## Trending Skill : Python Curated Course Collection

*in*Learn something new everyday

In Data Scientist’s daily duties, python programming plays a crucial role in combining statistical and machine learning methods for analyzing and interpreting complicated information.Python can be used for nearly all the steps engaged in data science procedures because of its versatility. It can ingest multiple information formats and can readily import SQL tables into your software and also enables datasets to be created or any sort of information set to be found on Google. In this post, we look at curated compilation of Python programming courses.

Quick Snapshot

- #1.Programming for Everybody (Getting Started with Python)
- #2.Python for Everybody
- #3.Python Basics
- #4.Python Classes and Inheritance
- #5.Python Functions, Files, and Dictionaries
- #6.Python Project: pillow, tesseract, and opencv
- #7.Introduction to Scripting in Python
- #8.Python 3 Programming
- #9.Using Databases with Python
- #10.Using Python to Access Web Data
- #11.Python Programming Essentials
- #12.Python Programming: A Concise Introduction
- #13.An Introduction to Interactive Programming in Python (Part 1)
- #14.An Introduction to Interactive Programming in Python (Part 2)
- #15.Python Data Analysis
- #16.Python Data Representations
- #17.Python Data Structures
- #18.Python Data Visualization
- #19.Introduction to Data Science in Python
- #20.Python for Data Science
- #21.Python for Genomic Data Science
- #22.Data Analysis with Python
- #23.Data Processing Using Python
- #24.Data Collection and Processing with Python
- #25.Data Visualization with Python
- #26.Understanding and Visualizing Data with Python
- #27.Statistics with Python
- #28.Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python
- #29.Fitting Statistical Models to Data with Python
- #30.Inferential Statistical Analysis with Python
- #31.Machine Learning with Python
- #32.Foundations of Data Science: K-Means Clustering in Python
- #33.Applied Data Science with Python
- #34.Applied Machine Learning in Python
- #35.Applied Plotting, Charting & Data Representation in Python
- #36.Applied Social Network Analysis in Python
- #37.Applied Text Mining in Python
- #38.Capstone: Retrieving, Processing, and Visualizing Data with Python
- #39.Python and Statistics for Financial Analysis
- #40.Python Data Products for Predictive Analytics
- #41.Using SAS Viya REST APIs with Python and R
- #42.Global Warming II: Create Your Own Models in Python
- #43.The Raspberry Pi Platform and Python Programming for the Raspberry Pi
- #Additional Resources

## #1.Programming for Everybody (Getting Started with Python)

**University of Michigan**

*This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.*

## #2.Python for Everybody

**University of Michigan**

*This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.*

## #3.Python Basics

**University of Michigan**

*This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures. You’ll program an on-screen Turtle to draw pretty pictures. You’ll also learn to draw reference diagrams as a way to reason about program executions, which will help to build up your debugging skills. The course has no prerequisites. It will cover Chapters 1-9 of the textbook “Fundamentals of Python Programming,” which is the accompanying text (optional and free) for this course. The course is for you if you’re a newcomer to Python programming, if you need a refresher on Python basics, or if you may have had some exposure to Python programming but want a more in-depth exposition and vocabulary for describing and reasoning about programs. This is the first of five courses in the Python 3 Programming Specialization.*

## #4.Python Classes and Inheritance

**University of Michigan**

*This course introduces classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways. You’ll also learn how to override built-in methods and how to create “inherited” classes that reuse functionality. You’ll also learn about how to design classes. Finally, you will be introduced to the good programming habit of writing automated tests for their own code. The course is best-suited for you if you are already familiar with Python fundamentals, which are covered in the “Python Basics” and “Python Functions, Files, and Dictionaries” courses (courses 1 and 2 of the Python 3 Programming Specialization). It is optional to have taken the “Data Collection and Processing with Python” course (course 3 of the specialization), but knowledge of retrieving and processing complex nested data is helpful. This is the fourth of five courses in the Python 3 Programming Specialization.*

