Data Science Tools and Techniques
( 25 Modules )

Module #1
Introduction to Data Science
Overview of data science, its importance, and applications

Module #2
Data Types and Formats
Understanding different data types, formats, and sources

Module #3
Data Preprocessing
Cleaning, transforming, and preparing data for analysis

Module #4
Python for Data Science
Introduction to Python programming for data science tasks

Module #5
NumPy and Pandas
Using NumPy and Pandas for data manipulation and analysis

Module #6
Data Visualization with Matplotlib and Seaborn
Visualizing data using Matplotlib and Seaborn libraries

Module #7
Exploratory Data Analysis (EDA)
Techniques for exploring and understanding datasets

Module #8
Statistical Inference and Hypothesis Testing
Understanding statistical concepts and hypothesis testing

Module #9
Supervised Learning Fundamentals
Introduction to supervised learning, regression, and classification

Module #10
Scikit-learn for Supervised Learning
Using scikit-learn for supervised learning tasks

Module #11
Unsupervised Learning Fundamentals
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #12
Scikit-learn for Unsupervised Learning
Using scikit-learn for unsupervised learning tasks

Module #13
Text Analysis and Natural Language Processing (NLP)
Introduction to text analysis, NLP, and language models

Module #14
Working with Big Data and NoSQL Databases
Handling large datasets and using NoSQL databases

Module #15
Data Warehousing and ETL
Designing and implementing data warehouses and ETL processes

Module #16
Machine Learning Model Evaluation and Selection
Evaluating and selecting machine learning models

Module #17
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and TensorFlow

Module #18
Cloud Computing and Data Science
Using cloud platforms for data science tasks and big data analytics

Module #19
Data Storytelling and Communication
Effectively communicating insights and results to stakeholders

Module #20
Ethics in Data Science
Understanding ethical considerations in data science

Module #21
Data Science Project Development
Guided development of a data science project

Module #22
Advanced Data Visualization
Advanced data visualization techniques using Tableau and Power BI

Module #23
Advanced Machine Learning Topics
Advanced machine learning topics, including ensemble methods and model interpretation

Module #24
Specialized Data Science Tools
Introduction to specialized data science tools, including OpenCV and spaCy

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Science Tools and Techniques career

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