DataManipulationTimeSeriesHandling asdfadsfas

DataManipulationTimeSeriesHandling.pptx

1. Data Manipulation 5/20/24 by Madhu N

2. Data Manipulation What is Data Manipulation? P rocess of organizing and structuring data to make it readable and useful. What is Data Cleaning and Standardization? Process of identifying incorrect, irrelevant, and incomplete data and then replacing or modifying the data appropriately Process of transforming data (available in different formats) to a standard format as defined for business or product. Standardized data follows a certain format and rules for consistency. 5/20/24 by Madhu N

3. Data Manipulation Basic Data Manipulation Techniques Sorting Sorting involves arranging data based on specific criteria, such as alphabetical order or numerical value. For instance, sorting customer names alphabetically facilitates quick access to desired information. Filtering Filtering allows for the extraction of relevant data based on specified conditions. It enables businesses to focus on specific subsets of data for analysis. For example, filtering out low-performing products based on sales volume can help companies identify areas for improvement. Aggregation Aggregating data involves combining and summarizing information to derive insights. Businesses can calculate metrics such as sum, average, or count to understand patterns and trends within their datasets.   5/20/24 by Madhu N

4. Data Manipulation Contd., Advanced Data Manipulation Techniques Joins Joins involve merging datasets based on common attributes to perform comprehensive analysis. For instance, merging customer data with sales data allows businesses to analyze customer behavior and purchasing patterns. Pivoting Pivoting refers to restructuring a dataset by transforming rows into columns or vice versa. This technique is useful when analyzing survey responses or comparing categorical variables across different dimensions. String Manipulation String manipulation techniques allow for the modification of text values within datasets using functions and operators. It is commonly used when dealing with unstructured data, such as social media posts or customer reviews.   5/20/24 by Madhu N

5. Data Cleaning What is Data Cleaning? Process of identifying incorrect, irrelevant, and incomplete data and then replacing or modifying the data appropriately. Only when you cleanse your data, you can use it to gain insights that help your company to make better decisions. Common Data Quality Issues: Missing Values Duplicates Inconsistent Formatting 5/20/24 by Madhu N

6. Handling Missing Values and Duplicates Identifying Missing Values Techniques to Deal with Missing Data: Dropping Rows or Columns Imputation (e.g., Mean, Median, Mode) Interpolation Detecting and Removing Duplicate Rows 5/20/24 by Madhu N

7. Standardizing Data Format Importance of Standardization Data consistency, Ease to process, Supports easy data exchange Techniques for Standardization: Changing Data Types Formatting Text Data Converting Dates and Times 5/20/24 by Madhu N

8. Time Series Handling 5/20/24 by Madhu N

9. Overview of Time Series Data ? What is Time Series Data? A time series is a data-set that tracks a sample over time. Sequence of data points that occur in successive order over some period of time Importance of Time Series Analysis Helps an Individual or an organization to understand the underlying causes of trends or systemic patterns over time Benefits of Using Python for Time Series Analysis 1 Rich Libraries, 2 Easy Visualization, 3 Versatile Ecosystem, 4 Community Support, 5 Flexibility in Coding, 6 Integration Capabilities Popular Libraries for Time Series Handling: Pandas, NumPy, datetime, dateutil Matplotlib, Seaborn 5/20/24 by Madhu N

10. Any Questions? 5/20/24 by Madhu N