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Train with a Data Science curriculum that's comprehensive. Get certified with world-class, face-to-face training in Data Science. Get mentored by Ivy League-educated business practitioners led by the industry-renowned Dr Mahendra Mehta.

• Foundation of Mathematics & Statistics 48 hrs

  Data Science can be defined as a science by which scientists use advanced mathematical & statistical concepts to extract embedded information from a complex dataset. Data scientists employ a number of tools and techniques which has originated from Mathematics & Statistics. Thus, the good knowledge of Mathematics & Statistics is vital and very important for understanding data science. The course will start by laying the foundation of Mathematics & Statistics for understanding how these concepts are used to solve the problem in AI & ML.

• Basics of Programming 8 hrs

  A brief introduction to programming would be given during this module. We would teach you about the various programming styles and languages you have available to you. We also explain why we have chosen Python as the primary programming language for this course.

• Python Language 24 hrs

  Python is an open-source interpreted, high-level general-purpose popular programming language. Its design philosophy focuses on code readability. Python Language constructs and its object-oriented approach aim to help programmers write clear, logical code for various applications.

  In recent years, Python has become a very popular language for the use of ML & AI application.

  It is a very rich language that has very comprehensive libraries for various applications in ML & AL, Visualization, Neural Networks, Deep Learning, etc.

  Learning Python forms an important part of the course.

  Python provides a good learning ecosystem, relatively easy to learn, huge flexibility, platform-independent, easily readable, excellent visualization options, outstanding community support and growing popularity.

• SQL/NoSQL 36 hrs

  SQL is a domain- specific language used in programming and designed for managing data held in a Relational Database Management System (RDBMS). It is very popularly used in handling structured data where there is a relation between different entities/variables of the data. This is extensively used in AI & ML when one encounters numerical and textual data.

  NoSQL provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational business. NoSQL databases are increasingly used in big data and real-time applications. NoSQL systems are called Not Only SQL to emphasize that they may support more than SQL like queries. This has now become an integral part of the databases for ML & AI problems when non-structured data are encountered.

• Data Warehousing 20 hrs

  A data warehouse is described as a technique for collecting and managing data from varied source to provide meaningful business insights. It is a blend of technologies and components which aids the strategic use of data. A Data Warehouse is used extensively for Airlines, Banking, Healthcare, Public Sector, Retail Chain, Investment and Insurance sector, Telecom industry, etc. A typical and popular function of data warehousing is Extract, Transform and Load (ETL). It is combined into one tool to put data of one database and to place it into another database.

• ML- Regression 24 hrs

  Linear Regression is one of the most well-known and well-undetermined algorithms in statistics and machine learning. Under this topic, various other types of regression such as Logistic regression, Ridge regression, Lasso, Robust regression, etc. will be covered. These techniques are widely used to solve many practical problems. These are also the starting point for more complex algorithms.

• ML- Classification 24 hrs

  ML Classification algorithm forms an important part in data science. The subject matter covers a variety of algorithms such as random forest, support vector machine and several other popular learning techniques and their applications.

• ML- Clustering 24 hrs

  This is unsupervised learning and this also forms an important part of learning from data in data science. There are many practical applications where clustering is useful and gives a very useful insight into the complexity of the data.

• Business Applications using Data Science 1 and 2 32 hrs

  Subject experts and corporate leaders would be brought in to talk about the difficulties they face in the real world. Students would be able to interact with people from various industries to help broaden their horizons. 

  This will facilitate informed decision-making for students when selecting which field to go into.

• Internet of Things (IoT) 20 hrs

  IoT is the extension of internet connectivity into physical devices and everyday objects. Embedded with electronics, internet connectivity and other forms of hardware which includes many sensors, these devices can communicate and interact with others over the internet, and they can be remotely monitored and controlled. There are several good examples in real-world using IoT such as home automation, several remote monitoring applications, driverless vehicle control system, etc.

• Deep Learning 12 hrs

  Deep Learning (also known as Deep Structured Learning or Hierarchical Learning) is part of a narrower family of Machine Learning methods based on ANN. The learning can be supervised, semi-supervised or unsupervised. In the last few years, deep learning is finding several new applications in real life. Image recognition and its related applications form a big part of research in deep learning. This is a very exciting area of real-life application in many disciplines.

• Neural Networks 20 hrs

  Neural Network is a class of computing systems that are inspired by, but not necessarily identical to, the biological neural networks that constitute a human brain. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. Image recognition, voice recognition, pattern recognition, etc. are some important applications of ANN. With the expansion of data science, ANN systems have developed a lot and finding a variety of applications in solving many real-life problems.