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Data Science Training in Jaipur

Data Science Training In Jaipur

Technoglobe city’s best institute for training and internship is providing training and internship in Data Science in Jaipur. Technoglobe is been performing excellent since last 17 years in the field of technology. Some of our achievements include affiliation from Rajasthan Technical University and authorized partner of Microsoft & HP.

Tehnoglobe provides in depth knowledge about the technology that Data Science also known as data drive science, is an interdisciplinary field of scientific method, processes, algorithms to extract knowledge. The modern infrastructure and friendly environment with the updated labs helps the students to get practical experience about their knowledge. We have the best qualified and well experienced trainers in Jaipur. Ours helps train students to understand the concept of Data Science that how to unify statistics, data analysis, machine learning and their related methods.

Tehnoglobe which is certified with the ISO certificatin ( International Standard Organization). Our trainers provide trainings on international curriculm and based on international quality standards focusing on market trends. Technoglobe also provide International Certifiactions from the global leaders like Microsoft & HP.

Technoglobe facilitates students on live and real time projects after their training. Through live and real time projects students experience that how the Data Engineers setup the database and data storage in order to facilitate process of data mining, data munging and other process. With training and internship Technoglobe provides assured placements for students. We are the recruiting partners of more than 200 companies across the country. Due to our quality training and internship till now more than 1000 of our students are working in their dream companies.

Data Science training in Jaipur

Data Science Course Training in Jaipur

Data Science Training will help you to find good job or create chance for your promotion. We have plenty of experienced professional instructors who will teach you at best level with live project that will help you to implement new stuffs. We designed this Data Science course according to current demand of software industry.

Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

Data Science is Cloud platform that employees can access entirely over the Internet — there’s no infrastructure to buy, set up, or manage — you just log in and get to work. And now our new Lightning Platform gives you the fastest, most complete way to put your customers at the center of everything you do.

Data Science Course Training in Jaipur- Technoglobe is one of the best Data Science training institute in Jaipur with 100% Placement Support. We provides real-time and placement focused Data Science training in Jaipur. We have a track record of more than 1000 placements.

Technoglobe, 16 years old IT Training company provides high-quality Data Science training to students as well as working professionals to enhance their technical skills in Data Science. Candidates are provided in depth theoretical & practical knowledge in Data Science along with working on Major Project in Data Science Technology.

Data Science Course Training in Jaipur- Technoglobe is a Leading Training Centre in Jaipur that provides Data Science training courses of different modules with assured Placements. We are into 17 years experience in these trainings. Call us 9928556083

Data Science Course

    R with Real Time Examples

  • Types of variables
  • Using Variables
  • Logical Variables and Operators
  • The "While" Loop
  • Using the console
  • The "For" Loop
  • The "If" statement
  • What is a Vector?
  • Let's create some vectors
  • Using the [] brackets
  • Vectorized operations
  • The power of vectorized operations
  • Functions in R
  • Packages in R
  • Matrices
  • Building Your First Matrix
  • Naming Dimensions
  • Colnames() and Rownames()
  • Matrix Operations
  • Visualizing With Matplot()
  • Subsetting
  • Visualizing Subsets
  • Creating Your First Function
  • Importing data into R
  • Exploring your dataset
  • Using the $ sign
  • Basic operations with a Data Frame
  • Filtering a Data Frame
  • Introduction to qplot
  • Building Dataframes
  • Merging Data Frames
  • Loop Functions
  • lapply()
  • sapply()
  • apply()
  • tapply()
  • mapply()
  • Regular Expressions
  • Saving r objects
  • dplyr package
  • Shiny Apps

