Ever since the pandemic accelerated the digitalization of businesses, the volumes of digital data have grown exponentially. And even though Data Science is not a new concept, it has indeed become one of the fastest-growing aspects of the IT industry. When it comes to implementing AI and ML into a software development project, though, it can be challenging to find the right Data Science tools that help organizations reach their business goals. Adopting the right tools and data-driven models can streamline processes, speed up decision-making, analyze data more accurately and avoid model biases.
In this article, we’ll go into more detail not only about the Data Science tools you can trust but also about the trends we all need to keep an eye on to stay competitive and relevant.
choosing the right data science tools and languages
The most popular language used by Data Scientists today is hats down Python. Its ability to deal with functions, statistics, and mathematics while using a simple syntax is what makes it both powerful and approachable. Python’s versatility makes it suitable for a wide range of projects from Machine Learning (ML) to Natural Language Processing (NLP) and Sentiment analysis. Apart from its elegant and easy-to-understand syntax, Python is a great choice for most Data Science projects as it provides a wide range of libraries such as Pandas, Numpy, Matplotlib, Scikit-learn, and more, a huge online community, and of course, it’s free.
Python also allows appending modules in other languages such as C/C++ or embedding code into applications achieving a programmable interface. Being extremely powerful and intuitive, Python takes care of a lot of details on the down-low such as the specification of memory allocation or the control of object type. This way Data Analysts can focus predominantly on analyzing data rather than the nitty-gritty details.
Accedia has chosen Python for a variety of Data Science projects. One of them includes predictive modeling, delivering insights on subscribers’ responsiveness to promotional campaigns with the goal to optimize marketing spending and improve customer interaction. This helps the client company get deeper insights up to 14 times faster! Another interesting Python project we have developed allows marketing teams globally to focus on strategizing how to enhance long-term customer value. An ML algorithm creates models for direct marketing campaigns, based on past transactions, images, customer lists, and more to access and analyze the probability of subscription renewal and identify tailored offerings for key clients.
R is a Data Science programming language built specifically for statistics and statistical analysis. As many Data Scientists and Analysts choose to opt for R there is a huge community and support for almost all statistical issues that users might face. Most importantly, however, R allows scientists to create complex models, histograms, scatterplots, or line plots using just a few lines of code which makes operations quick and efficient. It’s no surprise that many of the biggest tech enterprises today use R for Data Science. Google, for example, relies on R for calculating the effectiveness of ads and making financial forecasts. Other examples include HP, IBM, Facebook, Microsoft, and many more.
R allows data visualization before any analysis has even begun. Here we are talking about some very impressive and informative graphs and charts, including maps or animated data visualizations. Another useful capability of R is the simplicity of prepping data for being analyzed. This process requires just one line of code and allows data to be loaded from all types of files such as .csv, .txt, or Stata files. It’s just that simple to create a new dataset without the risk of missing values. This gives Data Analysts ample time to focus their time and effort on actually analyzing the data, which speeds up time-to-market significantly. Of course, the benefits of using R for your Data Science project don’t end here. We can also mention its ability to easily reproduce research and analysis, to personalize data to meet specific needs, and much more.
Julia is a high-level dynamic programming language created for the purposes of data mining, distributed and parallel computing, ML, large-scale linear algebra, and more. And even though Julia is a young programming language, many specialists don’t shy away from naming it the future language of Data Science. Created just 10 years ago, today Julia has close to 35 million downloads. The biggest advantage for any project when it comes to using Julia is its speed. To this day, it is known as one of the fastest languages ever built, and thus it’s used to plan space missions and aviation collision avoidance systems.
One of the most popular Julia libraries is Flux. It’s a native ML library with GPU acceleration that mitigates the need for training deep learning models. Another good selling point when choosing Julia for your next Data Science project is its syntax which mimics the structure of math operations from the non-computing world. Thus, the learning curve of getting scientists familiar with Julia is not as steep as with other Data Science tools and languages. Additionally, Julia provides automatic memory management and has fast multiple dispatches giving it the ability to behave differently depending on the types of the arguments.
