data science : make smarter business decisions

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  1. 1. www.edureka.in/data-science Data Science Make Business decisions Smarter
  2. 2. www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Objectives What is data mining What is data science?? What is need of data scientist?? Stages of data mining?? Roles and Responsibilities of a Data Scientist. Sentiment analysis on Zomato reviews At the end of this session, you will be able to
  3. 3. www.edureka.in/data-scienceSlide 3 Data Science Applications: Wine Recommendation Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
  4. 4. www.edureka.in/data-scienceSlide 4 Data Science Applications: Pizza Hut Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
  5. 5. www.edureka.in/data-scienceSlide 5 Data Science Applications: Summarize News
  6. 6. www.edureka.in/data-scienceSlide 6 How about this? Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
  7. 7. www.edureka.in/data-scienceSlide 7 Whats Common in these Applications? According to Wikipedia: Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. These scenarios involve: Storing, organizing and integrating huge amount of unstructured data Processing and analyzing the data Extracting knowledge, insights and predict future from the data Storage of big data is done in Hadoop. For more details on Hadoop please refer Big data and Hadoop blog http://www.edureka.in/blog/category/big-data-and-hadoop/ Processing, Analyzing, extracting knowledge and insights are done through Machine Learning. All above technologies and steps together can be termed as data mining process. Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
  8. 8. Slide 8Slide 8 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cross Industry standard Process for data mining ( CRISP DM ) Stages of Analytics / Data Mining
  9. 9. Slide 9Slide 9 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Knowledge discovery and data mining ( KDD) Stages of Analytics / Data Mining
  10. 10. Slide 10Slide 10 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is data science?? More data usually beats better algorithms, Such as: Recommending movies or music based on past preferences No matter how extremely unpleasant your algorithm is, they can often be beaten simply by having more data (and a less sophisticated algorithm).
  11. 11. Slide 11Slide 11 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Components data science??
  12. 12. Slide 12Slide 12 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is R R is Programming Language R is Environment for Statistical Analysis R is Data Analysis Software
  13. 13. Slide 13 www.edureka.in/data-science Data Science: Demand Supply Gap Big Data Analyst Big Data Architect Big Data Engineer Big Data Research Analyst Big Data Visualizer Data Scientist 50 43 44 31 23 18 50 57 56 69 77 82 Filled job vs unfilled jobs in big data Filled Unfilled Vacancy/Filled(%) Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015http://www.gartner.com/newsroom/id/2207915
  14. 14. Slide 14 www.edureka.in/data-science
  15. 15. Slide 15Slide 15 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions R : Characteristics Effective and fast data handling and storage facility A bunch of operators for calculations on arrays, lists, vectors etc A large integrated collection of tools for data analysis, and visualization Facilities for data analysis using graphs and display either directly at the computer or paper A well implemented and effective programming language called S on top of which R is built A complete range of packages to extend and enrich the functionality of R
  16. 16. Slide 16Slide 16 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data Visualization in R This plot represents the locations of all the traffic signals in the city. It is recognizable as Toronto without any other geographic data being plotted - the structure of the city comes out in the data alone.
  17. 17. Slide 17 www.edureka.in/data-science Data Science: Job Trends
  18. 18. Slide 18 www.edureka.in/data-science Machine Learning We have so many algorithms for data mining which can be used to build systems that can read past data and can generate a system that can accommodate any future data and derive useful insight from it Such set of algorithms comes under machine learning Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data Train data ML model Algorithms
  19. 19. Slide 19 www.edureka.in/data-science Types of Learning Supervised Learning Unsupervised Learning 1. Uses a known dataset to make predictions. 2. The training dataset includes input data and response values. 3. From it, the supervised learning algorithm builds a model to make predictions of the response values for a new dataset. 1. Draw inferences from datasets consisting of input data without labeled responses. 2. Used for exploratory data analysis to find hidden patterns or grouping in data 3. The most common unsupervised learning method is cluster analysis. Machine Learning
  20. 20. Slide 20 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Common Machine Learning Algorithms Types of Learning Supervised Learning Unsupervised Learning Algorithms Nave Bayes Support Vector Machines Random Forests Decision Trees Algorithms K-means Fuzzy Clustering Hierarchical Clustering Gaussian mixture models Self-organizing maps
  21. 21. Slide 21 www.edureka.in/data-science Use Case : Zomato Ratings Review
  22. 22. Slide 22 www.edureka.in/data-science Module 1 Introduction to Data Science Module 2 Basic Data Manipulation using R Module 3 Machine Learning Techniques using R Part -1 - Clustering - TF-IDF and Cosine Similarity - Association Rule Mining Module 4 Machine Learning Techniques using R Part -2 - Supervised and Unsupervised Learning - Decision Tree Classifier Course Topics Module 5 Machine Learning Techniques using R Part -3 - Random Forest Classifier - Nave Bayers Classifier Module 6 Introduction to Hadoop Architecture Module 7 Integrating R with Hadoop Module 8 Mahout Introduction and Algorithm Implementation Module 9 Additional Mahout Algorithms and Parallel Processing in R Module 10 Project Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
  23. 23. Slide 23 Questions? Enroll for the Complete Course at : www.edureka.in/data_science Twitter @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions www.edureka.in/data_science Please Dont forget to fill in the survey report Class Recording and Presentation will be available in 24 hours at: http://www.edureka.in/blog/application-of-clustering-in-data-science-using-real-life-examples/