## #5.Python Functions, Files, and Dictionaries

**University of Michigan**

*This course introduces the dictionary data structure and user-defined functions. You’ll learn about local and global variables, optional and keyword parameter-passing, named functions and lambda expressions. You’ll also learn about Python’s sorted function and how to control the order in which it sorts by passing in another function as an input. For your final project, you’ll read in simulated social media data from a file, compute sentiment scores, and write out. csv files. It covers chapters 10-16 of the textbook “Fundamentals of Python Programming,” which is the accompanying text (optional and free) for this course. The course is well-suited for you if you have already taken the “Python Basics” course and want to gain further fundamental knowledge of the Python language. Together, both courses are geared towards newcomers to Python programming, those who need a refresher on Python basics, or those who may have had some exposure to Python programming but want a more in-depth exposition and vocabulary for describing and reasoning about programs. This is a follow-up to the “Python Basics” course (course 1 of the Python 3 Programming Specialization), and it is the second of five courses in the specialization.*

## #6.Python Project: pillow, tesseract, and opencv

**University of Michigan**

*This course will walk you through a hands-on project suitable for a portfolio. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognize text (tesseract and py-tesseract), and how to identify faces in images using the popular opencv library. By the end of the course you will have worked with three different libraries available for Python 3 to create a real-world data-analysis project. The course is best-suited for learners who have taken the first four courses of the Python 3 Programming Specialization. Learners who already have Python programming skills but want to practice with a hands-on, real-world data-analysis project can also benefit from this course. This is the fifth and final course in the Python 3 Programming Specialization.*

## #7.Introduction to Scripting in Python

**Rice University**

*This specialization is intended for beginners who would like to master essential programming skills. Through four courses, you will cover key programming concepts in Python 3 which will prepare you to use Python to perform common scripting tasks. This knowledge will provide a solid foundation towards a career in data science, software engineering, or other disciplines involving programming.*

## #8.Python 3 Programming

**University of Michigan**

*This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance. You will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work. By the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them. And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer. This specialization is a good next step for you if you have completed Python for Everybody but want a more in-depth treatment of Python fundamentals and more practice, so that you can proceed with confidence to specializations like Applied Data Science with Python. But it is also appropriate as a first set of courses in Python if you are already familiar with some other programming language, or if you are up for the challenge of diving in head-first.*

## #9.Using Databases with Python

**University of Michigan**

*This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3. js library to do basic data visualization. This course will cover Chapters 14-15 of the book “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-13 of the textbook and the first three courses in this specialization. This course covers Python 3.*

## #10.Using Python to Access Web Data

**University of Michigan**

*This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.*

## #11.Python Programming Essentials

**Rice University**

*This course will introduce you to the wonderful world of Python programming! We’ll learn about the essential elements of programming and how to construct basic Python programs. We will cover expressions, variables, functions, logic, and conditionals, which are foundational concepts in computer programming. We will also teach you how to use Python modules, which enable you to benefit from the vast array of functionality that is already a part of the Python language. These concepts and skills will help you to begin to think like a computer programmer and to understand how to go about writing Python programs. By the end of the course, you will be able to write short Python programs that are able to accomplish real, practical tasks. This course is the foundation for building expertise in Python programming. As the first course in a specialization, it provides the necessary building blocks for you to succeed at learning to write more complex Python programs. This course uses Python 3. While many Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This first course will use a Python 3 version of the CodeSkulptor development environment, which is specifically designed to help beginning programmers learn quickly. CodeSkulptor runs within any modern web browser and does not require you to install any software, allowing you to start writing and running small programs immediately. In the later courses in this specialization, we will help you to move to more sophisticated desktop development environments.*

## #12.Python Programming: A Concise Introduction

**Wesleyan University**

*The goal of the course is to introduce students to Python Version 3.x programming using hands on instruction. It will show how to install Python and use the Spyder IDE (Integrated Development Environment) for writing and debugging programs. The approach will be to present an example followed by a small exercise where the learner tries something similar to solidify a concept. At the end of each module there will be an exercise where the student is required to write simple programs and submit them for grading. It is intended for students with little or no programming background, although students with such a background should be able to move forward at their preferred pace. The course is four modules long and is designed to be completed in four weeks.*

