Data Science training

In this module, you are going to learn about the various Data Science concepts, as follows:

• Introduction to Data Science, Importance of data science, applications, lifecycle, and components.
• Big data Hadoop, machine learning and deep learning
• Introduction to R programming and R studio.

• Introduction to data exploration
• Importing and exporting data to and from external sources
• Data exploratory analysis
• DataFrames, factors, loops, operators, conditional and looping statements, user-defined functions and data types, etc.

• Introduction to data manipulation
• Introduction to dplyr package
• Briefing about functions and combining different features with the pipe operator
• Implementing SQL operations with sqldf.

• Introduction to data visualization
• Explaining different graphs functions and it's implementations
• Multivariate analysis with geom_boxplot
• Univariate analysis with a barplot
• Creation of barplots
• Visualization with Plotly
• Working with themes and coordinates to present graphs more visually and clearly.
• Geographic visualization with ggmap() and building applications with shinyR.

• Need for statistics
• Categories of statistics
• Correlation and covariance, standardization, normalization, normal distribution, chi-square testing, ANOVA, and binary distribution.

• Introduction to machine learning, linear regression, predictive modeling, formulas, assumptions, and building a simple linear model
• Introduction to logistic regression
• Comparison of different types of regressions.
• Confusion matrix the accuracy of a model, threshold evaluation with ROCR, and understanding qqnorm() and qqline()
• Building linear models with multiple independent variables.

• Introduction to logistic regression
• Concepts of logistic regression
• Building a simple binomial model
• Finding out the right threshold using the ROC plot
• Real-time applications of logistic regression.

• Classification and it's techniques
• Introduction to decision trees, algorithm and building a decision tree in R
• Confusion matrix, regression trees vs. classification trees
• Introduction to bagging
• Random Forest and its implementation in R
• Naive Bayes and it's computing possibilities
• Concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
• Overfitting, pruning basics, finding out the correct number of trees, and evaluating performance metrics.

• Clustering, types and use cases
• Introduction to unsupervised learning
• Feature extraction, clustering algorithm, and k-means clustering algorithm
• Briefing about k-means and its implementation
• Explaining the Principal Component Analysis (PCA) in detail and implementing PCA in R

• Introduction to association rule mining, advantages, types, measuring the association
• rule mining and implementation.
• Introduction to recommendation engines
• How are recommendation engines implemented in R?
• Recommendation engines use cases.
• Summary

• AI and deep learning
• Fundamental of Artificial Neural Networks
• Tensor Flow
• Computational frameworks for building AI models
• Fundamental of tensorflow and working it with R

• What is a time series?
• Techniques, applications, and components of the time series
• ARIMA model
• Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis

• What is the Bayes theorem?, Naïve Bayes Classifier?
• How Naive Bayes classifier works and classifier building in Scikit-Learn.
• classification model using Naïve Bayes and the zero probability problem

• Introduction to text mining, use cases, and understanding and manipulating the text with 'tm' and 'stringR'.
• Text mining algorithms and the quantification of the text
• TF-IDF and after TF-IDF

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