Introduction to Data Science
( 24 Modules )

Module #1
What is Data Science?
Introduction to the field of data science, its importance, and its applications.

Module #2
Types of Data
Overview of different types of data, including numerical, categorical, and text data.

Module #3
Data Visualization
Introduction to data visualization, its importance, and popular data visualization tools.

Module #4
Python for Data Science
Introduction to Python programming language, its popularity in data science, and basic syntax.

Module #5
NumPy and Pandas
Introduction to NumPy and Pandas libraries in Python, and their applications in data science.

Module #6
Data Preprocessing
Introduction to data preprocessing, including data cleaning, handling missing values, and data normalization.

Module #7
Introduction to Statistics
Overview of descriptive statistics, inferential statistics, and probability theory.

Module #8
Data Visualization with Matplotlib and Seaborn
Using Matplotlib and Seaborn libraries in Python for data visualization.

Module #9
Working with CSV and Excel Files
Importing and working with CSV and Excel files in Python using Pandas.

Module #10
Introduction to Machine Learning
Overview of machine learning, its types, and popular machine learning algorithms.

Module #11
Supervised Learning
Introduction to supervised learning, including regression and classification problems.

Module #12
Unsupervised Learning
Introduction to unsupervised learning, including clustering and dimensionality reduction.

Module #13
Model Evaluation Metrics
Introduction to model evaluation metrics, including accuracy, precision, recall, and F1 score.

Module #14
Overfitting and Underfitting
Understanding overfitting and underfitting in machine learning, and techniques to prevent them.

Module #15
Introduction to Scikit-learn
Introduction to Scikit-learn library in Python, and its applications in machine learning.

Module #16
Working with Text Data
Introduction to working with text data, including text preprocessing and text visualization.

Module #17
Introduction to Natural Language Processing
Overview of natural language processing, including text processing and sentiment analysis.

Module #18
Data Storytelling
Introduction to data storytelling, including communicating insights and results effectively.

Module #19
Big Data and NoSQL Databases
Introduction to big data and NoSQL databases, including Hadoop and MongoDB.

Module #20
Data Science Workflow
Introduction to the data science workflow, including problem definition, data collection, and deployment.

Module #21
Collaboration and Communication in Data Science
Importance of collaboration and communication in data science, including working with stakeholders.

Module #22
Data Science Tools and Technologies
Overview of popular data science tools and technologies, including Jupyter, Git, and Tableau.

Module #23
Ethics in Data Science
Introduction to ethics in data science, including bias, privacy, and accountability.

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Data Science career

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Introduction to Data Science ( 24 Modules )

Introduction to Data Science
( 24 Modules )