Are you interested in a career in data science? Discover the latest data science technology and what you need to start your new career.
Technologies refer to the tools, techniques, and methods that are developed and used to solve problems, improve efficiency and enhance human capabilities. These can be physical or digital, and may range from simple tools such as a hammer or a wheel, to advanced software and hardware systems such as artificial intelligence, blockchain, and virtual reality.
Data Science Technologies
Data science technologies refer to the tools and technologies used in the field of data science to collect, process, analyse, and visualise data. These technologies include programming languages such as python and
R, statistical software like SAS and SPSS, machine learning frameworks such as tensorflow and scikit learn, big data technologies like hadoop and spark, and data visualisation tools like tableau and powerBI. These technologies have revolutionised the way data is collected, analysed, utilised, allowing businesses and organisations to gain insights and make data driven decisions in the real time. The field of data science continues to evolve rapidly, with new technologies and techniques being developed all the time to improve data analysis and decision making capabilities.
top 8 Data Science Technologies
➔ Amazon Web Services (AWS)
Amazon web services is a cloud computing platform that provides a wide range of cloud based services and tools for businesses and individuals. AWS offers scalable and reliable cloud computing services that allows users to easily and cost effectively manage their IT infrastructure and applications.
➔ Text Mining
Is also known as text data mining, is the process of deriving useful and valuable information from unstructured textual data. It involves using statistical, machine learning, and natural language processing techniques to analyse, understand and extract insights from large volumes of textual data. The goal is to transform raw textual data into structured and organised information that can be easily analysed and understood.
A few use cases for text mining are:
● Data Extraction
● Topic Modeling
● Sentiment Analysis
➔ Internet of Things (IOT)
The internet of things refers to the connection of everyday physical objects to the internet, allowing them to send and receive data. These objects can include many things from household appliances and wearable devices to industrial machinery and transportation systems. There are also concerns about the security and privacy implications of having so many devices connected to the internet.
➔ Streaming Analytics
Streaming analytics refers to the process of analysing real time data streams as they are generated by various sources, such as sensors, Iot devices, social media feeds and other data sources. The goal of streaming analytics is to quickly and accurately insights and actionable information from these data streams, and use this information to drive business decisions or take immediate action.
➔ Big Data Analytics
Big data analytics refers to the process of extracting valuable insights from large, complex, and varied data sets, often referred to as “Big data”. Big data typically involves data sets that are too large or too complex for traditional data processing tools to handle. The main benefits of big data analytics include improved decision making, increased efficiency and productivity, enhanced customer experience, and competitive advantages.
➔ Decision Intelligence
Decision intelligence is an emerging field that combines human intelligence, artificial intelligence and other advanced technologies to help organisations make better decisions. It involves using data driven insights and analytics methods to optimise decision making processes. The main goal of decision intelligence is to provide decision makers with a holistic view of the decision making landscape and help them make informed decisions that are optimised for their specific goals and objectives.
➔ Natural language processing (NLP) libraries
NLP is a subfield of data science that deals with analysing and generating human language. Libraries like NLTK, spaCy, and Gensim are used for NLP tasks like sentiment analysis, text classification, and language translation.
➔ Cloud computing
Cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide scalable computing resources for data science projects.
Conclusion
Technology has drastically transformed our world in various ways, from communication to healthcare, transportation, and more. Emerging technologies such as the Internet of Things, streaming analytics, big data analytics, and decision intelligence have provided new ways for organisations to collect, process, and analyse data, enabling them to make better decisions, optimise operations, and gain a competitive advantage.