Dear learners, Python module for #Data Science is completed. Shortly we will be starting with #Statistic Modules where we will be covering #Descriptive Statistics and #Inferential Statistics.

*Topic* : Data Visualization Extra Innings *Module* : #Python #DataVisualization 1.Marker styles 2.Color styles 3.Line styles *Further Reading* : https://t.co/8BikHGDYBi #DataScienceLearnersHub #DataScience #Python

*Topic* : Geographic Data Visualization with Folium *Module* : #Python #DataVisualization Folium is a powerful Python library for creating interactive maps. *Further Reading* :

*Topic* : Plotly for Interactive Visualization *Module* : #Python #DataVisualization *Significance of Plotly:* Plotly is a powerful Python library that enables the creation of interactive and visually appealing visualizations. *Further Reading* :

*Topic* : #Seaborn for #Statistical Data Visualization *Module* : #Python #DataVisualization Seaborn is a Python data visualization library based on #Matplotlib, specifically designed for statistical data visualization. *Further Reading* :

*Topic* : Advanced Matplotlib Techniques *Module* : #Python #DataVisualization 1. Bar Charts and Histograms 2. Subplots and Layouts 3. Advanced Plot Customization *Further Reading* : https://t.co/4qHiBBmttm #DataScienceLearnersHub #DataScience #Python

*Topic* : Intro to Data Visualisation *Module* : #Python Data visualization plays a crucial role in the field of data analysis, serving as a powerful tool to make sense of complex datasets and communicate insights effectively. *Further Reading* :

*Topic* : Best Practices and Tips *Module* : #Python #Pandas Efficiency Tips and Handling Large Data Sets *Further Reading* : https://t.co/5K1etMsYQq #DataScienceLearnersHub #DataScience #Python

*Topic* : Case Studies *Module* : #Python #Pandas Case Study 1: Financial Data Analysis Case Study 2: Healthcare Data Cleaning Case Study 3: E-commerce Sales Analysis Case Study 4: Social Media Engagement Metrics *Further Reading* :

*Topic* : #Statistical #Visualization *Module* : #Python #Pandas #Boxplots, #histograms, and #density #plots are powerful tools for visualizing the statistical properties of datasets. *Further Reading* : https://t.co/TFsJwjiUb7 #DataScienceLearnersHub #DataScience

*Topic* : Plotting with #Pandas *Module* : #Python ⁃Pandas provides convenient functions for basic data visualization, allowing users to create line, bar, and scatter plots directly from DataFrames. *Further Reading* : https://t.co/rKYXqFfHNH #DataScienceLearnersHub #Data

*Topic* : #TimeSeries #Data *Module* : #Python #Pandas *Importance of Handling Time and Date Data:* Handling time and date data is crucial in data analysis *Further Reading* : https://t.co/OhyWhdLgXE #DataScienceLearnersHub #DataScience #Python

*Topic Of the Day* *Topic* : Data Reshaping *Module* : #Python #Pandas 1. Pivoting and Melting in Pandas for #Data #Reshaping 2. Stacking and Unstacking Concepts: *Further Reading* : https://t.co/qIhWXz3tIX #DataScienceLearnersHub #DataScience

*Topic* : Merging and #Concatenating #DataFrames *Module* : #Python Combining or #merging DataFrames in #Pandas involves bringing together info from two or more DataFrames based on a common key or index. *Further Reading* : https://t.co/QD9GibVXfu #DataScienceLearnersHub

*Topic* : Grouping and Aggregation *Module* : #Python ⁃The GroupBy op in Pandas involves splitting a DataFrame into groups based on 1 or more criteria, applying a function to each group independently , & combining the results. *Further Reading* :

*Topic* : Data Sorting and Ranking *Module* : #Python #Pandas ⁃Sorting in Pandas involves arranging the rows of a DataFrame based on the values in one or more columns. *Further Reading* : https://t.co/kk3BL4n2Tb #DataScienceLearnersHub #DataScience #Python

*Topic* : Data Filtering and Selection Pandas allows for efficient conditional selection of data in a DataFrame using boolean expressions. *Module* : #Python #Pandas *Further Reading* :

*Topic* : Pandas #Handson *Module* : #Python #Pandas *Further Reading* : https://t.co/TZ7fxSpTsn #DataScienceLearnersHub #DataScience

*Topic* : Removing Duplicates in a DataFrame *Module* : #Python #Pandas *Significance of Identifying and Removing Duplicate Rows:* a. Data Accuracy b. Consistent Results c. Efficient Memory Usage d. Meaningful Insights *Further Reading* :

*Topic* : Dropping or Filling Missing Values *Module* : #Python #Pandas •Dropping missing values is suitable when the missingness is random *Further Reading* : https://t.co/jmp3h2uH8d #DataScienceLearnersHub #DataScience