Statistics with R

  • Data collection
  • Data collection - Questionaire Designing
  • Data collection - Observation
  • Data collection - Case Study Method
  • Qualitative Data Vs Quantitative Data
  • Data Patterns
  • Deciles Statistics
  • Venn Diagram
  • Central limit theorem
  • Chebyshev's Theorem
  • Kurtosis
  • Normal Distribution
  • Laplace Distribution
  • Log Gamma Distribution
  • Rayleigh Distribution
  • Exponential distribution
  • Multinomial Distribution
  • Binomial Distribution
  • Beta Distribution
  • F distribution
  • Negative Binomial Distribution
  • Gamma Distribution
  • Chi-squared Distribution
  • Geometric Mean
  • Harmonic Mean
  • Outlier Function
  • Stem and Leaf Plot
  • Poisson Distribution
  • Cumulative Poisson Distribution
  • Inverse Gamma Distribution
  • Continuous Uniform Distribution
  • Hypergeometric Distribution
  • Harmonic Number
  • Gumbel Distribution
  • Comparing plots
  • Power Calculator
  • Process Sigma
  • Harmonic Resonance Frequency
  • Standard normal table
  • Pooled Variance (r)
  • Mean Deviation
  • Means Difference
  • code(+theory) for Descriptive:
  • entral tendency:
  • Arithmetic Mean
  • Arithmetic Median
  • Arithmetic Mode
  • Arithmetic Range
  • Range Rule of Thumb
  • Adjusted R-Squared
  • Standard Deviation
  • Relative Standard Deviation
  • Analysis of Variance
  • Grand Mean
  • Boxplots
  • Quartile Deviation
  • Frequency Distribution
  • Bar Graph
  • Dot Plot
  • Scatterplots
  • Correlation Co-efficient
  • Pie Chart
  • Histograms
  • Cumulative Frequency
  • Cumulative plots
  • Skewness
  • Goodness of Fit
  • Transformations
  • Trimmed Mean
  • Reliability Coefficient
  • Linear regression
  • Logistic Regression
  • Quadratic Regression
  • Regression Intercept Confidence Interval
  • Residual sum of squares
  • Equation Sum of Square
  • Standard Error ( SE )
  • Root Mean Square
  • Cohen's kappa coefficient
  • Ti 83 Exponential Regression
  • Shannon Wiener Diversity Index

