Artificial Intelligence is transforming every aspect of our daily lives. IDC forecasts that global AI spending will reach $97.9 billion by 2023. As per IDC research, AI adoption is low but at a tipping point. Data quality, quantity and access, algorithm explainability and selection, lack of data science skilled personnel and cost of AI solutions are the key factors holding back AI initiatives. Only one tenth of PoCs reach to production deployments and about half of the AI initiatives fail. Businesses’ report more than 50% of the time on an AI project is spent on data integration and management and solution deployment vs actual data science tasks. An end-to-end solution covering all aspects of an AI lifecycle is crucial to an organization’s road to AI adoption and faster realization of superior business outcomes.

A Data Scientist dons many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms

Data Analytics career prospects depend not only on how good are you with programming —equally important is the ability to influence companies to take action. As you work for an organization, you will improve your communication skills.

A Data Analyst interprets data and turns it into information which can offer ways to improve a business, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends – as such a Data Analyst job description should highlight the analytical nature of the role.

You may also have heard machine learning and AI used interchangeably. AI includes machine learning, but machine learning doesn’t fully define AI. Machine learning and AI both have strong engineering components. You find AI and machine learning used in a great many applications today.

The basic ideas and algorithms behind deep learning have been around for decades, but the massive use of deep learning in consumer and industrial applications has only occurred in the last few years. Two factors have especially driven the recent growth in AI applications, and especially deep learning solutions: increased computation power accelerated by cloud computing and growth in digital data.

In this particular course we will be learning about the roadmaps, job opportunities, career paths, skills needed, applications, how these words etc. for the field of Artificial Intelligence, Data Science, Machine Learning and Deep learning and at last you will even see how you can make visualizations using the libraries of python for doing the data analysis.