Data science isn’t just the way of the future, it’s the way of right now! It is being adopted in all sorts of industries, from health care to route planning, marketing & sales to banking industries and beyond. Even industries such as retail that you might not associate with big data are getting on board. Data science is the fuel of the 21st Century.

We provide you with live recorded classes of the same session to follow up if you end up missing the same.

Each of these technologies complements one another yet can be used as a separate entity. Big Data refers to any large and complex acquisition of data. Extracting meaningful information from data is why Data Analytics is used for. While Data Science is a multidisciplinary field that aims to produce broader insights.

Accelerated data science career guidance with world-class training on the most in-demand data science and machine learning skills. Training and hands-on experience with key tools and technologies including Python, PowerBi, and concepts of Machine Learning. Upon completing the program, you will be receiving an international certificate from the extension of the prestigious Acacia University (AUX).

Aspirants and professionals who are having basic computer programming skills can enroll for the program.

Basic knowledge of programming logic and technology exposure will be helpful.

Google Duplex can make phone calls to make restaurant and hair appointments. Google Deep Mind won a global Starcraft game challenge against gaming pros. Amazon uses AI for book and product recommendations. Websites are using chatbots to answer basic customer queries. Airports are using image recognition for staff security. Rolls Royce is using AI for predictive maintenance and servicing of airplane engines. Informatica is using AI for compliance and data gathering and analysis purposes. Fintech is using AI to combine and analyze more diverse datasets. In healthcare, AI can help analyze more data for preventative medicine. Baidu in China is producing self-driving buses for large cities.

Automated transport, taking over dangerous jobs, robots working with humans, improved elderly care, cyborg (organic/bio-mechanic) organisms, environment monitoring and response to climate change goals.

TensorFlow is an end-to-end open-source platform for Machine Learning (ML). It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

Yes. Many data analysts go on to become data scientists after gaining experience, advancing their programming and mathematical skills, and earning an advanced degree.‎

Which you choose is largely a matter of preference. If you’re mathematically minded and enjoy the technical aspects of coding and modelling, a data science degree could be a good fit. On the other hand, if you love working with numbers, communicating your insights, and influencing business decisions, consider a degree in data analytics. Whether you study data science or data analytics, you’ll be building skills for an in-demand, high-paying career. ‎

The technical skills and concepts involved in machine learning and deep learning can certainly be challenging at first. But if you break it down using the learning pathways outlined above, and commit to learning a little bit every day, it’s possible. Plus, you don’t need to master deep learning or machine learning to begin using your skills in the real world.‎

Yes. The average base pay for a machine learning engineer in the US is $123,608, as of April 2022. According to a December 2020 study by Burning Glass, demand for AI and machine learning skills is projected to grow by 71 per cent over the next five years. The same study reports a $14,175 salary premium associated with these skills.