Machine Learning with Real Time Examples

  • Building Linear Regressors
  • Interpreting Regression Results and Interactions Terms
  • Performing Residual Analysis & Extracting Extreme Observations Cook's Distance
  • Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
  • Validating Model Performance on New Data with k-Fold Cross Validation
  • Building Non-Linear Regressors with Splines and GAMs
  • Building Logistic Regressors, Evaluation Metrics, and ROC Curve
  • Understanding the Concept and Building Naive Bayes Classifier
  • Building k-Nearest Neighbors Classifier
  • Building Tree Based Models Using RPart, cTree, and C5.0
  • Building Predictive Models with the caret Package
  • Selecting Important Features with RFE, varImp, and Boruta
  • Building Classifiers with Support Vector Machines
  • Understanding Bagging and Building Random Forest Classifier
  • Implementing Stochastic Gradient Boosting with GBM
  • Regularization with Ridge, Lasso, and Elasticnet
  • Building Classifiers and Regressors with XGBoost
  • Dimensionality Reduction with Principal Component Analysis
  • Clustering with k-means and Principal Components
  • Determining Optimum Number of Clusters
  • Understanding and Implementing Hierarchical Clustering
  • Clustering with Affinity Propagation
  • Building Recommendation Engines
  • Understanding the Components of a Time Series, and the xts Package
  • Stationarity, De-Trend, and De-Seasonalize
  • Understanding the Significance of Lags, ACF, PACF, and CCF
  • Forecasting with Moving Average and Exponential Smoothing
  • Forecasting with Double Exponential and Holt Winters
  • Forecasting with ARIMA Modelling
  • Scraping Web Pages and Processing Texts
  • Corpus, TDM, TF-IDF, and Word Cloud
  • Cosine Similarity and Latent Semantic Analysis
  • Extracting Topics with Latent Dirichlet Allocation
  • Sentiment Scoring with tidytext and Syuzhet
  • Classifying Texts with RTextTools
  • Building a Basic ggplot2 and Customizing the Aesthetics and Themes
  • Manipulating Legend, AddingText, and Annotation
  • Drawing Multiple Plots with Faceting and Changing Layouts
  • Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
  • ggplot2 Extensions and ggplotly
  • Implementing Best Practices to Speed Up R Code Preview
  • Implementing Parallel Computing with doParallel and foreach
  • Writing Readable and Fast R Code with Pipes and DPlyR
  • Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
  • Interface C++ in R with RCpp
  • Understanding the Structure of an R Package
  • Build, Document, and Host an R Package on GitHub
  • Performing Important Checks Before Submitting to CRAN
  • Submitting an R Package to CRAN
  • R Machine Learning solutions
  • Downloading and Installing R
  • Downloading and Installing RStudio
  • Installing and Loading Packages
  • Reading and Writing Data
  • Using R to Manipulate Data
  • Applying Basic Statistics
  • Visualizing Data
  • Getting a Dataset for Machine Learning
  • Reading a Titanic Dataset from a CSV File Preview
  • Converting Types on Character Variables
  • Detecting Missing Values
  • Imputing Missing Values
  • Exploring and Visualizing Data
  • Predicting Passenger Survival with a Decision Tree
  • Validating the Power of Prediction with a Confusion Matrix
  • Assessing performance with the ROC curve
  • Understanding Data Sampling in R
  • Operating a Probability Distribution in R
  • Working with Univariate Descriptive Statistics in R
  • Performing Correlations and Multivariate Analysis
  • Operating Linear Regression and Multivariate Analysis
  • Conducting an Exact Binomial Test
  • Performing Student's t-test
  • Performing the Kolmogorov-Smirnov Test
  • Understanding the Wilcoxon Rank Sum and Signed Rank Test
  • Working with Pearson's Chi-Squared Test
  • Conducting a One-Way ANOVA
  • Performing a Two-Way ANOVA
  • Fitting a Linear Regression Model with lm
  • Summarizing Linear Model Fits
  • Using Linear Regression to Predict Unknown Values
  • Generating a Diagnostic Plot of a Fitted Model
  • Fitting a Polynomial Regression Model with lm
  • Fitting a Robust Linear Regression Model with rlm
  • Studying a case of linear regression on SLID data
  • Reducing Dimensions with SVD
  • Applying the Poisson model for Generalized Linear Regression
  • Applying the Binomial Model for Generalized Linear Regression
  • Fitting a Generalized Additive Model to Data
  • Visualizing a Generalized Additive Model
  • Diagnosing a Generalized Additive Model
  • Preparing the Training and Testing Datasets
  • Building a Classification Model with Recursive Partitioning Trees
  • Visualizing a Recursive Partitioning Tree
  • Measuring the Prediction Performance of a Recursive Partitioning Tree
  • Pruning a Recursive Partitioning Tree
  • Building a Classification Model with a Conditional Inference Tree
  • Visualizing a Conditional Inference Tree
  • Measuring the Prediction Performance of a Conditional Inference Tree
  • Classifying Data with the K-Nearest Neighbor Classifier
  • Classifying Data with Logistic Regression
  • Classifying data with the Naïve Bayes Classifier
  • Classifying Data with a Support Vector Machine
  • Choosing the Cost of an SVM
