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Data science | What is Data science
- 1. WHAT IS DATASCIENCE ? BY SHILPA KRISHNA RESEARCH SCHOLAR
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- 3. DISCOVERY It involvesacquiring data from all the identified internal and external sources which helps you to answer the business question. The data can be : 1. Logs from webservers 2. Data gathered from social media 3. Census datasets 4. Data streamed from online sources using APIs
- 4. DATA PREPARATION Datacan have lots of inconsistencies like missing value,blank columns,incorrect data format which needs to be cleaned. You need to process,explore and condition data before modeling. The cleaner your data, the better are your predictions.
- 5. MODEL PLANNING Inthis stage, you need to determine the method and technique to draw the relation between input variables. Planning for a model is performed by using different statistical formulas and visualization tools like SQL analysis services, R and SAS/access
- 6. MODEL BUILDING Datascientist distributes datasets for training and testing. Techniques like association, classification, and clustering are applied to the training dataset. The model once prepared is tested against the “testing” dataset
- 7. OPERATIONALIZE You deliverthe final baselined model with reports,code and technical documents. Model is deployed into a real-time production environment after through testing.
- 8. COMMUNICATE RESULTS Thekey findings are communicated to all stakeholders. This helps you to decide if the results of the project are a success or a failure based on the inputs from the model.
- 10. MOST PROMINENT DATASCIENTIST JOB TITLES ARE : 1) Data scientist 2) Data engineer 3) Data analyst 4) Statistician 5) Data admin 6) Business analyst
- 11. Data Scientist ROLE LANGUAGES It is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms etc… R SAS PYTHON SQL HIVE MATLAB PIG SPARK
- 12. Data Engineer ROLE LANGUAGES He is working with large amounts of data and develops constructs, tests and maintains architectures like large scale processing system and databases. SQL HIVE R SAS MATLAB PYTHON JAVA RUBY C++ PERL
- 13. Data Analyst ROLE LANGUAGES Responsible for mining vast amounts of data and look for relationships, patterns, trends in data. Later deliver compeling reporting and visualization for analyzing the data to take the most viable business decisions. R PYTHON HTML JS C C++ SQL
- 14. Statistician ROLE LANGUAGES Collects,analyses, understand qualitative and quantitative data by using statistical theories and methods. SQL R MATLAB TABLEAU PYTHON PERL SPARK HIVE
- 15. Data Administrator ROLE LANGUAGES Data admin should ensure that the database is accessible to all relevant users also makes sure that it is performing correctly and is being kept safe from hacking RUBY on Rails SQL JAVA C# PYTHON
- 16. Business Analyst ROLE LANGUAGES This professional need to improves business processes and He is an intermediary between the business executive team and IT department SQL TABLEAU POWER BI PYTHON
- 19. DEFINE THE GOAL Define a measurable and quantifiable goal Goal should be specific and precise Goal is come up with candidate hypothesis. These hypothesis can then be turned into concrete questions or goals for a full-scale modeling project.
- 20. COLLECT AND MANAGEDATA Time consuming step Conduct initial exploration and visualization of the data Clean data: repair data errors and transform variables as needed
- 21. BUILD THE MODEL Mostcommon data science modeling tasks are Classification Scoring Ranking Clustering Finding relations Characterization
- 22. EVALUATE AND CRITIQUEMODEL Once you have a model, you need to determine if it meets your goals : Is it accurate enough for your needs ? Does it perform better than the obvious guess ? Do the results of the model make sense in the context of the problem domain ?
- 23. PRESENT RESULTS ANDDOCUMENT Present results to your project sponser and other stakeholders. Document the model for those in the organization who are responsible for using running and maintaining the model once it has been deployed.
- 24. DEPLOY MODEL Makesure that the model can be updated as its environment changes. The model initially be deployed in a small pilot program.
- 26. Several ways ofgathering data for analysis are : CSV FILE FLAT FILE(tab, space or any other separator) TEXT FILE(In a single file- reading data all at once) or (reading data line by line) ZIP FILE APIs(JSON) MULTIPLE TEXT FILE(data is split over multiple text files) DOWNLOAD FILE FROM INTERNET(file hosted on a server) WEBPAGE(scraping) RDBMS(SQL tables)
- 28. Relational databaseuses tables which are called Records Establish connections among records by using primary key and foreign key Allows users to establish defined relationships between tables In RDBMS, we use SQL instructions to reproduce and analyze data separately
- 30. SOME COMMONLY USEDPLOTS FOR EDA ARE : Histogram Scatter plots Maps Feature corelation plot(Heatmap) Time series plots
- 32. Data management platformsenables organizations and enterprises to use data analytics in beneficial ways, such as : Personalizing the customer experience Adding value to customer interactions Improving customer engagement Increasing customer loyalty Reaping and revenues associated with data driven marketing Identifying the root causes of marketing failures and business issues in real time