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MODULE 1: Introduction to Data Science
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
MODULE 2: Introduction to Python
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
MODULE 3: Python Basics
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
MODULE 4: Python Packages
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Data Science Course Content
Introduction to Data Science with R
- What is Data Science?
- Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
- Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
- Introduction to R programming and RStudio
Hands-on Exercise:
- Installation of RStudio
- Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases
Data Exploration
- Introduction to data exploration
- Importing and exporting data to/from external sources
- What are data exploratory analysis and data importing?
- DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types
Hands-on Exercise:
- Accessing individual elements of customer churn data
- Modifying and extracting results from the dataset using user-defined functions in R
Data Manipulation
- Need for data manipulation
- Introduction to the dplyr package
- Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
- Combining different functions with the pipe operator and implementing SQL-like operations with sqldf
Hands-on Exercise:
- Implementing dplyr
- Performing various operations for manipulating data and storing it
Data Visualization
- Introduction to visualization
- Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
- Multivariate analysis with geom_boxplot
- Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
- Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
- Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
- Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
- Geographic visualization with ggmap() and building web applications with shinyR
Hands-on Exercise:
- Creating data visualization to understand the customer churn ratio using ggplot2 charts
- Using plotly for importing and analyzing data
- Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot
Introduction to Statistics
- Why do we need statistics?
- Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
- Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
Hands-on Exercise:
- Building a statistical analysis model that uses quantification, representations, and experimental data
- Reviewing, analyzing, and drawing conclusions from the data
Machine Learning
- Introduction to Machine Learning
- Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
- Predicting results and finding the p-value and an introduction to logistic regression
- Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
- Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
- Understanding the summary results with null hypothesis, F-statistic, and building linear models with multiple independent variables
Hands-on Exercise:
- Modeling the relationship within data using linear predictor functions
- Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
Logistic Regression
- Introduction to logistic regression
- Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
- Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
- Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
- Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
- Real-life applications of logistic regression
Hands-on Exercise:
- Implementing predictive analytics by describing data
- Explaining the relationship between one dependent binary variable and one or more binary variables
- Using glm() to build a model, with ‘Churn’ as the dependent variable
Decision Trees and Random Forest
- What is classification? Different classification techniques
- Introduction to decision trees
- Algorithm for decision tree induction and building a decision tree in R
- Confusion matrix and regression trees vs classification trees
- Introduction to bagging
- Random forest and implementing it in R
- What is Naive Bayes? Computing probabilities
- Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
- Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
Hands-on Exercise:
- Implementing random forest for both regression and classification problems
- Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
- Using ROCR for performance metrics
Unsupervised Learning
- What is Clustering? Its use cases
- what is k-means clustering? What is canopy clustering?
- What is hierarchical clustering?
- Introduction to unsupervised learning
- Feature extraction, clustering algorithms, and the k-means clustering algorithm
- Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
- Dendograms, understanding hierarchical clustering, and implementing it in R
- Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
Hands-on Exercise:
- Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
- K-means clustering for visualizing and interpreting results for the customer churn data
Association Rule Mining and Recommendation Engines
- Introduction to association rule mining and MBA
- Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
- Introduction to recommendation engines
- User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
- Recommendation engine use cases
Hands-on Exercise:
- Deploying association analysis as a rule-based Machine Learning method
- Identifying strong rules discovered in databases with measures based on interesting discoveries
Self-paced Course Content
Introduction to Artificial Intelligence
- Introducing Artificial Intelligence and Deep Learning
- What is an artificial neural network? TensorFlow: The computational framework for building AI models
- Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
Time Series Analysis
- What is a time series? The techniques, applications, and components of time series
- Moving average, smoothing techniques, and exponential smoothing
- Univariate time series models and multivariate time series analysis
- ARIMA model
- Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
Hands-on Exercise:
- Analyzing time series data
- Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series
Support Vector Machine (SVM)
- Introduction to Support Vector Machine (SVM)
- Data classification using SVM
- SVM algorithms using separable and inseparable cases
- Linear SVM for identifying margin hyperplane
Naïve Bayes
- What is the Bayes theorem?
- What is Naïve Bayes Classifier?
- Classification Workflow
- How Naive Bayes classifier works and classifier building in Scikit-Learn
- Building a probabilistic classification model using Naïve Bayes and the zero probability problem
Text Mining
- Introduction to the concepts of text mining
- 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
Best Data Science Training in Ameerpet:
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Eligibility Data Science Training in Ameerpet :
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