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Data Science Tutorial | Introduction To Data Science | Data Science Training | Edureka

This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:

1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components

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Data Science Tutorial | Introduction To Data Science | Data Science Training | Edureka

  1. 1. www.edureka.co/data-scienceEdureka’s Data Science Certification Training
  2. 2. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Agenda for Today’s Session Why Data Science? What is Data Science? Who is a Data Scientist? How a Problem is Solved in Data Science? Data Science Components Demo
  3. 3. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Data Science?
  4. 4. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Data Science? The most abundant thing today, is data. We have data about everything which is increasing multifolds everyday! Increase in data Then
  5. 5. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Data Science?
  6. 6. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Data Science? It is called data-driven science, it is an inter-disciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured. A question that usually is asked to data scientists is “Tell us something, that we don’t know?” It involves: Programming + Statistics + Business
  7. 7. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Who is a Data Scientist?
  8. 8. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Who is a Data Scientist? MATHS Statistics Discrete Maths Information Theory Combinatorics Decision Theory Machine Learning Data Viz Builders Statistical Programmers Econometricians Management Scientists Actuaries DATA SCIENTIST BUSINESS Economics Finance Marketing Operations Management INFORMATION SYSTEMS Computer Science Software Engineering Systems Development BI Developers Data Analysis
  9. 9. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Role Of A Data Scientist The Data Scientist will be responsible for designing and creating processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes. Responsibilities ➢ Selecting features, building and optimizing classifiers using machine learning techniques. ➢ Data mining using state-of-the-art methods. ➢ Extending company’s data with third party sources of information when needed. ➢ Processing, cleansing, and verifying the integrity of data for analysis. ➢ Building predictive models using Machine Learning algorithms.
  10. 10. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How a problem is solved in Data Science?
  11. 11. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science
  12. 12. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results ➢ Discovery involves acquiring data from all the identified internal and external sources that can help answer the business question. ➢ This data could be • logs from webservers • social media data • census datasets • data streamed from online sources via APIs
  13. 13. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results Doctor gets this data from the medical history of the patient. Attributes: npreg – Number of times pregnant glucose – Plasma glucose concentration bp – Blood pressure skin – Triceps skinfold thickness bmi – Body mass index ped – Diabetes pedigree function age – Age income – Income Income is an irrelevant attribute in the prediction of diabetes
  14. 14. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results ➢ The data can have a lot of inconsistencies like missing values, blank columns, abrupt values and incorrect data format which need to be cleaned. ➢ It is required to explore, preprocess and condition data prior to modeling. ➢ This will help you to spot the outliers and establish a relationship between the variables.
  15. 15. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results This data has lot of anomalies and needs cleansing before further analysis can be done.
  16. 16. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results We clean and preprocess this data by removing the outliers, filling up the null values and normalizing the data type.
  17. 17. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results ➢ Here, we determine the methods and techniques to draw the relationships between variable. ➢ Apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.
  18. 18. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results Use of visualization techniques like histograms, line graphs, box plots to get a fair idea of the distribution of data.
  19. 19. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results ➢ Develop datasets for training and testing purposes. ➢ Consider whether existing tools will suffice for running the models. ➢ Analyze various learning techniques like classification, association and clustering to build the model.
  20. 20. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results This is a decision tree based on different attributes.
  21. 21. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Data Preparation Model Planning Model Building Operationalize Communicate Results ➢Deliver final reports, briefings, code and technical documents. ➢Implement pilot project in a real-time production environment. ➢Look for performance constraints if any.
  22. 22. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Initialization Model Planning Model Building Deployment Communicate Results ➢ Identify all the key findings and communicate to the stakeholders. ➢ Explaining the model and result to medical authorities. ➢ Determine if the results of the project are a success or a failure based on the criteria developed.
  23. 23. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science Discovery Initialization Model Planning Model Building Deployment Communicate Results ➢ Diabetes Positive set: • glucose > 154 • glucose >127 & <= 154 + bmi >30.9 • glucose<=127 + pregnant >5 • glucose<=127 + pregnant <=5 + age >28 • glucose<=127 + pregnant <=5 + age <=28 +bmi > 30.9 ➢ Diabetes Negative set: • glucose > 154 • glucose >127 & <= 154 + bmi <=30.9 • glucose<=127 + pregnant <=5 + age <=28 +bmi <= 30.9 ➢ We can use this decision tree result to know whether the patient is vulnerable to diabetes or not.
  24. 24. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How to choose Algorithms in Data Science?
