How to Get Started with Data Science on a Shoestring

In order to remain competitive in today’s markets, businesses of all sizes and across all industries are recognising the importance of incorporating data-driven decision-making into their corporate culture. This can take many forms, from analysts utilising data from multiple sources to uncover valuable insights, to developers implementing machine learning (ML) tools and intelligent automation systems built upon corporate data, to CEOs utilising business intelligence (BI) dashboards to monitor the performance of the company as a whole.

The application of data science has become increasingly sophisticated, making it impossible for businesses to overlook its potential. Establishing a successful data science stack starts with a well-planned financial strategy. Machine learning is an essential component of the data science stack, allowing companies to improve customer service, and access useful insights for upper management.

Given the interconnectedness of data science and business operations, it is essential to determine the optimal stack for data architecture. Making the correct selection of tools and equipment can reduce the amount of time and money spent on advertising, development and infrastructure, and can also simplify the process of maintaining the platform as a whole.

Data scientists rely on an extensive suite of tools, referred to as the “tech stack”, that encompasses a modelling framework and a runtime for inference operations. This tech stack also includes all the technologies and processes associated with data engineering, such as business intelligence and model implementation.

In this article, we will explore the essential elements to consider when constructing a cost-effective data science technology stack. To begin, we will provide a brief overview of the elements that comprise a data science stack.

Data Science Technology Stack: An Overview

In the typical business environment, data is collected from various departments and systems, and stored centrally in a data lake. This data lake is a large repository that holds data in its original format, irrespective of its source. The data is then processed and loaded into a data warehouse for more in-depth analysis.

Data warehouse work is completed by data scientists and business analysts who develop analytics modules and reports which can be reused. In certain cases, the data warehouse acts as a foundation for a module that creates descriptive insights in large quantities. Additionally, a set of components are incorporated with transactional systems to produce immediate results. Both models are often available through web interfaces to provide independent scalability and deployment.

The best way to build a data science toolkit

When deciding on the components for an analytics and data science stack, it is important to take into account a variety of factors and explore all available options. Prior to assembling a data science toolkit, it is advisable to consider the following points:

  • Which, on-premise or cloud services, do you prefer and why?
  • Can you create your own models and analytics tools, or do you need someone with programming experience to help you?
  • What about making an investment in a cloud service provider?
  • In your opinion, is there a need for continuous data collection and analysis?

Now that we have discussed the components in detail, it is important to remember certain aspects when selecting the stack at key stages of the process.

  1. Repository of information

    The selection of a data warehouse will depend heavily on whether you decide to opt for an on-premise or cloud-based solution. Cloud-based SaaS solutions are immensely advantageous, as they require no regular maintenance and can help to focus on the crux of the analytics challenge without being sidetracked.

    The most widely deployed on-premises solution consists of an execution engine such as Apache Spark or Apache TEZ in combination with a querying layer like Apache Hive or Presto. The most notable benefit of this architecture is that it allows organisations to maintain full control of their data. Additionally, Apache Spark provides the capability to develop custom analytics and machine learning applications. Furthermore, some querying engines, such as Presto, already incorporate basic machine learning functionalities.

    Cloud-based services such as Redshift, Azure Data Warehouse and BigQuery may be preferable to on-premises systems, particularly if your organisation lacks the necessary coding expertise to manage the latter. These cloud-based packages come with built-in machine learning (ML) modules which can be easily accessed and utilised.

    For a number of years, Google BigQuery Machine Learning (ML) has been available for use, however Amazon Web Services (AWS)’s Redshift ML has just recently been introduced. As such, for those looking to construct Machine Learning models straight from their cloud data warehouse, Google BigQuery and Microsoft Azure ML may be more reliable alternatives than AWS.
  2. ETL (Extract, Transform, Load) (Extract, Transform, Load)

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

    In order to guarantee dependable feature development, it is necessary to create scheduling and structures. Pentaho Data Integration is an open-source option that may be employed, yet it is not as adjustable as a customised solution.

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

    Exploratory data analysis (EDA) relies heavily on business intelligence and visualisation tools, making them an essential part of a data scientist’s technology stack. Popular on-premise 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 Azure Data Explorer are all excellent Software-as-a-Service (SaaS) solutions for data visualisation and analysis. AWS QuickSight goes further with rudimentary machine learning capabilities that can be used to generate insights, such as detecting outliers and making predictions. Additionally, it is possible to build self-driving dashboards with AWS QuickSight. If you are already utilising Amazon’s cloud stack, but have not fully integrated data from other sources, then it is advantageous to leverage their services.
  4. Methodology architectures for data analytics and machine learning

    For the past few years, Python has been the undisputed leader in the development of custom-built machine learning and analytics applications. Among the most popular libraries for statistical analysis and modelling are Scikit-learn and Statsmodels. Moreover, R is widely used in the manufacturing sector due to its comprehensive set of statistical modelling capabilities. In addition, other deep learning frameworks such as TensorFlow, MXNet and Pytorch can also be employed.

    If you have chosen Java as your language of choice for development, Deeplearning4j is a great option to consider. Community support is an important factor to take into account, as most developers may require in-depth research before completing the model pipeline. If your organisation does not employ Machine Learning specialists or wish to design custom models, many cloud service providers offer Machine Learning models and automated model building as a service. With tools such as Azure Machine Learning, Google Cloud AI and AWS Machine Learning Services, you can create models and intelligence without the need to write any code.
  5. Deployment Stack

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

    It is well-known that optimising inference modules for speed can be a challenging task and often necessitates the use of more sophisticated approaches such as batching and threading. However, rather than devoting significant resources to creating one’s own solutions, it is more efficient to utilise the deployment tools that are already available in popular machine learning frameworks such as TensorFlow, MXNet, Pytorch, and others.

    If you are looking for a way to simplify the complex deployment process, you may want to consider using the machine learning (ML) serving options available through cloud providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. It is also possible to deploy custom models developed outside of these cloud providers. One of the major advantages of using such services is the ease with which they can be scaled to meet your needs.

In conclusion, there are a number of factors to consider when building a data science stack with a limited budget. The size of the company and the direction it decides to pursue are both key determinants in this process. It is essential to analyse the organisation’s needs and resources in order to make the most cost-effective decisions and ensure the best results.

Join the Top 1% of Remote Developers and Designers

Works connects the top 1% of remote developers and designers with the leading brands and startups around the world. We focus on sophisticated, challenging tier-one projects which require highly skilled talent and problem solvers.
seasoned project manager reviewing remote software engineer's progress on software development project, hired from Works blog.join_marketplace.your_wayexperienced remote UI / UX designer working remotely at home while working on UI / UX & product design projects on Works blog.join_marketplace.freelance_jobs