Data Science Training

In this module, you will learn the basics of data science and R programming, Importance of Data Science.

Topics covered in this section are: 

• What is Data Science
• Significance of Data Science in today’s world.
• R Programming basics

Learning Outcomes: By the end of this module, you will get a fundamental idea about data science and R programming.
 

This python fundamentals module discusses the python concepts required for a data scientist. 

Topics covered in this section are: 

• Python Introduction
• Indentations in Python
• Python data types and operators
• Python Functions

Learning Outcomes: By the end of this module, you will get the basic Python programming knowledge.
 

This module deals with the basic concepts of data structures and data visualization.

Topics covered in this section are: 

• Data Structures Overview
• Identifying the Data Structures
• Allocating values to the Data Structures
• Data Manipulation Significance
• Dplyr Package and performing different data manipulation operations.

Learning Outcomes: Upon completing this module, you will be able to understand the significance of Data structures and Data manipulation in Data science.
 

This module discusses topics like Data visualization, types of graphs, Ggplot2 package, bar plots creation, Univariant, and Multivariant analysis.

Topics covered in this section are: 

• Introduction to Data Visualisation
• Various kinds of graphs, Graphics grammar
• Ggplot2 package
• Multivariant analysis by using geom_boxplot
• Univariant analysis by using the histogram, barplot, multivariate distribution, and density plot.
• Creating the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Learning Outcomes: At the end of this module, you will be able to visualize the data through different graphs, Ggplot2 package. Also, you will get a real-time experience of bar plot creation, Univariant, and Multivariant analysis. 
 

This Data Science online classroom training module deals with statistics concepts like Classification, Probability Types, Covariance, and Correlation. Along with this, you will learn how to analyze the given data set through Data Sampling, Hypothesis Test, and Binary Distribution.

Topics covered in this section are: 

• Statistics Importance
• Statistics classification, Statistical terminology.
• Data types, Probability types, measures of speed, and central tendency.
• Covariance and Correlation, Binary and Normal distribution
•  Data Sampling, Confidence, and Significance levels.
• Hypothesis Test and Parametric testing

Learning Outcomes: By the end of this module, you will gain practical knowledge of different statistical concepts like Probability types, Hypothesis test, Covariance. You will also be able to work with other statistics techniques like Correlation, Data sampling, Normal and Binary Distribution.
 

This Machine learning module discusses machine learning basics like Supervise learning, classification, linear regression, and ensemble learning techniques.

Topics covered in this section are: 

• Machine Learning Fundamentals
• Supervised Learning, Classification in Supervised Learning
• Linear Regression and mathematical concepts related to linear regression
• Classification Algorithms, Ensemble Learning techniques

Learning Outcomes: Upon completing this module, you will get a basic knowledge of machine learning, and you will be proficient in Supervised learning, Linear regression, and Ensemble learning. 
 

In this module, you will learn concepts like logistic regression basics, Bivariate and Multivariate Logistic regression, Poisson Regression. Also, it discusses developing logistic models and logistic regression applications.

Topics covered in this section are: 

• Logistic Regression Introduction
• Logistic vs Linear Regression, Poisson Regression
• Bivariate Logistic Regression, math related to logistic regression
• Multivariate Logistic Regression, Building Logistic Models
• False and true positive rate, Real-time applications of Logistic Regression

Learning Outcomes: At the end of this module, you will get practical knowledge of Logistic regression, Linear Regression, Poisson Regression, and Logistic models.
 

This module discusses topics like classification techniques, implementing random forest, Naive Bayes, Entropy, Information Gain, and Gini Index.

Topics covered in this section are: 

• Classification Techniques. Decision Tree Induction Algorithm
• Implementation of Random Forest in R
• Differences between classification tree and regression tree
• Naive Bayes, SVM
• Entropy, Gini Index, Information Gain

Learning Outcomes: Upon completing this module, you will acquire an in-depth understanding of decision tree induction algorithms, implementing the random forest in the R programming. 
 

This module provides a detailed overview of different clustering types, K-means clustering algorithm, K-means clustering concepts, and implementing historical clustering and PCA in R programming.

Topics covered in this section are: 

• Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
• Unsupervised learning, Clustering algorithm, K-means clustering algorithm
• K-means theoretical concepts, k-means process flow, and K-means implementation.
• Implementing Historical Clustering in R
• PCA(Principal Component Analysis) Implementation in R

Learning Outcomes: Upon completing this module, you will get a real-time experience of k-means clustering, clustering algorithm, and Principal Component Analysis.
 

This data science online training module will help you master natural language processing,  text mining, and NPL working with text mining.

Topics covered in this section are: 

• Natural language processing and Text mining basics
• Significance and use-cases of text mining
• NPL working with text mining, Language Toolkit(NLTK)
• Text Mining: pre-processing, text-classification and cleaning

Learning Outcomes: At the end of this module, you will get a working knowledge of Natural Language Processing and Text Mining.