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About
What is Data Science? | Completely RoadMap |
Introduction to Data Science: Exploring Real-World Problems (Focusing on Data Manipulation and Visualization)
Data Manipulation and Visualization
Pandas
Introduction to Pandas: A powerful data manipulation library for Python.
Key features:
DataFrames: 2-dimensional labeled data structure with columns of potentially different types.
Series: One-dimensional labeled array capable of holding any data type.
Basic Operations:
Reading and writing data.
Filtering and slicing data.
Merging, joining, and concatenating data.
Grouping and summarizing data.
Matplotlib
Introduction to Matplotlib: A plotting library for Python.
Key features:
Static, animated, and interactive plots.
Various plot types: line, scatter, bar, histogram, etc.
Basic Operations:
Creating basic plots.
Customizing plots (labels, titles, legends, etc.).
Subplots and multi-figure layouts.
Advanced Topics
Specialization in Big Data:
Handling and processing massive datasets.
Distributed computing: Apache Hadoop, Spark, etc.
Algorithms:
Machine learning algorithms for regression, classification, clustering, etc.
Optimization techniques for large-scale data.
Business:
Quantitative analysis: statistical methods, risk management, etc.
Decision-making: data-driven insights, resourcing, etc.
SQL and Database Management
Data Storage and Handling
Introduction to SQL (Structured Query Language): A language for interacting with relational databases.
Key features:
Defining data structures.
Inserting, querying, updating, and deleting data.
Basic Operations:
Creating and managing databases and tables.
Writing, reading, and modifying data using SELECT, INSERT, UPDATE, DELETE statements.
Projects and Portfolio Building
Kaggle: A platform for predictive modelling and analytics competitions.
GitHub: A platform for hosting and sharing code, documentation, and projects.
Introduction to Data Science: Exploring Real-World Problems (Cont.)
Foundational Mathematics
Statistics: Descriptive and inferential statistical methods.
Probability: Random events, distributions, and expectations.
Calculus: Limits, derivatives, and integrals, with applications to mathematical modeling and optimization.
Machine Learning
Overview: A set of algorithms that learn from data and make predictions or decisions.
Types:
Supervised Learning: Labeled training data.
Unsupervised Learning: Unlabelled training data.
Introduction to Data Science: Exploring Real-World Problems (Focusing on Data Manipulation and Visualization)
Data Manipulation and Visualization
Pandas
Introduction to Pandas: A powerful data manipulation library for Python.
Key features:
DataFrames: 2-dimensional labeled data structure with columns of potentially different types.
Series: One-dimensional labeled array capable of holding any data type.
Basic Operations:
Reading and writing data.
Filtering and slicing data.
Merging, joining, and concatenating data.
Grouping and summarizing data.
Matplotlib
Introduction to Matplotlib: A plotting library for Python.
Key features:
Static, animated, and interactive plots.
Various plot types: line, scatter, bar, histogram, etc.
Basic Operations:
Creating basic plots.
Customizing plots (labels, titles, legends, etc.).
Subplots and multi-figure layouts.
Advanced Topics
Specialization in Big Data:
Handling and processing massive datasets.
Distributed computing: Apache Hadoop, Spark, etc.
Algorithms:
Machine learning algorithms for regression, classification, clustering, etc.
Optimization techniques for large-scale data.
Business:
Quantitative analysis: statistical methods, risk management, etc.
Decision-making: data-driven insights, resourcing, etc.
SQL and Database Management
Data Storage and Handling
Introduction to SQL (Structured Query Language): A language for interacting with relational databases.
Key features:
Defining data structures.
Inserting, querying, updating, and deleting data.
Basic Operations:
Creating and managing databases and tables.
Writing, reading, and modifying data using SELECT, INSERT, UPDATE, DELETE statements.
Projects and Portfolio Building
Kaggle: A platform for predictive modelling and analytics competitions.
GitHub: A platform for hosting and sharing code, documentation, and projects.
Introduction to Data Science: Exploring Real-World Problems (Cont.)
Foundational Mathematics
Statistics: Descriptive and inferential statistical methods.
Probability: Random events, distributions, and expectations.
Calculus: Limits, derivatives, and integrals, with applications to mathematical modeling and optimization.
Machine Learning
Overview: A set of algorithms that learn from data and make predictions or decisions.
Types:
Supervised Learning: Labeled training data.
Unsupervised Learning: Unlabelled training data.
What is Data Science? | Completely RoadMap | Introduction to Data Science: Exploring Real-World Problems (Focusing on Data Manipulation and Visualization) Data Manipulation and Visualization Pandas Introduction to Pandas: A powerful data manipulation library for Python. Key features: DataFrames: 2-dimensional...
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