  • Visualizing an SVM Fit
  • Predicting Labels Based on a Model Trained by an SVM
  • Tuning an SVM
  • Training a Neural Network with neuralnet
  • Visualizing a Neural Network Trained by neuralnet
  • Predicting Labels based on a Model Trained by neuralnet
  • Training a Neural Network with nnet
  • Predicting labels based on a model trained by nnet
  • Estimating Model Performance with k-fold Cross Validation
  • Performing Cross Validation with the e1071 Package
  • Performing Cross Validation with the caret Package
  • Ranking the Variable Importance with the caret Package
  • Ranking the Variable Importance with the rminer Package
  • Finding Highly Correlated Features with the caret Package
  • Selecting Features Using the Caret Package
  • Measuring the Performance of the Regression Model
  • Measuring Prediction Performance with a Confusion Matrix
  • Measuring Prediction Performance Using ROCR
  • Comparing an ROC Curve Using the Caret Package
  • Measuring Performance Differences between Models with the caret Package
  • Classifying Data with the Bagging Method
  • Performing Cross Validation with the Bagging Method
  • Classifying Data with the Boosting Method
  • Performing Cross Validation with the Boosting Method
  • Classifying Data with Gradient Boosting
  • Calculating the Margins of a Classifier
  • Calculating the Error Evolution of the Ensemble Method
  • Classifying Data with Random Forest
  • Estimating the Prediction Errors of Different Classifiers
  • Clustering Data with Hierarchical Clustering
  • Cutting Trees into Clusters
  • Clustering Data with the k-Means Method
  • Drawing a Bivariate Cluster Plot
  • Comparing Clustering Methods
  • Extracting Silhouette Information from Clustering
  • Obtaining the Optimum Number of Clusters for k-Means
  • Clustering Data with the Density-Based Method
  • Clustering Data with the Model-Based Method
  • Visualizing a Dissimilarity Matrix
  • Validating Clusters Externally
  • Transforming Data into Transactions
  • Displaying Transactions and Associations
  • Mining Associations with the Apriori Rule
  • Pruning Redundant Rules
  • Visualizing Association Rules
  • Mining Frequent Itemsets with Eclat
  • Creating Transactions with Temporal Information
  • Mining Frequent Sequential Patterns with cSPADE
  • Performing Feature Selection with FSelector
  • Performing Dimension Reduction with PCA
  • Determining the Number of Principal Components Using the Scree Test
  • Determining the Number of Principal Components Using the Kaiser Method
  • Visualizing Multivariate Data Using biplot
  • Performing Dimension Reduction with MDS
  • Reducing Dimensions with SVD
  • Compressing Images with SVD
  • Performing Nonlinear Dimension Reduction with ISOMAP
  • Performing Nonlinear Dimension Reduction with Local Linear Embedding
  • Preparing the RHadoop Environment
  • Installing rmr2
  • Installing rhdfs
  • Operating HDFS with rhdfs
  • Implementing a Word Count Problem with RHadoop
  • Comparing the Performance between an R MapReduce Program & a Standard R Program
  • Testing and Debugging the rmr2 Program
  • Installing plyrmr
  • Manipulating Data with plyrmr
  • Conducting Machine Learning with RHadoop
  • Configuring RHadoop Clusters on Amazon EMR
  • Deep Learning with R
  • Introduction to Multi-hidden-layer Architectures
  • Fundamental Concepts in Deep Learning
  • Introduction to Artificial Neural Networks
  • Classification with Two-Layers Artificial Neural Networks
  • Probabilistic Predictions with Two-Layer ANNs
  • Introduction to Multi-hidden-layer Architectures
  • Tuning ANNs Hyper-Parameters and Best Practices
  • Neural Network Architectures
  • Neural Network Architectures Continued
  • The LearningProcess
  • Optimization Algorithms and Stochastic Gradient Descent
  • Backpropagation
  • Hyper-Parameters Optimization
  • Introduction to Convolutional Neural Networks
  • Introduction to Convolutional Neural Networks Continued
  • CNNs in R
  • Classifying Real-World Images with Pre-Trained Models
  • Introduction to Recurrent Neural Networks
  • Introduction to Long Short-Term Memory
  • RNNs in R
  • Use-Case – Learning How to Spell English Words from Scratch
  • Introduction to Unsupervised and Reinforcement Learning
  • Autoencoders
  • Restricted Boltzmann Machines and Deep Belief Networks
  • Reinforcement Learning with ANNs
  • Use-Case – Anomaly Detection through Denoising Autoencoders
  • Deep Learning for Computer Vision
  • Deep Learning for Natural Language Processing
  • Deep Learning for Audio Signal Processing
  • Deep Learning for Complex Multimodal Tasks
  • Other Important Applications of Deep Learning
  • Debugging Deep Learning Systems
  • GPU and MGPU Computing for Deep Learning
  • A Complete Comparison of Every DL Packages in R
  • Research Directions and Open Questions

Big Data & Hadoop with Real Time Examples

  • Introduction to Big Data
  • Getting started with Hadoop
  • Hive Concepts
  • Pig Concepts
  • Use case on log collections & analysis
  • Use Case on Ecommerece

Take Away – Data Science Training in Jaipur, Data Science Certification Courses in Jaipur

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