

    If you have chosen Java as your language for development, Deeplearning4j is an excellent option to consider. Community support is a crucial factor to consider, as most developers may require extensive research before completing the model pipeline. If your organisation does not employ Machine Learning specialists or you want to design custom models, many cloud service providers offer models for Machine Learning and automated model building as a service. With tools such as Azure Machine Learning, Google Cloud AI and AWS Machine Learning Services, models and intelligence can be created without the need for any code.
  5. Deployment Stack

    Once the models have been developed, they should be deployed to use for real-time or batch inferencing. If an on-premises setup is opted for, the models must be packaged in a web service framework, like Flask or Django, and then delivered using Docker containers. After that, the models can be horizontally scaled with either a container orchestration framework or a load balancer. It is important to consider the level of effort and expertise required to complete this task.

    Optimising inference modules for speed is a challenging task, and it often necessitates using more advanced approaches such as batching and threading. Instead of dedicating significant resources to creating one’s own solutions, it is more efficient to utilise the deployment tools available in popular machine learning frameworks like TensorFlow, MXNet, Pytorch, and others.

    Cloud providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure offer machine learning (ML) serving options to streamline the complex deployment process. Custom models not developed within these cloud providers can also be To sum up, several factors must be taken into account when constructing a data science stack with limited resources. The company’s size and the chosen path are both crucial determinants in this process. Analysing the organisation’s requirements and resources is crucial in making cost-effective decisions and achieving optimal outcomes.

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