Data Science Training
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The document outlines a data science training program that covers fundamental concepts, techniques, and applications across disciplines such as statistics and computer science. Participants will learn data analysis, visualization, machine learning algorithms, and practical data handling tools. The course is suitable for anyone from aspiring data scientists to business analysts, with no specific prerequisites except for a basic understanding of R being beneficial.
Data Science Training
- 1. Data Science Training Let’s Get Started!
- 2. Description Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K- Means Clustering, Decision Trees, Random Forest, and Naive Bayes.
- 3. Objectives • Gain insight into the 'Roles' played by a Data Scientist • Analyze several types of data using R • Describe the Data Science Life Cycle • Work with different data formats like XML, CSV etc. • Learn tools and techniques for Data Transformation • Discuss Data Mining techniques and their implementation
- 4. Objectives • Analyze data using Machine Learning algorithms in R • Explain Time Series and it’s related concepts • Perform Text Mining and Sentimental analyses on text data • Gain insight into Data Visualization and Optimization techniques • Understand the concepts of Deep Learning
- 5. Why learn Data Science Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.
- 6. This course is appropriate for: • Developers aspiring to be a 'Data Scientist‘ • Analytics Managers who are leading a team of analysts • Business Analysts who want to understand Machine Learning (ML) Techniques • Information Architects who want to gain expertise in Predictive Analytics • 'R' professionals who want to captivate and analyze Big Data • Analysts wanting to understand Data Science methodologies
- 7. Prerequistes There is no specific pre-requisite for this training program, however basic understanding of R can be beneficial.