Python Data Science Fundamentals
( 25 Modules )

Module #1
Introduction to Data Science with Python
Overview of data science, importance of Python, and setup of Python environment

Module #2
Python Basics for Data Science
Review of Python basics, data types, variables, control structures, and functions

Module #3
Working with Data in Python
Introduction to Pandas, NumPy, and data structures (lists, dictionaries, etc.)

Module #4
Data Ingestion and Cleaning
Importing data from various sources, handling missing values, and data preprocessing

Module #5
Data Visualization with Matplotlib
Introduction to Matplotlib, plotting basics, and customizing visualizations

Module #6
Data Visualization with Seaborn
Introduction to Seaborn, visualization best practices, and advanced visualizations

Module #7
Introduction to Statistical Analysis
Descriptive statistics, inferential statistics, and hypothesis testing

Module #8
Data Manipulation with Pandas
Data merging, joining, and reshaping with Pandas

Module #9
Data Filtering and Grouping
Filtering, grouping, and aggregating data with Pandas

Module #10
Handling Missing Data
Methods for handling missing data, including imputation and interpolation

Module #11
Data Transformation and Feature Engineering
Transforming and creating new features from existing data

Module #12
Introduction to Machine Learning
Overview of machine learning, types of learning, and supervised/unsupervised learning

Module #13
Scikit-learn Fundamentals
Introduction to Scikit-learn, loading datasets, and basic machine learning workflows

Module #14
Supervised Learning with Scikit-learn
Regression, classification, and model evaluation with Scikit-learn

Module #15
Unsupervised Learning with Scikit-learn
Clustering, dimensionality reduction, and density estimation with Scikit-learn

Module #16
Model Evaluation and Selection
Evaluating and comparing machine learning models, overfitting, and underfitting

Module #17
Hyperparameter Tuning
Grid search, random search, and Bayesian optimization for hyperparameter tuning

Module #18
Introduction to Deep Learning
Overview of deep learning, neural networks, and Keras

Module #19
Deep Learning with Keras
Building and training neural networks with Keras

Module #20
Working with Big Data in Python
Introduction to big data, Hadoop, and PySpark

Module #21
Data Science Best Practices
Code organization, testing, and collaboration in data science projects

Module #22
Data Visualization Best Practices
Effective communication of insights through visualization

Module #23
Case Study 1:Exploratory Data Analysis
Applying Python data science skills to a real-world dataset

Module #24
Case Study 2:Predictive Modeling
Applying machine learning skills to a real-world problem

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Python Data Science Fundamentals career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Python Data Science Fundamentals ( 25 Modules )

Python Data Science Fundamentals
( 25 Modules )