Data Science Course in Jaipur

Learn real-time Big Data Analytics applications, understand the need for analytics through a use case, and get a high-level understanding of the best project management methodology for Data Mining – CRISP-DM.

This module delves deeper into the CRISP-DM project management methodology for data science projects. Learn about data collection, cleansing, preparation, munging, and wrapping, among other things. With us, know about exploratory data analysis, which is the first step in churning the data.

Learn about the different statistical calculations used to capture business moments to make data-driven decisions and how to intercept information using visuals to represent data. Univariate, Bivariate and Multivariate analyses are also covered.

Our professionals will let you know about the different graphical representations in this module. You’ll learn to use the central limit theorem to deal with variations that arise when analyzing different samples for the same population.

We will provide you with in-depth detailed properties of a continuous random variable and its distribution under normal conditions. Here you will learn how to use a normal Q-Q plot to see if a continuous random variable follows a normal distribution

We will guide you on how to frame business statements by making assumptions. You will get acquainted with conducting a Hypothesis test, and interpretation will be introduced in this module.

Learn about the Linear Regression components with the regression line equation. Our experts will introduce you to Linear Regression analysis with a use case for predicting a continuous dependent variable and let you understand the ordinary least squares technique.

In the continuation of the Regression analysis, you will learn how to deal with multiple independent variables affecting the dependent variable, conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions.

In this guide, we will provide every detail about the balance between overfitting, underfitting, and regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions.

With us, get acquainted with the principles of the logistic regression model and how to interpret the likely outcome of the logistic regression model using cutoff values. Also, Understand the confusion matrix and its parameters to evaluate the prediction’s outcome.

We will give you a thorough understanding of multi logit equations, baseline, and making classifications using probability outcomes. Also, provide you with detail on handling multiple categories in output variables, including nominal and ordinal data.

As part of this module, you learn other different regression techniques used for predicting discrete data. These regression techniques include analyzing the numeric data known as count data.

Get acquainted with clustering algorithms in this unsupervised learning course. We will teach you how to create homogeneous data groups using various approaches to data segregation and data segregation using various mathematical approaches.

In this course, you will learn to apply data reduction techniques without any variable deletion, the advantages of dimensional reduction techniques, and another factor analysis technique.

Here, you will learn to measure the relationship between entities and understand the metrics Support, used to define the rules with the help of the Apriori algorithm. Also, learn the pros and cons of each metric used in Association rules.

With us, you’ll learn the science of making personalized recommendations and various methods applied for collaborative filtering; their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

Our professionals will provide the vertex and edge about the statistics used to calculate the value of nodes in the network. You will also learn about the google page ranking algorithm as part of this module.

k Nearest Neighbor algorithm is a distance-based machine learning technique. This course will teach you how to classify the dependent variable using the proper k value. The k-NN classifier, often known as the lazy learner, is a popular and simple method.

In this decision tree & random forest course, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each attribute.

Here you will be educated on improving the reliability and accuracy of decision tree models using Bagging and Boosting ensemble techniques. We will also discuss the parallel and sequential methodologies used in the Bagging and Boosting procedures.

The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed in this module. You will also learn about stacking approaches and algorithms, which provide remarkable accuracy and have helped many aspiring data scientists win first place.

As part of the Text Mining and NLP module, you will learn about advanced text mining ideas, topic models using the LDA algorithm, and emotion mining utilising lexicons that assist in interpreting the context of raw text data.

This course will inform you about joint probability and applications. Learn how to tell if an incoming email is a spam or not. Discover Bayesian probability and how it may be used to solve complicated business problems.

The Perceptron algorithm is defined based on a biological brain model. You will be guided by the perceptron method’s parameters, which serve as the foundation for creating far more complicated neural network models for AI applications.

With our experts, you can learn the principles of training and weights calculations using various parameters and tweaking them. Also, grasp the activation and integration functions utilised in developing a neural network.

You will learn the differences between Networks like Neural Networks are Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) and their applications in real-time.

Learn about another Deep Learning algorithm, SVM, a black box technique and how to use kernel tricks to convert data into high-dimensional spaces so that non-linear spaces can be classified as linearly separable data as a part of this course.

Our experts will acquaint you with how survival analysis techniques can be used to understand the survival plot and real-time applications analysis in customer churn, medical sciences, and other sectors.

This forecasting module will teach you how to use model-based forecasting techniques and time series analysis to identify series components, level, trend, seasonality, and noise in a time series data set.

Learn about data-driven forecasting techniques, such as ARMA and ARIMA models, which combine model-based and data-driven techniques and get introduced to make data more stationary by de-trending and depersonalizing it.