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

Upon completing this data science training, you will be able to learn how to analyze the data, serve useful information, and apply algorithms and mathematical skills to the data that helps make sound business decisions, develop the product, and forecast the business for future development.

The Data ScienceTraining Certification was designed for professionals with and without work experience in any of the profiles below: 

• Freshers / Officials from any field 
• Who wants to seek their career as a data scientist
• Big data analyst professionals 
• Machine learning professionals
• Predictive Analytics and Information Architects
• Programming Enthusiasts.

Everyone with basic programming knowledge and a good understanding of mathematics, logical reasoning is mandatory.

You need to look for the right platform where you gain full knowledge; one such as HKR training, provides excellent support for data science training by illustrating real-time scenarios and use cases. And gradually that helps you to build your career in this field.

Once you complete the entire course, HKR issues the course complete certificate, which will help you get your desired job.

Our trainers are highly qualified and certified with many years of industry experience and technology background.

We don't provide any job, but we offer complete assistance and guide you to get through the certification exam and help you clear the interview with some amplified interview questions. Moreover, your job is based on your performance in the interview panel.