In short, Apache Spark is an analytics tool for ML and big data. It’s predominantly used for data processing, analytics report generation, and querying. Spark is used by enterprises such as eBay, Netflix, and Yahoo, along with other 80% of the Fortune 500 companies, whether to provide a customized experience to its users or real-time analytics for better user experience.
Apache Spark is one of the Data Science tools that is particularly appealing due to its speed and ability to manage petabytes of data simultaneously. It can handle any analytics obstacles, thanks to its low-latency in-memory data processing features. Spark allows users to easily build parallel applications due to its high number of operators and supports several programming languages such as Java, R, Scala, and Python. Some other very useful features include the ability for lazy evaluation and structured streaming (Spark Streaming).
Apache Spark also comes with native libraries for ML and graph processing that make developers more productive and efficient. It comes as no surprise that many companies are shifting to Apache Spark as it provides outstanding performance, speed, and accuracy in its real-time big data processing and trend forecasting.
Jupyter is a new interactive tool created for notebooks, code, and data that allows users to configure workflows in ML, Data Science, scientific computing, and more and combine in a single document software code, multimedia resources, texts, and so on. It supports around 40 programming languages, including some used for Data Science, such as Python, R, Scala, and Julia. Something else that differentiates Jupyter from other Data Science tools and languages is its ability to merge text with code snippets and visual outputs like graphs and charts into one single page.
Another feature that makes it very convenient is the ability to convert files into HTML, PDF, or other formats in case a device is not able to read a file. Jupyter also offers ease of working and file sharing, exploratory data analysis, data protection as no data is stored locally, and more. Still, note that if your project requires a large team to use Jupyter simultaneously, collaboration may be a challenge.
Apache Hadoop is an open-source tool used for processing huge data sets across clusters of computers. It can store and analyze the constantly growing digital information without the risk of errors while proving impressive scalability and reliability. Apache Hadoop is especially useful when the data needs to be distributed throughout different servers or when that same data is moved to the system memory itself. In those cases, it helps data to be transferred quickly and safely to different nodes. However, Hadoop does so much more than that. It also makes it possible for data exploration, storing, filtering, sampling, summarizing, and more. This way it allows Data Scientists to gather and store data without having to interpret it or filter data that is unhelpful and unnecessary for the need of the project. Hadoop offers a full picture of the entire available data so that the Data Scientist can properly analyze it, avoid any bias, and choose the best technique for data modeling. This again helps reduce the number of records and saves resources from the project.
Choosing the right Data Science tools for a project, often raises the question of which is better Apache Hadoop or Apache Spark. The answer here is that it very much depends on the individual case. For example, Hadoop would probably be the better choice if you are working with large amounts of data and require huge utilities for storage as it provides various frameworks for both storage and processing of that data.
data science trends to keep in mind
Even though cloud computing is nothing new as a concept, in recent years its popularity has raised exponentially, making it not just optional but necessary for even standing a chance against the overwhelming amounts of data. Cloud-based storage mitigates the need for extra support costs, reduces the risk of data loss, increases scalability and reliability, and much more. Additionally, storing data in the cloud makes processes much faster, increases operational efficiency, and speeds up time-to-market. Thanks to the growing number of organizations that are going digital, cloud-based services are gaining even bigger popularity. So are cloud-native Data Science and analytics solutions that offer better accuracy, speed, and lower latency.
natural language processing (NLP)
NLP is where Artificial Intelligence (AI) meets linguistics and computer science. It’s a constantly expanding market that is believed to reach $25.7 billion by 2027 and to have a notable influence in industries such as healthcare, retail and e-commerce, automotive, transportation, manufacturing, and so on. NLP is an exciting new approach to analyzing and studying data and providing trends and forecasts. A new NLP technique that is gaining more and more popularity is sentiment analysis, used to determine whether customers’ feedback on a product or service is positive or negative. This allows companies to understand what their target audience wants and needs.
Using Python and NLP Accedia has created a solution, identifying and analyzing available parking spots via an advanced, localized voice assistant. The NLP is trained to understand audio speech and connect to a Computer Vision (CV) model when given a command. It then returns a response with the exact number of available parking spaces. This all happens in a private network protecting any sensitive information.
