Data Science Fundamentals and Practices.pptx

Data Science Fundamentals and Practices

1. Data Science Fundamentals: Data Collection, Cleaning, and Visualization An Introduction to Key Concepts

2. Agenda - Overview of Topics - Data Collection - Data Cleaning - Data Visualization - Practical Exercises - Q&A

3. Agenda (Continued) - In-depth exploration of each topic - Hands-on exercises to solidify learning - Opportunity to ask questions at the end

4. Introduction to Data Science - Data Science is the interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. - It involves data collection, cleaning, analysis, and visualization.

5. Importance of Data Collection - Data Collection is the foundation of Data Science. - Without accurate and relevant data, all subsequent analyses and visualizations are meaningless.

6. Importance of Data Cleaning and Visualization - Data Cleaning ensures the data's quality and consistency, making it ready for analysis. - Data Visualization transforms data into a visual context, such as a graph or map, to make data easier to understand.

7. Data Collection Overview - Data Collection is the process of gathering and measuring information on variables of interest. - It is a critical step in data science, setting the stage for data analysis.

8. Types of Data: Structured vs. Unstructured - Structured Data: Organized in a fixed format (e.g., databases, spreadsheets). - Unstructured Data: Not organized in a predefined manner (e.g., text files, images).

9. Types of Data: Qualitative vs. Quantitative - Qualitative Data: Descriptive and conceptual (e.g., interviews, surveys). - Quantitative Data: Numeric and can be measured (e.g., statistics, counts).

10. Sources of Data: Databases - Centralized collections of structured data, easily queryable using SQL.

11. Sources of Data: APIs - Application Programming Interfaces (APIs) allow for automated data retrieval from online services.

12. Sources of Data: Web Scraping and Sensors - Web Scraping: Extracting data from websites using automated scripts. - Sensors and IoT: Collecting data from physical devices like temperature sensors, smart devices.

13. Tools and Techniques for Data Collection: Python Libraries - requests: For making HTTP requests to fetch data from the web. - BeautifulSoup: For parsing HTML and XML documents. - pandas: For data manipulation and analysis.

14. Using APIs for Data Collection - APIs provide a way to access large amounts of data in a structured and efficient manner. - Example: Fetching weather data from an API.

15. Brief Demo/Example of Data Collection - Demonstrate a simple API call or web scraping example using Python.

16. Why Data Cleaning is Essential - Ensures data quality, making it ready for analysis. - Increases accuracy, consistency, and reliability of the data.

17. Overview of Common Data Issues - Missing Data: Missing values in the dataset. - Duplicates: Repeated entries in the dataset. - Inconsistencies: Irregular data formats or misaligned data.

18. Importance of Data Cleaning - Poor quality data can lead to incorrect conclusions. - Cleaning helps in transforming raw data into a usable format.

19. Data Cleaning Techniques Introduction - Introduction to techniques such as handling missing values, removing duplicates, and correcting inconsistencies.

20. Handling Missing Values - Methods: Imputation, Removal, or Substitution.

21. Removing Duplicates - Identifying and eliminating duplicate records to maintain data integrity.

22. Correcting Inconsistencies - Standardizing data formats and correcting any inconsistencies in data entry.

23. Standardizing Data Formats - Ensuring all data follows a consistent format, e.g., date formats, string cases.

24. Hands-On Data Cleaning Practical Example - Open a sample dataset in Excel. - Identify issues such as missing values, duplicates, and inconsistent formats.

25. Step-by-Step Walk-Through - Step 1: Handling missing data. - Step 2: Removing duplicates. - Step 3: Standardizing formats.

26. Cleaning Data in Excel - Practical demo or screenshots showing how to clean data in Excel.

27. Final Cleaned Dataset - Compare before and after cleaning. - Highlight the improvements and ready-to-analyze data.

28. Introduction to Data Visualization - Helps in understanding complex data. - Makes patterns and trends more apparent.

29. Benefits of Data Visualization - Easier communication of insights. - Supports data-driven decision-making.

30. Visualization Overview - Visualization is key to conveying findings in an understandable way.

31. The Need for Effective Visualizations - Poor visualizations can mislead; effective ones clarify and inform.

32. Types of Data Visualizations: Bar Charts and Histograms - Bar Charts: Used for comparing categories. - Histograms: Used for showing distributions of data.

33. Types of Data Visualizations: Pie Charts and Scatter Plots - Pie Charts: Represent parts of a whole. - Scatter Plots: Show relationships between two variables.

34. Tools for Data Visualization: Excel/Google Sheets - Built-in charting tools for quick visualizations.

35. Python Libraries for Visualization - matplotlib: Basic plotting library. - seaborn: Statistical data visualization. - plotly: Interactive visualizations.

36. Step-by-Step Guide to Creating Visualizations - Excel/Google Sheets: Simple chart creation. - Python: Example code for creating a bar chart or scatter plot.

37. Using Python for Visualization - Code examples showing how to create different visualizations.

38. Visualization of a Sample Dataset - Example: Create a bar chart from a dataset. - Walkthrough of the process and interpretation of the results.

39. Practical Exercise: Instructions - Collect a small dataset. - Clean the data using techniques covered. - Create at least two visualizations.

40. Time Allocation - Allocate 30 minutes for the exercise. - Encourage presenting findings after the exercise.

41. Q&A - Open the floor for any questions. - Clarify any doubts related to the lecture content.

42. Summary: Recap of Key Concepts - Data Collection: Fundamental to acquiring relevant data for analysis. - Data Cleaning: Ensures data quality and consistency for reliable analysis. - Data Visualization: Critical for interpreting and communicating data insights.

43. Summary: Data Collection - Importance of collecting accurate and relevant data.

44. Summary: Data Cleaning - The role of data cleaning in ensuring data integrity.

45. Summary: Data Visualization - Effective visualizations enhance understanding of data.

46. Closing Slide - Thank you for your participation and attention.