Python Programming|System Source

Python has been around for decades, but it's still one of the most versatile and popular programming languages out there. Whether you're relatively new to programming or have been developing software for years, Python is an excellent language to add to your skill set. In this course, you'll learn the fundamentals of programming in Python, and you'll develop applications to demonstrate your grasp of the language.

Set up Python and develop a simple application.
Declare and perform operations on simple data types, including strings, numbers, and dates.
Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets.
Write conditional statements and loops.
Define and use functions, classes, and modules.
Manage files and directories through code.
Deal with exceptions.

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

Python Programming

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