Introduction to the Data Science Certification Course Online Training in USA

We are providing Best Data Science Online Training Course in USA, This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kick start your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You’ll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

Data Science Certification Course Contents

Introduction to Data Science

  • 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

Statistical Inference

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

Data Extraction, Wrangling and Exploration

  • 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

Introduction to Machine Learning

  • 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

Classification Techniques

  • 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

Unsupervised Learning

  • 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

Recommender Engines

  • 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

Text Mining

  • 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

Time Series

  • 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

Deep Learning

  • 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

Data Science FAQ’s

What if I miss a class?

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

Will I Get Placement Assistance?

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.

Can i attend demo session before Enrollment?

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

Who are the instructors at Sprint IT?

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.

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