Data science is growing. The pace at which it is growing has left many analysts surprised! Large organizations are spending billions of dollars on data science and trying to hire the best AI talent that is certified by the leading data scientist online courses in India. Even during the pandemic lockdowns, the data science domain grew 300 percent more than any other technology. Not only does this trend support the growth of technology for meeting current goals but also helps companies sustain their business goals through sustainable planning and resources management.
According to a recent survey of IT domains, more than 90 percent of the business leaders from AI and machine learning fields have mentioned that their data scientists are focusing entirely on digital transformation and security. These are related to the application of Artificial Intelligence and machine learning.
In the next 3 years, AI’s importance as a dominant technology within data science can be fathomed by the volume of investments IT companies are ready to pump. By 2025, AI’s data science share would cross 4 trillion USD, nearly 24 percent of the total share in the IT industry in terms of revenue generation and investments.
In this article, we have highlighted some of the best known tools that are extensively used by data scientists for their projects.
Data scientists are working with specialized tools to unify and scale their disparate data sources. The rise of enterprise tools like Power BI makes it so easy for data scientists to gain a very deep insight into what Microsoft’s other applications offer, such as Excel, Word, or Teams. With superior connections and internal integrations, you can link to any of the current Microsoft technologies and derive smarter intelligence from the tools to expand on the scope of your existing BI applications.
Power BI was one of the first proprietary data science tools that Microsoft released in 2011 to secure its leadership position in the Cloud computing and SaaSindustry. In the last 12 years or so, Power BI has emerged as the genuine resource for learning and training with big data techniques. If you are learning in top data science classes, it is worth going through the web pages of Power BI, as it provides ample information about the current trends in data warehousing, data management, data discovery, IT and security, and custom visualization techniques for business intelligence groups.
Not only is it useful for building big data business intelligence solutions but also for mobile applications that specifically cater to the needs of modern day software developers who are looking to create something out of the traditional Excel and XML spreadsheets and workbooks.
Julia’s data science applications
Learning a new language is always great. Julia is fun to start with if you are learning data science techniques. Open source programmers have flooded the data science world with their wide scale deployments. In the world of Python and R, Julia is an outlier. Julia developers rely on its core computing standards to manage big data, data mining, and database management—all this happening within a highly secured system where Julia offers the best results.
Known for its computing prowess, Julia is a very useful data science tool that addresses the challenges in the machine learning developments. It combines the performance of computing with ease of conversion from other prominent AI ML languages in real time.
Popular Julia based tools include:
- Spark, and much more
If you are only new to machine learning or data scientist online courses, we would highly recommend that you take up the basic computing route with Julia.
Excited to be part of the data science? So, have you heard about the futuristic applications that machine learning and deep learning (ML DL) streams are bringing to the world? Well, if you are in a top course for data science training, it is possible that you are in the middle of simulations and testing with Deep Learning techniques that specifically use different types of programming languages.
Now, one programming language can’t solve the challenges of deep learning on its own. It requires a ton of ideation, processing, and experimentation to succeed. A tool like Keras makes it easy to do all these, with the advantage of working in an already established data science ecosystem like TensorFlow and Python. Common deployments with Keras happen mostly in the fields of Computer vision, NLP developments, Generative DL, Audio data management, synthetic data and structure data classification, Graph data, and so on.
If you love DL and want to have a great data scientist career, training with Keras and Python libraries would help you scale newer techniques that are yet to be documented and listed in the AI world.
How much impact can a programming language with a single syllable make on the data science domain?
According to the recent trends, the effect is a hell lot! R programming language is a very powerful and reliable tool for data science projects that are used for graphs and knowledge management in AI ML. The best part about R is that, like Python, it can be learned very quickly and training in AI ML space is possible within 8 to 10 weeks of dedicated coding. It also allows quick integration with proprietary platforms like SAS, Power BI, Tableau, and others.
Data science portfolios are exploding and this is the right time to understand the key components that make the data science industry so powerful, AI is just a part of it. It is aptly supported by Data management, big data intelligence, business intelligence, robotics, and data visualization. There is so much still to be done in the field of data science for organizations. So, it is worth knowing the tools that data scientists use to future proof their projects and plans.
If you want to make a critical impact on data science, master these tools.