Through a series of hands-on exercises, students will learn to turn data into actionable information. The world is drowning in data. Each day 2.5 Exabytes of data (250 new Library of Congresses built or 90 years of HD video) is produced. The problem is getting the data into a format which can be used by tools that help in understanding and verifying the data. Python programming is relatively quick to learn and has a great set of tools for importing, transforming, exploring, extracting insights from, making predictions with, and exporting the data.
This course introduces the major Python tools used for preparing the data for analysis, the tools available for understanding the data, and using the data for insights and predictions.
Learning Objectives
- Introduce statistical tools for working with data sets
- Introduce machine learning tools for working with data sets
- Work through a complete data analysis to understand how the tools interact with each other
- Pandas essentials for data analysis
- Pandas essentials for data visualization
- Seaborn essentials for data visualization
- Getting Data in Python
- Cleaning data in Python
- Preparing the Data
- Analyze the data
- Analyzing time-series data
- Making predictions with linear regression model
- Making predictions with multiple regression model
Who Should Attend
Anyone wanting to use Python as part of their data analysis program.
Course Outline
Introduction to Python
- Introduction to python
- Environment setup & start programming
- Python Conditional Statements, Loops and File Handling
- Core Objects and Advanced Data Structures; Functions and Lambdas
- The Object Oriented Side of it
Module 1: Introduction to Data Analysis with Python
- What is data analysis?
- Overview of Python and its data analysis libraries (NumPy, pandas, Matplotlib, Seaborn)
- Setting up your Python environment
Module 2: Data Preprocessing and Cleaning
- Importing data from various sources (CSV, Excel, SQL)
- Exploring and understanding the dataset
- Handling missing data: imputation techniques
- Dealing with outliers and anomalies
- Data transformation: normalization, standardization
- Data integration and manipulation using pandas
Module 3: Exploratory Data Analysis (EDA)
- Descriptive statistics: mean, median, mode, variance, etc.
- Histograms, box plots, scatter plots
- Correlation analysis and heatmaps
- Univariate and bivariate analysis
- Data visualization using Matplotlib and Seaborn
Module 4: Data Visualization
- Advanced data visualization techniques: bar plots, line plots, pie charts, etc.
- Interactive visualizations using Plotly
- Geospatial visualization
- Effective data storytelling and communication
Module 5: Statistical Analysis
- Sampling techniques and the Central Limit Theorem
- Hypothesis testing: t-tests, chi-square tests, ANOVA
- Confidence intervals and p-values
- Interpreting statistical results
Module 6: Machine Learning algorithms for Data Analysis
- Introduction to machine learning
- Feature engineering and selection
- Linear regression: simple and multiple regression
- Logistic regression for classification
- Decision trees and random forests
- Model evaluation metrics: R-squared, MAE, RMSE, accuracy, precision, recall, F1-score
- Model assumptions and diagnostics
Module 7: Time Series Analysis
- Introduction to time series data
- Time series components: trend, seasonality, noise
- Decomposition techniques
- Time series forecasting methods: moving average, ARIMA, exponential smoothing
- Implementing time series analysis in Python
Module 8: Advanced Topics
- Dimensionality reduction techniques (PCA, t-SNE)
- Clustering algorithms (K-means, hierarchical clustering)
- Advanced statistical techniques (non-parametric tests, ANCOVA)
Module 9: Real-World Projects and Case Studies
- Applying data analysis concepts to real datasets
- Solving data analysis challenges and problems
- Creating a portfolio of data analysis projects
Duration: 2 Months
Price: 300,000