Data Science Training | Data Science Tutorial | Data Science Certification | Edureka

1. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

2. What to expect? What is Data Science? Job Roles in Data Science Components of Data Science Concepts of Statistics Power of Data Visualization Introduction to Machine Learning using R Supervised & Unsupervised Learning Classification, Clustering & Recommenders Text Mining & Time Series Deep Learning           www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

3. What is Data Science? www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

4. What is Data Science?  Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.  By combining aspects of statistics, computer science, applied mathematics and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge. Data Science Components www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

5. Job Roles of Data Science www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

6. Job Roles of Data Science Data Scientist Data Analyst Data Architect Data Engineer Database Administrator Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

7. Job Roles of Data Science Role: Cleans and organizes big data. Works on distributed computing and predictive modeling. Data Scientist Data Analyst Languages: R, SAS, Python, Matlab, SQL, Hive, Pig and Spark Data Architect Data Engineer Database Administrator Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

8. Job Roles of Data Science Role: Collects, statistical data analyses processes and performs Data Scientist Data Analyst Data Architect R, Python, HTML, JS, C, C++ and SQL Data Engineer Languages: Database Administrator Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

9. Job Roles of Data Science Role: Creates blueprints for data management systems to integrate, centralize, protect and maintain data sources. Data Scientist Data Analyst Data Architect Data Engineer Languages: SQL, XML, Hive, Pig and Spark Database Administrator Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

10. Job Roles of Data Science Role: Develops, constructs, tests and maintains architectures such as databases and large-scale processing systems. Data Scientist Data Analyst Data Architect Data Engineer Languages: SQL, Hive, Pig, R, Matlab, SAS, Python, Java, Ruby, C++ and Perl Database Administrator Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

11. Job Roles of Data Science Data Scientist Data Analyst Data Architect Data Engineer Role: Collects, analyses and interprets qualitative and quantitative data with statistical theories and methods Database Administrator Languages: R, SA, SPSS, Matlab, Tableau, Stata, Python, Perl, Hive, Spark and SQL Business Analyst Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

12. Job Roles of Data Science Data Scientist Data Analyst Data Architect Data Engineer Role: Ensures that the database is available to all relevant users, is performing properly and is being kept safe Database Administrator Business Analyst Languages: SQL, Java, Ruby on Rails, XML, C# and Python Data & Analyst Manager Statistician www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

13. Job Roles of Data Science Data Scientist Data Analyst Data Architect Data Engineer Role: Improves business processes as intermediary between business and IT Database Administrator Business Analyst Data & Analyst Manager Statistician Languages: SQL, C, Excel, Tableau, Power BI and Python www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

14. Job Roles of Data Science Data Scientist Data Analyst Data Architect Data Engineer Role: Manages a team of analysts and data Database Administrator scientists Business Analyst Data & Analyst Manager Statistician Languages: SQL, R, SAS, Python, Matlab and Java www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

15. Components of Data Science www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

16. Components of Data Science Data Science has the following components. Statistics Statistics Visualization Machine Learning Deep Learning www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

17. Concepts of Statistics www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

18. Concepts of Statistics Collection  Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation and organization of data. Analysis  Statistics began in the ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Interpretation Presentation DATA Predictive Analysis Visual Representation Figure: Concepts of Statistics www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

19. Power of Visualization www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

20. Scope of Visual Analytics www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

21. Data Visualization Integrate Different Data Sets Analyze Visualize www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

22. Introduction to Machine Learning using R www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

23. Machine Learning using R  Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data.  Closely related to computational statistics.  Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics. Speech Recognition Face Recognition Anti Virus Weather Prediction www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

24. Supervised & Unsupervised Learning www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

25. Supervised & Unsupervised Learning Supervised Learning Unsupervised Learning Supervised learning is the machine learning task of inferring a function from labelled training data. The training data consists of a set of training examples. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses. Algorithms: SVM, Regression, Naive Bayes, Decision Trees, K-nearest Neighbour Algorithm & Neural Networks Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models E.g. If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas. E.g. In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow fruits”. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