Hyperautomation is a business-driven approach that includes the automation of as many IT and business processes as possible. It incorporates tools and technologies such as advanced analytics, ML, AI, Robotics process automation (RPA), Business process management (BPM), and more. It is a term set by Gartner and aims to reduce operational complexity throughout organizations and speed up data gathering and analyzing by eliminating the need for manual human involvement. Automating the entire process from cleaning and preparing the data to its analysis will continue to transform Data Science. Additionally, hyperautomation will help ML make machines learn much faster and become more agile when it comes to meeting changes. This process, also known as AutoML, helps in data visualization, as well as in model intelligibility and deployment.
We are used to taking data generated by AI or ML as an objective single point of truth. And while in the ideal case scenario, it should be exactly that, in practice, it not always is. And the reason for that is that while data should be trustworthy, it is collected and analyzed by people with cognitive biases they don’t even know about. This means that data often is distorted by our own belief systems, personal experiences, and perceptions. Thus, data models automatically inherit those biases and therefore generate unreliable results as the principles of “Garbage in, garbage out” (GI/GO) suggests – if you input flawed or wrong data, then that will reflect on the outputs, as well. Many famous cases prove that phenomena like when Amazon’s ML recruitment system showed bias against female candidates. Thus, more and more companies encourage Data Scientists to avoid bias by thoroughly inspecting the entire data before jumping to any conclusions or hypotheses, using randomization, establishing inclusivity frameworks, and actively looking for data supporting the opposite point of view. The human factor behind Data Science and analytics is not going anywhere. Thus, we need to figure out a way to eliminate any cognitive bias, as this is what the accuracy of our data depends on.
As we already discussed, the development of AI and ML solutions has seen a huge rise recently. However, implementing those solutions and their many use cases throughout entire organizations can be challenging or often even unrealistic. As it turns out, usually just a fraction of the developed models ends up deployed to production. So, to automate, streamline, and scale the deployment of ML models, many companies now use a framework known as ModelOps. It’s originally based on the concept of DevOps but modified and improved to answer the needs of ML models.
In short, ModelOps includes testing, model versioning, development environments, monitoring, CI/CD, model store, and more. ModelOps is flexible and easily adjustable to changes and different business problems. The framework helps to easily adopt new technologies, transfer data from the Data Science team to the Development team, and provide a single source of truth for workflows, costs, and more. All in all, ModelOps enables collaboration and communication between teams, provides insights into AI and ML models’ performance, and provides security and background information on all model versions.
Data privacy laws such as the GDPR (General Data Protection Regulation) in Europe and the CCPA (California Consumer Privacy Act) in the USA are proof of raising awareness around data protection. Data is the core of every single aspect of AI, ML, predictive analytics, NLP, and so on, and thus its governance should not be an afterthought. The trend is pushing more and more organizations towards compliance with data privacy and security regulations. In the next few years, we are bound to see wider adoption of GDPR, as well as the adoption of new national data privacy laws.
Another major trend will be the merger of data privacy and security that has already begun and the adoption of multistandard compliance tools for data privacy management. All in all, the more attention we pay to data privacy, the more it allows us to identify sensitive data sources, create data catalogs for data search, build traceability via data watermarking, and more.
Utilizing data and the insights it provides when properly aggregated and analyzed can play a crucial role in the survival of organizations. Data is what makes possible the creation of NLP, ML, AI, and more solutions, the automation of predictive models, and the creation of interactive visualizations. The Data Science tools and languages we have listed above can help you with statistics and functions, provide real-time analytics, streamline and scale the deployment of ML models, and much more.
Learn more about how Accedia can help you implement Data Science through AI and ML in your next software development project.
Note: This piece is written in collaboration with Iliyan Gochev – a Data Science specialist proficient in Predictive Analytics, Deep Learning, and Machine Learning. Iliyan is experienced in building intelligent solutions for SMEs and world-known organizations from industries like automotive, utilities, telecommunications, and more.