  25. 25. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem Solving in Data Science We take a top down approach to answer the same: Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How much or how many? How is this organized? What should I do next? These are the 5 questions which can be answered in data science. These algorithms are fitted into three types of categories, which are the following:
  26. 26. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Categories of Algorithms Supervised Learning Reinforcement Learning Unsupervised Learning Types of Learning
  27. 27. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Supervised Learning Supervised Learning Unsupervised Learning Reinforcement Learning Let’s take an example here. Say you are a teacher, and your way of teaching is, To teach by example, i.e for every problem in their life you are providing solutions to them, this type of learning is called supervised learning. Let’s take the same example forward: Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. Teaching by Example
  28. 28. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Unsupervised Learning Supervised Learning Unsupervised Learning Reinforcement Learning When your kids are taking decisions out of their own understanding, this type of learning would be Unsupervised Learning. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. Self Learning
  29. 29. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement Learning Supervised Learning Unsupervised Learning Reinforcement Learning If a new situation comes up, the kid will take actions on his own i.e from his past experiences, but as a parent towards the end of an action you can tell him whether he did good or not. Good or Bad?
  30. 30. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Data Science Tools
  31. 31. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Data Science Tools The tool that is widely used by Data Analysts is R R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
  32. 32. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why R? Programming and Statistical Language Data Analysis and Visualization Apart from being used as a statistical language , it can also be used a programming language for analytical purposes. Apart from being one of the most dominant analytics tools, R also is one of the most popular tools used for data visualization.
  33. 33. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why R? Simple and Easy to Learn Free and Open Source R is a simple and easy to learn, read & write R is an example of a FLOSS (Free/Libre and Open Source Software) which means one can freely distribute copies of this software, read it's source code, modify it, etc.
  34. 34. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Datasets A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer Now to do any kind of analysis, you need data right? This need of data is fulfilled through Data Sets. What are datasets? Sample Dataset
  35. 35. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Datasets But what if you have a HUGE dataset! Ever heard of Big Data?
  36. 36. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Big Data?
  37. 37. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING What is Big Data? “Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications” Volume Variety Velocity Value Veracity Uncertainty and inconsistencies in the data Finding correct meaning out of the data Data is being generated at an alarming rate Processing different types of data Processing increasing huge data sets
  38. 38. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING Big Data Now these problems had to be dealt with, right? Hence, Hadoop came into the picture.
  39. 39. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Hadoop?
  40. 40. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING What is Hadoop? Hadoop is a framework that allows us to store and process large data sets in parallel and distributed fashion H A D O O P Storage: Distributed File System Processing: Allows parallel & distributed processing
  41. 41. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING What is Hadoop? Hadoop is a framework that allows us to store and process large data sets in parallel and distributed fashion H A D O O P Storage: Distributed File System Processing: Allows parallel & distributed processing
  42. 42. www.edureka.co/big-data-and-hadoopEDUREKA HADOOP CERTIFICATION TRAINING What is Hadoop? Hadoop is a framework that allows us to store and process large data sets in parallel and distributed fashion H A D O O P Storage: Distributed File System Processing: Allows parallel & distributed processing
  43. 43. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Hadoop? Now you need a data analytics tool, which can handle this much processing and data. For that we use Spark R
  44. 44. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Spark R? SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 2.1.1,SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets. WOW!
  45. 45. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo
  46. 46. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo This dataset provides detailed road safety data about the circumstances of personal injury road accidents from 1979 -2013. Our aim is to find the following things: To find the number of accidents happened: ✓ In various weather conditions ✓ In various light conditions ✓ In various road surface conditions ✓ With make information of the accident vehicles ✓ During various days of week ✓ On various road types ✓ Number of casualties per accident per year ✓ Number of accidents happening at various speed limits We have to find the results of the queries in Hadoop
  47. 47. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo with make information of the accident vehicles Huge amount of Accident data 1 Data Stored in HDFS 2 Using R for Analysis 3 in various weather conditions in various light conditions in various road surface conditions Analyze the following queries for accidents
  48. 48. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Session In A Minute Why Data Science? Demo How is a problem solved in Data Science? Who is a Data Scientist? Data Science Components What is Data Science?
  49. 49. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Thank You … Questions/Queries/Feedback

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