Course Content
Introduction and Course Outline
Module 1: Introduction to Python for Data Analysis (Using VS Code) 1.1. Python Basics - Overview of Python: Installing Python on your system - Use VS Code’s Python extension for syntax highlighting, debugging, and IntelliSense - Variables, Data Types: Understand basic Python types (int, float, string, list, tuple, dict, set) - Control Structures: if, else, loops (for, while) - Functions: Defining and calling functions, passing arguments - File Handling: Reading and writing text files using built-in functions 1.2. Setting Up VS Code for Python - Install VS Code and the Python extension - Explore useful extensions: Pylance, Jupyter (for notebook-like experience in VS Code) - Setting up a Python Virtual Environment to manage dependencies - Integrated terminal in VS Code for running Python code 1.3. Python Libraries for Data Analysis - Introduction to key Python libraries for data analysis: - NumPy: Install and use with VS Code for numerical computing and arrays - Pandas: Install and explore DataFrame and Series - Matplotlib and Seaborn: Install and use for plotting graphs and visualizations - SciPy: Install and use for scientific functions - Statsmodels: Install and use for statistical modeling --- Module 2: Data Manipulation and Cleaning (VS Code) 2.1. Introduction to Pandas - Creating Series and DataFrames from data (CSV, Excel, etc.) - Exploring data: `.head()`, `.tail()`, `.info()`, `.describe()` - Selecting data: Using `.loc[]` and `.iloc[]` for rows and columns 2.2. Data Cleaning Techniques - Handling Missing Data: `isnull()`, `dropna()`, `fillna()` - Data Transformation: Using `.apply()`, `.map()`, `.replace()` - Dealing with Duplicates: `.drop_duplicates()` - String Operations: Using `.str` methods to manipulate text data 2.3. Data Aggregation and Grouping - GroupBy: Grouping data based on columns and applying aggregation functions - Pivot tables and Cross-tabulations --- Module 3: Data Exploration and Visualization (VS Code) 3.1. Introduction to Data Visualization - Matplotlib: Creating basic visualizations (line plots, bar charts, histograms) - Seaborn: Enhancing visualizations with better aesthetics (box plots, pair plots, heatmaps) - Customizing plots: Titles, axis labels, legends 3.2. Exploratory Data Analysis (EDA) - Distribution Analysis: Histograms, KDEs (Kernel Density Estimation) - Correlation: Scatter plots, heatmaps to visualize correlation - Outliers Detection: Boxplots, violin plots - Multivariate Analysis: Pairplot, correlation matrix --- Module 4: Statistical Analysis (VS Code) 4.1. Descriptive Statistics - Central Tendency: Mean, median, mode - Dispersion: Variance, standard deviation - Percentiles: Calculating percentiles, quantiles 4.2. Inferential Statistics - Hypothesis Testing: t-tests, chi-square tests, ANOVA - P-values and Significance: Understanding p-values and significance level - Confidence Intervals: Calculating and interpreting confidence intervals 4.3. Probability Distributions - Normal Distribution: Using `scipy.stats.norm` - Binomial and Poisson Distributions 4.4. Linear Regression - Simple Linear Regression: Using `statsmodels` or `sklearn` - Evaluating Regression Models: R-squared, RMSE, residual analysis --- Module 5: Advanced Data Analysis (VS Code) 5.1. Time Series Analysis - Time Series Data: Handling DateTime objects in Pandas - Time Series Decomposition: Identifying trend, seasonality, and residuals - ARIMA: Using `statsmodels` to build ARIMA models 5.2. Machine Learning Basics (VS Code) - Supervised Learning: Implementing linear regression, decision trees, and KNN models using scikit-learn in VS Code - Evaluating Models: Accuracy, precision, recall, confusion matrix - Unsupervised Learning: K-means clustering 5.3. Model Deployment - Flask/FastAPI: Build a simple web API to deploy models created in VS Code - Saving Models: Using `joblib` or `pickle` to serialize models for future use - Building a Web Interface: Displaying predictions through web interfaces using Flask or FastAPI --- Module 6: Real-world Data Analysis Projects (VS Code) 6.1. Project 1: Analyzing a Sales Dataset - Objective: Clean, manipulate, and visualize a sales dataset - Tasks: Calculate sales statistics, identify trends, make