Data-Analysis-and-Visualization-in-Python-1.pptx

Data-Analysis-and-Visualization-in-Python-1.pptx

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  Data Analysis & Visualizationin Python This presentation will guide you through the fundamentals and advanced techniques of data analysis and visualization using Python, empowering you to transform data into insightful stories. by Dr. Sitanath Biswas
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  Overview of Pythonfor Data Analysis Open-source Python is free to use and modify, fostering collaboration and innovation. Extensive Libraries A rich ecosystem of libraries for data analysis, visualization, and machine learning. Beginner-friendly Python's syntax is relatively simple, making it accessible for beginners.
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  Setting Up theEnvironment Install Python Download and install the latest version of Python from the official website. Install Libraries Use the pip package manager to install essential libraries like Pandas, NumPy, and Matplotlib. Integrated Development Environment (IDE) Choose a suitable IDE like Jupyter Notebook, VS Code, or PyCharm for a smooth coding experience.
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  Introduction to Pandas 1Data Structures Pandas provides powerful data structures like Series (1D) and DataFrames (2D) for efficient data manipulation. 2 Data Loading Easily load data from various sources like CSV, Excel, and SQL databases. 3 Data Selection and Filtering Access and filter data using intuitive methods for targeted analysis.
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  Data Cleaning and Preprocessing HandlingMissing Data Identify and address missing values using techniques like imputation or removal. Data Transformation Convert data types, standardize units, and apply necessary transformations. Removing Duplicates Identify and eliminate redundant entries to ensure data integrity.
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  Exploratory Data Analysis (EDA) 1Descriptive Statistics Calculate summary statistics like mean, median, and standard deviation to understand data distributions. 2 Data Visualization Create various charts and plots to identify trends, patterns, and relationships in the data. 3 Hypothesis Testing Formulate and test hypotheses based on the observed data patterns.
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  Data Visualization Basics BarCharts Ideal for comparing categorical data. Line Charts Illustrate trends and patterns over time. Scatter Plots Show the relationship between two numerical variables. Histograms Visualize the distribution of a single numerical variable.
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  Advanced Visualization withMatplotlib 1 Customization Fine-tune plot elements, labels, colors, and styles. 2 Interactive Plots Create interactive plots that respond to user input. 3 3D Plots Visualize data in three dimensions for a deeper understanding.
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  Seaborn for StatisticalPlots 1 Statistical Relationships Create plots that highlight statistical relationships and distributions. 2 Aesthetically Pleasing Seaborn provides high-level functions for visually appealing plots. 3 Seaborn Gallery Explore the Seaborn gallery for inspiration and discover a wide range of plot types.
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  Working with TimeSeries Data 1 Data Preparation Load, clean, and transform time series data into a suitable format. 2 Trend Analysis Identify patterns and trends in the data over time. 3 Forecasting Use statistical models to predict future values based on historical data.