Professional Course in Data Science
More and more businesses today are using Data Science to add value to every aspect of their operations. This has led to a substantial increase in the demand for Data Scientists who are skilled in technology, mathematics and business. However, the supply has not kept pace with the demand, creating many highly paid job opportunities for Data Scientists. This extensive 6 months' training in Data Science gives you broad exposure to key concepts and tools from Python, R to Machine Learning and much more. After 1400+ hours of training, you will be ready to face any Data Science challenge.
Being a process-oriented organization that provides data science training, the trainees will be evaluated for certification on the basis of performance in following criteria: -
- Academic performance
- Assignment Scores
- Attitude, Punctuality and Dedication
- Live project performance reports by client and academic group.
Module 1: Programming Basics
It is acknowledged by industry experts that anybody who is comfortable and understands the basics of programming such as loops, functions, if-else, and programming logic can become a successful data scientist. Being a good programmer is a highly preferred skill for a data scientist and that's where this module will help you.
This section will cover all the basics you require to achieve that.
Module 2: Mathematics Basics
Mathematics is the backbone for Data Science domain. May it be an implementation of a simple uni-variate Linear Regression model or application of statistical concepts for exploring data, understanding the underlying mathematical concept is pertinent for a successful Data Science career. This is way a Data scientist must have a strong mathematical foundation.
This section will cover the mathematical concepts required for this course.
This section will clear your understanding on Statistics and Probability
This section will cover the basics of Linear Algebra and Calculus, required to complete this course.
Module 3: Machine Learning Basics
With the help of Machine Learning, a Data Scientist is able to analyze huge amounts of data in real time. It helps in understanding the underlying trend or relationship present in the data and because of this reason, Machine Learning has become an integral part of Data Science.
This section will delve into the basics of Machine Learning and types of Machine Learning.
This section will covers basics of Machine Learning and its various types.
Module 4: Supervised Machine Learning
Supervised Machine Learning is used when we have to map the relationship that transforms the input into the output. It is used only in such scenarios where we have ample amount of data such that we know what the output is based on a given set of input values. The goal is to approximate the mapping function so well that when we have new input data x we can predict the output variable Y for that data. It is called supervised learning because the process of algorithm learning from the data can be thought of as a teacher supervising the learning process.
This will cover the mathematical concepts and implementation details for a Uni-variate Linear Regression model
After completing this section, you will be comfortable in implementing this Machine Learning algorithm in real world situations.
In simple terms, a Random Forest algorithm can be seen as a collection of multiple decision trees merged together to obtain a more stable and accurate prediction. There is a direct relationship between the number of trees in the forest and the results it can get – the more trees in the forest, the more robust would be the prediction and thus higher accuracy.
This section will cover all the information you need to understand and implement a Random Forest algorithm.
This section will help you to implement and understand SVMs.
Module 5: Unsupervised Machine Learning
In the real world, many a times, a Data Scientist is faced with a situation where only the predictor or input variable is known with no corresponding data for output variable. In such scenarios, Unsupervised Machine Learning comes to our aid. The goal for unsupervised learning is to understand or learn the underlying relationship or distribution in the data in order to learn more about it. These are called unsupervised learning because unlike supervised learning there are no correct answers and there is no teacher.
This section will help you in implementing unsupervised machine learning algorithms.
Module 6: Deep Learning
Deep Learning is a subfield of machine learning which comprises of algorithms that try to mimic the structure and function of the human brain. Just like our brain learns from experience, a deep learning algorithm would perform a task repeatedly in order to improve the outcome by learning or improving from experience. The word 'deep learning' refers to neural networks having various deep layers that enables complex learning behavior. With an increase in generation of huge data on a daily basis, relevance of Deep-learning algorithms has soared recently. Availability of strong computing power have also contributed in increased usage of such algorithms.
Under this section you will cover the necessary concepts of Deep Learning.
Similarly, a Neural Network (NN) loosely mimics the way our brain solves a problem. Like a human brain, they learn to recognize patterns by training itself on a labeled dataset. However, huge number of datasets is required in order to train a NN.
This section will cover the required concepts for implementation of Neural Networks (NN).
Module 7: Machine Learning Project Implementation
This will be of 3 months duration with hands-on training and development of live Machine Learning projects as per the requirement of our client. This will be unique learning experience where you can learn about implementation and understand how professionals work in a development scenario. This training will help you to be prepared to crack interviews in field of Data Science and help you get your dream job.