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data science

1. An Introduction to DATA SCIENCE FYBSC

2. SYLLABUS Introduction to Data Science types of data Evolution of Data Science Data Science Roles Stages in a Data Science Project Applications of Data Science in various fields – Data Security Issue Data Collection Strategies – Data PreProcessing Overview

3. Data v/s Information Data - > always in raw form ; storage is in the form of 0’s and 1’s Information -> Processed form of data. Process A 66 66 99 B 55 88 98 C 66 87 89 Roll no mks1 mks2 Mks3 A 66 66 99 b 55 88 98

4. Data v/s Information Data Information Meaning Method of Collection Format of collection Consists of Can we take a decision? Dependency?? Based on Examples…

5. Data v/s Information Data Information Meaning Raw facts Processed fact Method of Collection Random collection Specific collection Format of collection Unorganized form of collection Systematic form of processed data Consists of Text and numbers Refined form of data Can we take a decision? Decision making process is difficult Easy to take decision Dependency?? Data is not depend on information Information is dependent on data Based on Records and observation Analysis Examples…

6. Data Shape -> how data is represented in business and storage form

7. Types of data

8. Further classification of data Demographic data  (this customer is a woman, 35 years old, has two children, etc.). Transactional data  (the products she buys each time, the time of purchases, etc.) Web behaviour data  (the products she puts into her basket when she shops online). Data from customer-created texts  (comments about the retailer that this woman leaves on the internet).

9. DBMS… way of data extraction

10. Problems faced by current DBMS large quantities of data is generated /processed. data may get doubled in every say 3 months. Seeking knowledge from this massive data is most required. Fast developing in computer science and engineering techniques generates new demands. To fulfill those demands we require to analyze the data Data Rich , Information Poor.. Raw data by itself does not provide much information. In today's life we require only significant data from which we can judge the customer’s likings and strategies.

11. What is data mining?

12. Data Mining is…. Data mining is a powerful tool with great potential. Focus on the most important information in data Gives detail information about their potential customer and their behavior. Extraction of useful information. Finding useful valid and understandable data or patterns in a data. It is also defined as finding hidden information in a data base

13. Why Big Data 15 Old Model: Few companies are generating data, all others are consuming data New Model: all of us are generating data, and all of us are consuming

14. Why Big Data?? 16

15. examples of big data and ML

16. Data sources..

17. What is Data Science? various tools, algorithms, and machine learning principles involves obtaining meaningful information Involves elements like mathematics, statistics, computer science How Data Science Works? Problem Statement Data Collection Optimization and Deployment: Data Analysis and Exploration Data Modelling Data Cleaning

18. The Data Science Lifecycle Capture : Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data. Maintain : Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used. Process : Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis. Analyze : Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data. Communicate : Data Reporting, Data Visualization,  Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

19. What is Data Science? Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.

20. Importance of Data Science 01

21. Career Opportunities " The rise of Data Science needs will create roughly 11.5 million job openings by 2026"    US Bureau of Labour Statistics "By 2026, Data Scientists and Analysts will become the number one emerging role in the world."    World Economic Forum Data Science and Artificial Intelligence are amongst the hottest fields of the 21st century that will impactall segments of daily life by 2025, from transport and logistics to healthcare and customer service.

22. Examples: Oil giant Shell, for instance, used data science to anticipate machine failure at facilities across the world. Agricultural company Cargill developed a mobile data-tracking app that helps shrimp farmers reduce mortality rates. Dr. Pepper Snapple Group analyzed data with machine learning to glean more details about beverage sales and vendors. And freight company Pitt Ohio used historical data and predictive analytics to estimate delivery time with 99 percent accuracy.

23. Facts on Data Generation

24. Facts on Data Genaration Statistics show that more than 500 terabytes of new data are entered into the databases of the social networking site Facebook every day. A single Jet engine can generate over 10 terabytes of data in 30 minutes of flight time. With several thousand flights per day, data generation reaches several petabytes . Stock Exchange is also an example of big data that generates about a terabyte of new trade data per day

25. How does Data Science Work? 02

26. Collect Data Raw data is gathered from various sources that explain the business problem Using various statistical analysis, and machine learning approaches, data modeling is performed to get the optimum solutions that best explain the business problem. Actionable insights that will serve as a solution for the business problems gathered through data science. How does Data Science Work? Analyze Data Insights

27. Collect Data Gather the previous data on the sales that were closed. Use statistical analysis to find out the patterns that were followed by the leads that were closed. Use machine learning to get actionable insights for finding out potential leads. Consider an Example! Analyze Data Insights Suppose there is an organization that is working towards finding out potential leads for their sales team.  They can follow the following approach to get an optimal solution using Data Science:

28. L ets check relationship between AI and Data Science “In above example we saw machine learning is required for insights”

29. AI and Data Science 03

30. Data science and artificial intelligence are not the same. “ Data science and artificial intelligence are two technologies that are transforming the world. While artificial intelligence powers data science operations, data science is not completely dependent on AI. Data Science is leading the fourth industrial revolution.   ”

31. Data science also requires machine learning algorithms , which results in dependency on AI.  

32. Comparison Between AI and Data Science Data science jobs require the knowledge of ML languages like R and Python to perform various data operations and computer science expertise. Data science uses more tools apart from AI. This is because data science involves multiples steps to analyze data and generate insights. Data science models are built for statistical insights whereas AI is used to build models that mimic cognition and human understanding.

33. Comparison Between AI and Data Science Today’s industries require both, data science and artificial intelligence.  Data science  will help them make necessary data-driven decisions and assess their performance in the market, while artificial intelligence will help industries work with smarter devices and software that will minimize workload and optimize all the processes for improves innovation.

34. Comparison Between AI and Data Science

35. Class Activity 1 Justify the role of data scientist. What is the Prerequisites for Data Science How one can observe different types of data in “Identifying a particular type of disease’ What are the responsibilities of Data Scientist , Data Analyst , Data Engineers .