26. Reinforcement Learning Reinforcement Learning  Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.  It differs from standard supervised learning in that correct input/output pairs are never presented nor sub-optimal actions explicitly corrected. Applications: Robots used in Manufacturing, Advertising, Inventory Management, Player vs AI Games. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

27. Classifiers www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

28. Introduction to Classification  Classification is the problem of identifying to which set of categories a new observation belongs.  Classification belongs to the supervised learning.  It is based on the training set of data containing observations. Figure: Examples of Classification www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

29. Classification Algorithms Linear Naive Bayes Decision Trees Logistic Regression SVM Perceptron Linear Classifier Radial Basis Function (RBF) Neural Networks Non Linear Recurrent Neural Network (RNN) Kernel Estimation Quadratic Modular Neural Network www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

30. Classification Example  Let us look at how a classification algorithm works.  Here is an example of Linear Regression using alternating least squares method. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

31. Clustering www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

32. Clustering  Clustering is the problem of categorizing objects to which different groups without any prior information about labels or classes.  Clustering belongs to the unsupervised learning. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

33. Recommender Systems www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

34. Recommender Systems  Recommender System is a subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item.  Recommendations can be everywhere from Netflix & BookMyShow movies to YouTube videos, Amazon products to Goibibo hotels, Xbox games to Zomato restaurants. Figure: Companies using Recommendation Systems www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

35. Recommender Systems - Example Recommendation systems work in two ways: 1. approaches building a model from a user's past behaviour as well as similar decisions made by other users. Collaborative Filtering: Collaborative filtering 2. approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. Content-based Filtering: Content-based filtering Figure: Movie Recommendation in IMDb www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

36. Text Mining www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

37. Text Mining Text Sentiment Analysis Text Clustering Categorization Document Summarization Concept Extraction www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

38. Text Mining Text categorization (a.k.a. text classification) is the task of assigning predefined categories to free-text documents. E.g. News categories, academic paper categories. Text Categorization www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

39. Text Mining Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Document Summarization www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

40. Text Mining Text Clustering is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering Text Clustering www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

41. Text Mining Concept mining is an activity that results in the extraction of concepts from artifacts. Solutions to the task typically involve aspects of artificial intelligence and statistics, such as data mining and text mining. Concept Extraction www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

42. Text Mining Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Use Case: Twitter Sentiment Analysis, Customer Sentiment Analysis Sentiment Analysis www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

43. Time Series www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

44. Time Series  A time series is a series of data points indexed (or listed or graphed) in time order.  Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.  Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Ocean Tides Sunspots Stock Market Prices www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

45. Deep Learning www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

46. Deep Learning Before moving ahead let us look at some of the drawbacks of machine learning. 1. Traditional ML algorithms are not useful while working with high dimensional data, that large number of is where we have outputs. a inputs and For example, in case of handwriting recognition we have large amount of input where we will have different type of inputs associated with different type of handwriting. 2. Second major challenge with traditional machine learning models is a process called feature extraction. Specifically, the programmer needs to tell the computer what kinds of things it should look for so as to make more accurate decision. www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

47. Deep Learning Artificial Intelligence  Deep learning is one of the only methods by which we can circumvent the challenges of feature extraction in machine learning. Machine Learning  This is because deep learning models are capable of learning to focus on the right features by themselves, requiring little guidance from the programmer. Deep Learning  Therefore, we can say that Deep Learning is: 1. A collection of statistical machine learning techniques 2. Used to learn feature hierarchies 3. Often based on artificial neural networks www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

48. Deep Learning Examples Figure: Face Recognition using Deep Learning www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

49. Deep Learning Examples Speech Recognition Self Driving Cars Automatic Translation www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING

50. Summary www.edureka.co/data-science EDUREKA DATA SCIENCE CERTIFICATION TRAINING