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Intro to Data Science and Data Wrangling.pptx
- 1. Introduction to Data Scienceand Data Wrangling By - Vineeta Rathore
- 2. What is DataScience? Key Points: ● Study of data to derive useful insights for business decision-making. ● Combines mathematics, computer science, and domain expertise to tackle real-world challenges. ● Processes raw data to solve business problems and make predictions about future trends.
- 3. Why it Matters?(Need for Data Science) ● Crucial for organizations to extract meaningful insights from vast amounts of data. ● Drives better decision-making and problem-solving across various industries. ● Essential for navigating complexities of the modern, data-driven world. ● Helps businesses optimize operations, anticipate trends, and personalize experiences. Example questions Data Science can answer: "What do customers want?", "How can we improve our service?", "What will be the upcoming trends in sales?", "How much stock is needed for the upcoming festival?".
- 11. Hands-On with BasicData Science Operations 1. Data Exploration and Summarization: Core Libraries: Pandas, NumPy Key Operations: ➢ Loading and Inspecting Data (Operation 1) - You'll almost always start by loading a dataset (commonly from a CSV file) into a Pandas DataFrame and performing initial inspections.
- 13. ➢ Descriptive Statistics(Operation 2) -
- 14. 2. Data Cleaningand Preprocessing: Raw data is rarely clean. We have to identify and handle common data quality issues. Core Libraries: Pandas, NumPy Key Operations: ➢ Handling Missing Values (Operation 1) - This is one of the most common data cleaning tasks.
- 16. ➢ Handling Duplicates(Operation 2) - Duplicate records can skew your analysis and model training.
- 17. 3. Data Selectionand Manipulation: You'll often need to select specific subsets of your data or manipulate it to create new columns or structures. Core Libraries: Pandas Key Operations: ➢ Selecting Data with loc and iloc (Operation 1) - Understanding the difference between label-based indexing (loc) and integer-based indexing (iloc) is fundamental.
- 19. ➢ Applying Functions(Operation 2) - You can apply functions to a DataFrame to perform custom transformations.
- 20. ➢ Grouping andAggregating (Operation 3) - The groupby operation is powerful for calculating statistics on different segments of your data.
- 21. 4. Data Visualization: Visualizingyour data is key to understanding patterns and communicating your findings. Core Libraries: Matplotlib, Seaborn Key Operations: ➢ Histograms and Box Plots (Operation 1) - For understanding the distribution of a single variable.
- 23. ➢ Scatter andLine Plots (Operation 2) - For exploring the relationship between two variables.