predictions using simple models 6.2. Project 2: Predicting Housing Prices - Objective: Build a regression model to predict house prices based on features - Tasks: Data preprocessing, feature selection, model training, evaluation 6.3. Project 3: Time Series Forecasting - Objective: Forecast future stock prices or temperature data using ARIMA models - Tasks: Time series decomposition, ARIMA model fitting, prediction 6.4. Project 4: Customer Segmentation with Clustering - Objective: Use clustering algorithms to segment customers into groups - Tasks: Preprocess data, apply K-means, visualize clusters --- Module 7: Advanced Topics 7.1. Big Data with Python (VS Code) - Working with Large Datasets: Using Dask or PySpark in VS Code for parallel processing - Data Handling: Leveraging VS Code’s integration with Dask or Spark to handle large data volumes 7.2. Natural Language Processing (NLP) - Text Processing: Using libraries like `nltk` and `spaCy` for text analysis - Sentiment Analysis: Analyzing text data for sentiment or classification tasks 7.3. Deep Learning - Deep Learning: Implementing basic neural networks using TensorFlow or PyTorch in VS Code - Building Models: Train models for tasks like image or text classification With VS Code, you can have a streamlined, robust, and highly productive data analysis environment. It also allows you to easily integrate version control, run code in the integrated terminal, and organize your work efficiently.
Module 1: Introduction to Python for Data Analysis (Using VS Code)
Module 1: Introduction to Python for Data Analysis (Using VS Code) 1.1. Python Basics - Overview of Python: Installing Python on your system - Use VS Code’s Python extension for syntax highlighting, debugging, and IntelliSense - Variables, Data Types: Understand basic Python types (int, float, string, list, tuple, dict, set) - Control Structures: if, else, loops (for, while) - Functions: Defining and calling functions, passing arguments - File Handling: Reading and writing text files using built-in functions 1.2. Setting Up VS Code for Python - Install VS Code and the Python extension - Explore useful extensions: Pylance, Jupyter (for notebook-like experience in VS Code) - Setting up a Python Virtual Environment to manage dependencies - Integrated terminal in VS Code for running Python code 1.3. Python Libraries for Data Analysis - Introduction to key Python libraries for data analysis: - NumPy: Install and use with VS Code for numerical computing and arrays - Pandas: Install and explore DataFrame and Series - Matplotlib and Seaborn: Install and use for plotting graphs and visualizations - SciPy: Install and use for scientific functions - Statsmodels: Install and use for statistical modeling --- Module 2: Data Manipulation and Cleaning (VS Code) 2.1. Introduction to Pandas - Creating Series and DataFrames from data (CSV, Excel, etc.) - Exploring data: `.head()`, `.tail()`, `.info()`, `.describe()` - Selecting data: Using `.loc[]` and `.iloc[]` for rows and columns 2.2. Data Cleaning Techniques - Handling Missing Data: `isnull()`, `dropna()`, `fillna()` - Data Transformation: Using `.apply()`, `.map()`, `.replace()` - Dealing with Duplicates: `.drop_duplicates()` - String Operations: Using `.str` methods to manipulate text data 2.3. Data Aggregation and Grouping - GroupBy: Grouping data based on columns and applying aggregation functions - Pivot tables and Cross-tabulations --- Module 3: Data Exploration and Visualization (VS Code) 3.1. Introduction to Data Visualization - Matplotlib: Creating basic visualizations (line plots, bar charts, histograms) - Seaborn: Enhancing visualizations with better aesthetics (box plots, pair plots, heatmaps) - Customizing plots: Titles, axis labels, legends 3.2. Exploratory Data Analysis (EDA) - Distribution Analysis: Histograms, KDEs (Kernel Density Estimation) - Correlation: Scatter plots, heatmaps to visualize correlation - Outliers Detection: Boxplots, violin plots - Multivariate Analysis: Pairplot, correlation matrix --- Module 4: Statistical Analysis (VS Code) 4.1. Descriptive Statistics - Central Tendency: Mean, median, mode - Dispersion: Variance, standard deviation - Percentiles: Calculating percentiles, quantiles 4.2. Inferential