Professional Course in Data Science

Program Description

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.

A data scientist spends majority of their time either cleaning raw data in order to make it usable or implementing appropriate ML algorithms to extract underlying relationships that will help in business decision making. All this is carried out using Python or R. Being a vital skill for a Data Scientist, knowledge of Python goes a long way in reducing the learning curve for a Data Science enthusiast. This section will cover basic concepts in Python and will provide you an opportunity to polish your programming skills.
Data Manipulation and Visualization are important components of being a Data Scientist. As a data scientist, you might have access to huge amount of undesired and raw data which first needs to be cleaned and scrubbed (Data Manipulation). In most scenarios, insights gained from exploring of raw data are in an undesirable form. It is one of the roles of a Data Scientist to present it in an understandable and presentable format (Data Visualization) for a stakeholder in order to be used in decision making. While using Python, one has access to libraries for performing data manipulation and visualization.
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.

Probability and Statistics form the basis of Data Science. Estimates and predictions form an important part of Data science and probability theory is very much helpful for making predictions. Similarly, Statistics is also an integral part of Exploratory Data Analysis.
This section will clear your understanding on Statistics and Probability
Data is normally conceptualized as points, sometimes in a high-dimensional vector-space. Thus, knowledge of linear algebra helps a Data Scientist in comprehending it. Coupled up with calculus, a Data Scientist can understand the logic behind any Machine Learning algorithm. This helps in deciding which ML algorithm to choose and at times, such decisions effect success of a project.
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.

When working with huge amounts of data, a Data Scientist uses Machine Learning to implement statistical models so as to make a predictive analysis based on underlying relationships present in data. However, there are different types of Machine Learning and their knowledge will provide you with the required understanding needed to choose the right one for a job.
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 model is useful for finding relationship between one or more independent variables and a dependent variable. It is used only when data is continuous. We try to find a "Best-fit" line which actually represents the relationship between the dependent and independent variables involved. The sole aim is to find a linear relationship such that it's prediction error for all data points is as small as possible.
This will cover the mathematical concepts and implementation details for a Uni-variate Linear Regression model
The previous section covered "Uni-variate Linear Regression" model which is used in situations where there is just one predictor or independent variable. However, in most of the scenarios, we end up having multiple predictors. In such circumstances, we use "Multi-variate Linear Regression" model which will be covered in this section
With "Multi-variate" models, comes the problem of Over-fitting, Under-fitting, Normalization, etc. Also, we have to find an appropriate value for our "learning rate" and "initialization of parameters". Such issues and concepts will be dealt in this section
Chapter Description: Logistic Regression is a type of Supervised Machine Learning algorithm that apply statistical concepts for classification – binary (two classes) or multi-class (more than two classes) – problems. It is a predictive analysis algorithm where we use sigmoid function to predict an outcome based on its probability. After completing this section, you'll be able to implement Logistic Regression to a Binary class as well as Multi-class classification problem
Decision Tree is also a Supervised Machine Learning algorithm which uses predictive analysis. It looks like a flow chart where each internal node represents a condition or a test on an independent variable. Each label (there can either be two labels for a binary class classification or more than two for a multi-class classification problem) is represented by a leaf-node while branches represent combinations of independent variable that lead to those classes. The sole purpose of using a Decision Tree is to predict value of a dependent variable based on simple decision rules.
After completing this section, you will be comfortable in implementing this Machine Learning algorithm in real world situations.
Random Forest another Machine Learning algorithm which is used for classification problem. Just like a decision tree, this algorithm can be used as both classification and regression algorithm.
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.
Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithms. SVMs have their unique way of implementation as compared to other machine learning algorithms. A SVM algorithm tries to find a decision boundary such that it can segregate all the available different classes successfully. It does so by iteratively generating decision boundary and selects one which provides maximum margin with it.
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.

Unsupervised Machine Learning algorithms try to find underlying trend or pattern based on the values of independent variables without having any knowledge regarding the actual values of dependent variables under those circumstances. Though, unsupervised machine learning methods cannot be directly applied to a regression or a classification problem, they are useful in clustering, Anomaly detection, Association mining, etc.
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.

A human brain is an incredible pattern-recognition machine. It learns on itself through experiences by processing 'inputs' from the world. It does so by categorizing all the inputs into a "yes" or "no" situations and then generates an 'output' or decision.
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.

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