This session introduces you to the fundamental concepts of Data Science.

Topics   

• Data science life cycle 
• Significance of Data Science in this data-driven world
• Applications of Data Science 
• Introduction to big data and Hadoop 
• Introduction to machine learning, deep learning, R programming, and R Studio  

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Data Exploration section is one of the essential topics of Data Science training. Data exploration is an approach that is similar to initial data analysis where a data analyst uses it to understand what a data set is and know the characters that a dataset contains.  

Topics  

• Importance of data exploration in Data Science 
• Extraction and exporting of data from various external sources 
• How to conduct data exploration using R?
• Data exploration methods 
• Working with data frames
• Operator in-built functions 
• Looping statements and user-defined functions
• Matrix, list, user-defined functions, and arrays. 

Data manipulation is one of the important concepts of Data Science. It helps in organizing data into an easily understandable format.    

Topics  

• Introduction to data manipulation
• Need for data manipulation 
• Discussing various functions such as mutate() function, sample_frac() & count() functions, Sampling & Counting with sample()

Data visualization is the internal and crucial part of Data Science. This section helps you to understand how to extract the hidden trends out of data and represent them in the form of charts and graphs.

Topics               

• Introduction to visualization
• Explanation of different charts and graphs 
• Introduction to graphics 
• Building frequency polygons with geom_freqpoly
• Numerical distribution with geom_hist() function,
• Visualization with Plotly package & building web applications with shinyR,
• Univariate Analysis with Bar-plot, histogram and Density Plot, and multivariate distribution,
• Bar-plots for categorical variables 
• Visualization with Plotly package & building web applications with shinyR,
• Continuous vs categorical with box-plots, 
• Intro to plotly & various plots, visualization with ggvis package, and themes to make the graphs more presentable,
• Visualization with ggvis package
• Building web applications with shinyR.
• Visualization with ggvis package,

Statistics is an integral part of data science and plays an important role in it. Multiple statistical methods available are regression, classification, time series and hypothesis testing; data scientists use all these methods to run suitable experiments and also to summarize the data fairly & quickly.  

Topics  

• Introduction to statistics and relation between Data Science and statistics.
• The terminology used in statistics & categories of statistics. 
• Central Tendency, Correlation & Covariance, Measures of Spread, standardization & normalization
• Probability & its types 
• Chi-Square testing, hypothesis testing, a binary distribution, normal distribution, and ANOVA  

As Data Science is a broader concept or multidisciplinary subject, machine learning also falls under Data Science segment. In this section, you will be introduced to various machine learning concepts.  

Topics

• Introduction to machine learning, linear regression, and predictive modelling 
• Modelling with simple linear, multiple Linear regression and Linear regression 
• Finding P-value, making the comparison between Linear regression and logistic regression, 
• Evaluation with ROCR, detailed formulas, and understanding the fit of a model 
• Predicting results, understanding the summary results with the null hypothesis. 

Building Linear models with multiple independent variables.             

This section deals with complete regression concepts, their importance, and how they work in real-time. It contains complete details regarding regression concepts and usage. 

Topics 

• Introduction to logistic regression, its concepts, and Linear vs Logistic regression  
• Math behind linear regression concept, detailed formulas, and logit and odds
• Building simple “binomial” model, confusion matrix, accuracy, true positive rate, false-positive rate, etc
• Introduction to confusion for evaluating the built model and finding the right threshold by building the ROC plot.
• Concepts like cross-validation and building logistic models based on real-life applications of Regression. 

This section is meant to deal with fundamental concepts of Decision Tree & Random Forest which include classification techniques algorithms, creating decision trees and classification trees, and all other core concepts. 

Topics     

• Introduction to classification and other classification techniques 
• Introduction to Decision tree & algorithms for decision induction, and building decision tree in R. 
• The process to create a perfect decision tree and confusion matrix. 
• Introduction to the ensemble of trees, Random Forest, bagging, implementing Random Forest in R. 
• Computing probabilities, light split node, Information gain, Gini index for a right split of the node.  
• Cost complexity pruning, Pre-pruning, and post-pruning 
• Finding the perfect number of trees and evaluate performance metrics. 

Unsupervised learning occupies an important position in Data Science. It is a type of self-organized Hebbian learning which helps in finding previously unknown patterns in data without the need for having a pre-existing label and allows modelling in probabilities densities of given inputs. 

Topics 

• Introduction to Unsupervised learning, clustering & its use cases
• Concepts like Canopy clustering, Hierarchical clustering, and Theoretical aspect of K-means  
• Introduction to Unsupervised learning, Clustering algorithms, K-means in the process flow, K-means ion process Flow, and K-means in R 
• Finding the right number of clusters with the help of Scree-plot Dendrogram & clustering 
• Understand Hierarchical clustering and implementation of R within hierarchical clustering,
• Introduction to Component analysis, PCA in R, and procedure to Implement PCA  

Association rule learning is a method used in machine learning for discovering interesting relations between variables in a large data set. And, the other topic is a recommendation engine, which is a software algorithm used to analyze the available information and to recommend relevant information to the user. 

Topics  

• Introduction to rule mining, the measure of association rule mining, Apriori algorithm and its implementation process in R
• Introduction to a recommendation engine, collaborative user-based filtering, & Item-based collaborative filtering.
• Implementing recommendations in R, recommendation use-cases: user-based and item-based. 

This section deals with the most important topic, artificial intelligence, and how it is associated or related to Data Science. 

Topics  

• Introduction to Artificial intelligence & Deep learning 
• Introduction to TensorFlow computational process & Artificial Neural Network 
• A process on how to build Artificial Neural Networks using TensorFlow, and working with TensorFlow in R.