Data Science Certification Training in Richmond, VA
Key Features: 32 hours of Classroom training 100% Money Back Guarantee Real-life case studies Life time access to Learning Management System (LMS) Practical Assignments Certification: Zillion Venture certifies you based on the project. 24/7 customer support About Data Science Certification Training Zillion Venture’s Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You’ll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You’ll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR. Who Should Apply? The training is a best fit for: IT professionals interested in pursuing a career in analytics Graduates looking to build a career in analytics and data science Experienced professionals who would like to harness data science in their fields Anyone with a genuine interest in the field of data science Data Science Certification Training - Course Agenda Introduction to Data Science Goal – Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools. Objectives – At the end of this Module, you should be able to: • Define Data Science • Discuss the era of Data Science • Describe the Role of a Data Scientist • Illustrate the Life cycle of Data Science • List the Tools used in Data Science • State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science Topics: • 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 Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis. Objectives – At the end of this Module, you should be able to: • Define Statistical Inference • List the Terminologies of Statistics • Illustrate the measures of Center and Spread • Explain the concept of Probability • State Probability Distributions Topics: • What is Statistical Inference? • Terminologies of Statistics • Measures of Centers • Measures of Spread • Probability • Normal Distribution • Binary Distribution Data Extraction, Wrangling and Exploration Goal – Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format. Objectives – At the end of this Module, you should be able to: • Discuss Data Acquisition techniques • List the different types of Data • Evaluate Input Data • Explain the Data Wrangling techniques • Discuss Data Exploration Topics: • Data Analysis Pipeline • What is Data Extraction • Types of Data • Raw and Processed Data • Data Wrangling • Exploratory Data Analysis • Visualization of Data Hands-On/Demo: • Loading different types of dataset in R • Arranging the data • Plotting the graphs Introduction to Machine Learning Goal – Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms. Objectives – At the end of this module, you should be able to: • Define Machine Learning • Discuss Machine Learning Use cases • List the categories of Machine Learning • Illustrate Supervised Learning Algorithms Topics: • What is Machine Learning? • Machine Learning Use-Cases • Machine Learning Process Flow • Machine Learning Categories • Supervised Learning Linear Regression Logistic Regression Hands-On/Demo: • Implementing Linear Regression model in R • Implementing Logistic Regression model in R Classification Goal – In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision
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