Advanced Data Analysis with Python
( 23 Modules )

Module #1
Introduction to Advanced Data Analysis
Overview of the course, importance of data analysis, and setting up the Python environment

Module #2
Review of Python Basics for Data Science
Refresher on Python fundamentals, data types, and libraries for data science

Module #3
Working with Pandas:Data Structures and Manipulation
In-depth exploration of Pandas data structures, indexing, and data manipulation techniques

Module #4
Data Cleaning and Preprocessing
Handling missing data, data normalization, and feature scaling

Module #5
Data Visualization with Matplotlib and Seaborn
Introduction to data visualization, creating plots, and customizing visualizations

Module #6
Exploratory Data Analysis (EDA) with Pandas and NumPy
Using Pandas and NumPy for data exploration, summarization, and statistical analysis

Module #7
Introduction to Statistical Modeling with SciPy
Overview of statistical modeling, hypothesis testing, and confidence intervals

Module #8
Regression Analysis with Scikit-learn
Simple and multiple linear regression, model evaluation, and feature selection

Module #9
Time Series Analysis with Statsmodels and Pandas
Time series decomposition, autoregressive models, and forecasting techniques

Module #10
Working with Advanced Data Structures:NumPy Arrays and Pandas DataFrames
Optimizing data storage and manipulation with NumPy arrays and Pandas DataFrames

Module #11
Machine Learning Fundamentals with Scikit-learn
Introduction to machine learning, supervised and unsupervised learning, and model evaluation metrics

Module #12
Classification with Scikit-learn
Logistic regression, decision trees, random forests, and support vector machines

Module #13
Clustering and Dimensionality Reduction with Scikit-learn
K-means clustering, hierarchical clustering, PCA, and t-SNE

Module #14
Working with Big Data:Introducing Dask and Spark
Scaling data analysis with Dask and Spark, parallel processing, and distributed computing

Module #15
Advanced Data Visualization with Plotly and Bokeh
Interactive visualizations, dashboards, and animations with Plotly and Bokeh

Module #16
Geospatial Data Analysis with Geopandas and Folium
Working with geospatial data, maps, and spatial analysis techniques

Module #17
Network Analysis with NetworkX and Gephi
Introduction to network analysis, graph theory, and visualization techniques

Module #18
Advanced Topics in Data Analysis:Text Analysis and Recommendation Systems
Text analysis with NLTK and spaCy, and building recommendation systems

Module #19
Case Studies in Advanced Data Analysis
Real-world applications of advanced data analysis techniques, including data storytelling and communication

Module #20
Best Practices and Tools for Data Analysis
Version control with Git, data pipelines, and collaboration tools for data science teams

Module #21
Advanced Data Analysis with Python:Project Development
Guided project development, peer review, and feedback

Module #22
Advanced Data Analysis with Python:Project Presentations
Final project presentations, feedback, and course wrap-up

Module #23
Course Wrap-Up & Conclusion
Planning next steps in Advanced Data Analysis with Python career

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Advanced Data Analysis with Python ( 23 Modules )

Advanced Data Analysis with Python
( 23 Modules )