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
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How to install python

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