Introduction to the Data Science Certification Course Online Training in USA

• What is Data Science?
• What does Data Science involve?
• Era of Data Science
• Business Intelligence vs Data Science
• Life cycle of Data Science
• Tools of Data Science
• Introduction to Big Data and Hadoop
• Introduction to R
• Introduction to Spark
• Introduction to Machine Learning

• What is Statistical Inference?
• Terminologies of Statistics
• Measures of Centers
• Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data
• Loading different types of dataset in R
• Arranging the data
• Plotting the graphs

• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression

• Implementing Linear Regression model in R
• Implementing Logistic Regression model in R

• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
• Implementing Decision Tree model in R
• Implementing Linear Random Forest in R
• Implementing Naive Bayes model in R
• Implementing Support Vector Machine in R

• What is Clustering & its use cases
• What is K-means Clustering?
• What is C-means Clustering?
• What is Canopy Clustering?
• What is Hierarchical Clustering?
• Implementing K-means Clustering in R
• Implementing C-means Clustering in R
• Implementing Hierarchical Clustering in R

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendations
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation use cases
• Implementing Association Rules in R
• Building a Recommendation Engine in R

• The concepts of text-mining
• Use cases
• Text Mining Algorithms
• Quantifying text
• TF-IDF
• Beyond TF-IDF
• Implementing Bag of Words approach in R
• Implementing Sentiment Analysis on Twitter Data using R

• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective ETS model for forecasting
• Visualizing and formatting Time Series data
• Plotting decomposed Time Series data plot
• Applying ARIMA and ETS model for Time Series Forecasting
• Forecasting for given Time period

• Reinforced Learning
• Reinforcement learning Process Flow
• Reinforced Learning Use cases
• Deep Learning
• Biological Neural Networks
• Understand Artificial Neural Networks
• Building an Artificial Neural Network
• How ANN works
• Important Terminologies of ANN’s

You will never lose any lecture. You can attend the missed session, in any other live batch. 

• We also provide placement assistance with specialized team for every stream to prepare a professional cv’s placement assistant programs like mock interviews and resume marketing etc.

Yes, you can attend a Demo session before enrolment, student feedback is very important to us while they enrolling with us.

All the instructors at Sprint IT are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by sprint for providing an awesome learning experience.