Statistics - Hypothesis Testing: t-tests, chi-square tests, ANOVA - P-values and Significance: Understanding p-values and significance level - Confidence Intervals: Calculating and interpreting confidence intervals 4.3. Probability Distributions - Normal Distribution: Using `scipy.stats.norm` - Binomial and Poisson Distributions 4.4. Linear Regression - Simple Linear Regression: Using `statsmodels` or `sklearn` - Evaluating Regression Models: R-squared, RMSE, residual analysis --- Module 5: Advanced Data Analysis (VS Code) 5.1. Time Series Analysis - Time Series Data: Handling DateTime objects in Pandas - Time Series Decomposition: Identifying trend, seasonality, and residuals - ARIMA: Using `statsmodels` to build ARIMA models 5.2. Machine Learning Basics (VS Code) - Supervised Learning: Implementing linear regression, decision trees, and KNN models using scikit-learn in VS Code - Evaluating Models: Accuracy, precision, recall, confusion matrix - Unsupervised Learning: K-means clustering 5.3. Model Deployment - Flask/FastAPI: Build a simple web API to deploy models created in VS Code - Saving Models: Using `joblib` or `pickle` to serialize models for future use - Building a Web Interface: Displaying predictions through web interfaces using Flask or FastAPI --- Module 6: Real-world Data Analysis Projects (VS Code) 6.1. Project 1: Analyzing a Sales Dataset - Objective: Clean, manipulate, and visualize a sales dataset - Tasks: Calculate sales statistics, identify trends, make predictions using simple models 6.2. Project 2: Predicting Housing Prices - Objective: Build a regression model to predict house prices based on features - Tasks: Data preprocessing, feature selection, model training, evaluation 6.3. Project 3: Time Series Forecasting - Objective: Forecast future stock prices or temperature data using ARIMA models - Tasks: Time series decomposition, ARIMA model fitting, prediction 6.4. Project 4: Customer Segmentation with Clustering - Objective: Use clustering algorithms to segment customers into groups - Tasks: Preprocess data, apply K-means, visualize clusters --- Module 7: Advanced Topics 7.1. Big Data with Python (VS Code) - Working with Large Datasets: Using Dask or PySpark in VS Code for parallel processing - Data Handling: Leveraging VS Code’s integration with Dask or Spark to handle large data volumes 7.2. Natural Language Processing (NLP) - Text Processing: Using libraries like `nltk` and `spaCy` for text analysis - Sentiment Analysis: Analyzing text data for sentiment or classification tasks 7.3. Deep Learning - Deep Learning: Implementing basic neural networks using TensorFlow or PyTorch in VS Code - Building Models: Train models for tasks like image or text classification With VS Code, you can have a streamlined, robust, and highly productive data analysis environment. It also allows you to easily integrate version control, run code in the integrated terminal, and organize your work efficiently.
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Installing Python
10:44
Installing VS Code
09:43
Installing A Virtual Environment and Pandas Library in VSCode
08:59
Reading Data From A CSV Excel File With Python On VSCode
00:00
Module 2: Data Manipulation and Cleaning (VS Code)
Module 3: Data Exploration and Visualization (VS Code)
Module 4: Statistical Analysis (VS Code)
Module 5: Advanced Data Analysis (VS Code)
Module 6: Real-world Data Analysis Projects (VS Code)
Data Analysis With Python On VS Code
About Lesson

Learn how to set up a Python virtual environment and install the Pandas library to kickstart your journey into data analysis.

This step-by-step guide is perfect for beginners who want to create isolated environments for their Python projects and start working with the powerful Pandas library for analyzing and manipulating data.

In this video, you’ll discover:

How to create a Python virtual environment using venv.

Activating your virtual environment.

Installing the Pandas library with pip.

Best practices for managing Python environments for data analysis.

Whether you’re new to Python or just want a clean setup for your data analysis projects, this tutorial will guide you through the entire process.

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