Data Science Certification Course in Fremont

Syllabus of Data Science Course in Fremont

• What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, elements of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
• Hands-on Exercise - Installation of R Studio, implementing easy mathematical operations and logic victimization R operators, loops, if statements and switch cases.

• Introduction to data exploration, commerce and commercialism data to/from external sources, what is data exploratory analysis, data commerce, data frames, operating with data frames, accessing individual components, vectors and factors, operators, in-built functions, conditional, process statements and user-defined functions, matrix, list and array.
• Hands-on Exercise -Accessing individual components of client churn data, modifying and extracting the results from the dataset victimization user-defined functions in R.

• Need for Data Manipulation, Introduction to duly package, choosing one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_fn(), sample_frac() & count() functions, obtaining summarized results with the summarize() function, Combining totally different functions with the pipe operator, Implementing sql like operations with self.

• Hands-on Exercise -Implementing punctually to perform numerous operations for abstracting over however data is manipulated and stored.

• Introduction to visualization, differing types of graphs, Introduction to synchronic linguistics of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), creating a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, variable distribution, Bar-plots for categorical variables victimization geom_bar(), adding themes with the theme() layer, visualization with portly package & building web applications with shiny R, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with gives package, geographic visualization with ggmap(), building web applications with shiny R.
• Hands-on Exercise -Creating data visualization to know the client churn ratio using charts using ggplot2, Portly for importing and analyzing data into grids. You'll visualize tenure, monthly charges, total charges and alternative individual columns by victimization the scatter plot.

• Why do we want Statistics?, Categories of Statistics, Statistical applied Terminologies, Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance, Standardization & Normalization, Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
• Hands-on Exercise -– Building a statistical applied analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.

• Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, ideas and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy match of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression, residuals, qqnorm(), qqline(), understanding the fit of the model, building straight forwarding linear model, predicting outline results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
• Hands-on Exercise -Modeling the link at intervals within the data using linear predictor functions. Implementing Linear & provision Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple freelance variables.

• Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit operate function and odds, Bi-variate logistic Regression, Poisson Regression, building straightforward “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold analysis evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of providing Logistic Regression.
• Hands-on Exercise -Implementing prognosticative analytics by describing the data and explaining the relationship between one dependent binary variable and one or a lot of binary variables. You'll use glm() to create a model and use ‘Churn’ as the dependent variable. Module 8: Decision Trees & Random Forest

• What is classification and totally different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naïve Bayes, Computing Possibilities, Impurity Function – Entropy, understand the ideas of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, realize the right no of trees and evaluate performance metrics.
• Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ because of the dependent variable and build a Random Forest with the proper of trees, victimization ROCR for performance metrics.

• Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriority algorithm & implementing it in R, Introduction to Recommendation Engine, user-based cooperative filtering & Item-Based Cooperative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation 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 fascinating discoveries.
• 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.

• Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation 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.

• introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.

• What is Time Series, techniques and applications, parts of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series statistic in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
• Hands-on Exercise -Analyzing time series of statistic data, sequence of statistic measurements that follow a non-random order to identify the nature of development and to forecast the future run values in the series.

• Introduction to Support Vector Machine (SVM), Data classification treatment using SVM, SVM Algorithms using mistreatment Separable and Inseparable cases, Linear SVM for characteristics margin hyperplane.

• what is Bayes theorem, What's Naïve Bayes Classifier, Classification Workflow, However Naïve Bayes classifier works, Classifier building in Simit-learn, building a probabilistic classification model using Naïve Bayes, Zero chance drawback Probability Problem.

• Introduction to ideas of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.

• This case study is related to the modeling technique of Market Basket Analysis wherever you'll study about loading of data, numerous techniques for plotting the things and running the algorithms. It includes checking out what are the items that go hand in hand and thence will be clubbed together. This can be used for various real world eventualities like a supermarket shopping cart and so on.