Practical Data Science with Python|System Source

Practical Data Science with Python course helps you understand Data Science and its techniques step by step using Python.

This course is designed for helping you to grasp Data Science in the easiest way possible with a lot of examples. Instead of tough math formulas, this course contains several graphs and images which detail all important Data Science concepts and their applications.

This course takes a different approach that is based on providing simple examples of how each Data Science technique work, and building on those examples step by step to encompass Data Science.

Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors and logistic regression

Viewing outline for:

Introduction to Data Science
1. The Ascendance of Data
2. What Is Data Science?
3. Motivating Hypothetical: DataSciencester
Python Basics
1. The Basics
2. The Not-So-Basics
Visualizing Data
1. matplotlib
2. Bar Charts
3. Line Charts
4. Scatterplots
Linear Algebra
1. Vectors
2. Matrices
Statistics
1. Describing a Single Set of Data
2. Correlation
3. Simpson’s Paradox
4. Some Other Correlational Caveats
5. Correlation and Causation
Probability
1. Dependence and Independence
2. Conditional Probability
3. Bayes’s Theorem
4. Random Variables
5. Continuous Distributions
6. The Normal Distribution
7. The Central Limit Theorem
8. For Further Exploration
Machine Learning
1. Modeling
2. What Is Machine Learning?
3. Overfitting and Underfitting
4. Correctness
5. The Bias-Variance Trade-off
6. Feature Extraction and Selection
k-Nearest Neighbors
1. The Model
2. Example: Favorite Languages
3. The Curse of Dimensionality
Simple Linear Regression
1. The Model
2. Using Gradient Descent
3. Maximum Likelihood Estimation
Multiple Regression
1. The Model
2. Further Assumptions of the Least Squares Model
3. Fitting the Model
4. Interpreting the Model
5. Goodness of Fit
6. Digression: The Bootstrap
7. Standard Errors of Regression Coefficients
8. Regularization
Logistic Regression
1. The Problem
2. The Logistic Function
3. Applying the Model
4. Goodness of Fit
5. Support Vector Machines

Required

Little programming experience required

Recommended

EDX11011: Data Science Essentials - DAT203.1x
EDX11033: Introduction to Python for Data Science - DAT208x
EDX11065: Introduction to Data Science - DAT101x
Python Programming

Beginers who want to approach Data Science, but are too afraid of complex math to start
Newbies in computer science techniques and data science
Professionals in Data Science and Social Sciences
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on Data Science

Practical Data Science with Python

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