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Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial Using R | Edureka

** Data Science Certification using R: https://www.edureka.co/data-science ** <br>This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics: <br><br>Why Data Science? <br>What is Data Science? <br>Who is a Data Scientist? <br>What does a Data Scientist do? <br>How to solve a problem in Data Science? <br>Data Science Tools <br>Demo <br><br>Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs <br>Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist <br><br>Follow us to never miss an update in the future. <br><br>Instagram: https://www.instagram.com/edureka_learning/ <br>Facebook: https://www.facebook.com/edurekaIN/ <br>Twitter: https://twitter.com/edurekain <br>LinkedIn: https://www.linkedin.com/company/edureka

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Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial Using R | Edureka

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  1. Agenda Why Data Science? What is Data Science? Who is a Data Scientist? What does a Data Scientist do? How to solve a problem in Data Science? Data Science Tools Demo

  2. Agenda Why Data Science? What is Data Science? Who is a Data Scientist? What does a Data Scientist do? How to solve a problem in Data Science? Data Science Tools Demo

  3. Why Data Science?

  4. Why Data Science? You can make better decisions, you can reduce your production costs by coming out with efficient ways, and give your customers what they actually want! Cost Reduction Faster & Better Decision Making Improved Services and Products Risk Detection www.edureka.co/data-science Data Science Certification Course using R

  5. Why Data Science? Data Science can help prevent Fraudulent transactions using advanced Machine Learning algorithms and prevent great monetary losses. www.edureka.co/data-science Data Science Certification Course using R

  6. What is Data Science?

  7. What is Data Science? Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. DATA SCIENCE Analysis Structure Algorithm Programming Insight Process www.edureka.co/data-science Data Science Certification Course using R

  8. What is Data Science? It is an inter-disciplinary field deploying scientific methods, processes and systems to gain insight from data in various forms. Tell us something we don’t know already. Statistics Code Business www.edureka.co/data-science Data Science Certification Course using R

  9. What is Data Science? How is this different from what statisticians have been doing for years? Business Administration Business Analyst Exploratory Data Analysis Machine Learning & Advanced Algorithms Data Scientist Data Product Engineering www.edureka.co/data-science Data Science Certification Course using R

  10. Who is Data Scientist?

  11. Who is a Data Scientist? www.edureka.co/data-science Data Science Certification Course using R

  12. Who is a Data Scientist? Statistics Discrete Theory Combinatorics Decision Theory Machine Learning www.edureka.co/data-science Data Science Certification Course using R

  13. Who is a Data Scientist? www.edureka.co/data-science Data Science Certification Course using R

  14. Who is a Data Scientist? Economics Finance Operations Management Business Intelligence www.edureka.co/data-science Data Science Certification Course using R

  15. Who is a Data Scientist? www.edureka.co/data-science Data Science Certification Course using R

  16. Who is a Data Scientist? Computer Science Software Engineering Systems Development www.edureka.co/data-science Data Science Certification Course using R

  17. What does a Data Scientist do?

  18. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  19. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  20. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  21. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  22. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  23. What does a Data Scientist do? Optimizing and building classifiers using Machine Learning Building Prediction Models Processing & Cleansing Data Extending Data Data Mining www.edureka.co/data-science Data Science Certification Course using R

  24. How to solve a problem in Data Science?

  25. How to solve a problem in Data Science? Model Building Communicating Results Data Preparation 2 1 3 4 5 6 Discovery Model Planning Operationalize www.edureka.co/data-science Data Science Certification Course using R

  26. How to solve a problem in Data Science? ➢ Discovery involves acquiring data from all identifies internal and external resources that can help with a business solution. 1 Discovery 2 Data Preparation ➢ You assess if you have the required resources present in terms of people, technology, time and data to support the project. 3 Model Planning 4 Model Building 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  27. How to solve a problem in Data Science? ➢ In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. 1 Discovery 2 Data Preparation ➢ This is what a Sandbox is supposed to look like; 3 Model Planning Preparing the Analytics Sandbox Performing ETLT Data Conditioning Survey & Visualize 4 Model Building ➢ ETLT means to Extract, Transform, Load and Transform. 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  28. How to solve a problem in Data Science? ➢ You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools. 1 Discovery 2 Data Preparation Common Tools for Model Planning 3 Model Planning 4 Model Building SQL Service Analysis Services R SAS/ ACCESS 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  29. How to solve a problem in Data Science? ➢ In this phase, you will develop datasets for training and testing purposes. 1 Discovery 2 Data Preparation Common Tools for Model Building 3 Model Planning 4 Model Building SAS Miner Alpine Miner WEKA SPCS MATLAB Statistica 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  30. How to solve a problem in Data Science? ➢ In this phase, you deliver final reports, briefings, code and technical documents. 1 Discovery 2 Data Preparation ➢ In addition, sometimes a pilot project is also implemented in a real- time production environment. 3 Model Planning ➢ This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment. 4 Model Building 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  31. How to solve a problem in Data Science? ➢ You do the following things in this phase; 1 Discovery 2 Data Preparation 1. You identify all the key findings 3 Model Planning 2. communicate to the stakeholders 3. Look for performance constraints, if any 4 Model Building 4. determine if the results of the project are a success or a failure 5 Operationalize 6 Communicate www.edureka.co/data-science Data Science Certification Course using R

  32. How to Choose an Algorithm in Data Science? Is it A or B? Classification Algorithm Is this weird? Anomaly Detection Algorithm How much / How many? Regression Algorithm How is this organised? Clustering Algorithm What should I do next? Reinforcement Learning www.edureka.co/data-science Data Science Certification Course using R

  33. What is machine Learning? It is a type of Artificial Intelligence that makes the computers capable of learning on their own i.e without explicitly being programmed. With machine learning, machines can update their own code, whenever they come across a new situation. www.edureka.co/data-science Data Science Certification Course using R

  34. Categories of Algorithm 1 2 3 Supervised Learning Unsupervised Learning Reinforcement Learning Unsupervised Learning is a type of machine learning algorithm that uses a input datasets without labelled responses to draw inference. Reinforcement Learning is a type of algorithm inspired by behaviourist psychology, concerned with taking actions to maximise reward. Supervised Learning is a type of machine learning algorithm that uses a known dataset to make predictions. www.edureka.co/data-science Data Science Certification Course using R

  35. Data Science Tools

  36. Data Science Tools 1. 2 3 4 5 5 Datasets R programming Spark Big Data Hadoop www.edureka.co/data-science Data Science Certification Course using R

  37. Demo

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