## #13.An Introduction to Interactive Programming in Python (Part 1)

**Rice University**

*This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game “Pong”.*

## #14.An Introduction to Interactive Programming in Python (Part 2)

**Rice University**

*This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse. In part 2 of this course, we will introduce more elements of programming (such as list, dictionaries, and loops) and then use these elements to create games such as Blackjack. Part 1 of this class will culminate in building a version of the classic arcade game “Asteroids”. Upon completing this course, you will be able to write small, but interesting Python programs. The next course in the specialization will begin to introduce a more principled approach to writing programs and solving computational problems that will allow you to write larger and more complex programs.*

## #15.Python Data Analysis

**Rice University**

*This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We’ll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python’s support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data. By the end of the course, you will be comfortable working with tabular data in Python. This will extend your Python programming expertise, enabling you to write a wider range of scripts using Python. This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course uses basic desktop Python development environments, allowing you to run Python programs directly on your computer.*

## #16.Python Data Representations

**Rice University**

*This course will continue the introduction to Python programming that started with Python Programming Essentials. We’ll learn about different data representations, including strings, lists, and tuples, that form the core of all Python programs. We will also teach you how to access files, which will allow you to store and retrieve data within your programs. These concepts and skills will help you to manipulate data and write more complex Python programs. By the end of the course, you will be able to write Python programs that can manipulate data stored in files. This will extend your Python programming expertise, enabling you to write a wide range of scripts using Python This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course introduces basic desktop Python development environments, allowing you to run Python programs directly on your computer. This choice enables a smooth transition from online development environments.*

## #17.Python Data Structures

**University of Michigan**

*This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.*

## #18.Python Data Visualization

**Rice University**

*This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs. By the end of the course, you will be comfortable installing Python packages, analyzing existing data, and generating visualizations of that data. This course will complete your education as a scripter, enabling you to locate, install, and use Python packages written by others. You will be able to effectively utilize tools and packages that are widely available to amplify your effectiveness and write useful programs.*

## #19.Introduction to Data Science in Python

**University of Michigan**

*This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.*

## #20.Python for Data Science

**IBM**

*This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Module 1 – Python Basics o Your first program o Types o Expressions and Variables o String Operations Module 2 – Python Data Structures o Lists and Tuples o Sets o Dictionaries Module 3 – Python Programming Fundamentals o Conditions and Branching o Loops o Functions o Objects and Classes Module 4 – Working with Data in Python o Reading files with open o Writing files with open o Loading data with Pandas o Numpy Finally, you will create a project to test your skills. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.*

## #21.Python for Genomic Data Science

**Johns Hopkins University**

*This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.*

## #22.Data Analysis with Python

**IBM**

*Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.*

## #23.Data Processing Using Python

**Nanjing University**

*This course (The English copy of “ç”¨PythonçŽ©è½¬æ•°æ®” ) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields. The course has been updated. Updates in the new version are: 1) the whole course has moved from Python 2.x to Python 3.x 2) Added manual webpage fetching and parsing. Web API is also added. 3) Improve the content order and enrich details of some content especially for some practice projects. Note: videos are in Chinese (Simplified) with English subtitles. All other materials are in English.*

## #24.Data Collection and Processing with Python

**University of Michigan**

*This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You’ll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site. The course is well-suited for you if you have already taken the “Python Basics” and “Python Functions, Files, and Dictionaries” courses (courses 1 and 2 of the Python 3 Programming Specialization). If you are already familiar with Python fundamentals but want practice at retrieving and processing complex nested data from Internet services, you can also benefit from this course without taking the previous two. This is the third of five courses in the Python 3 Programming Specialization.*

## #25.Data Visualization with Python

**IBM**

*A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.”*

## #26.Understanding and Visualizing Data with Python

**University of Michigan**

*In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.*

## #27.Statistics with Python

**University of Michigan**

*This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.*

## #28.Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

**Ludwig-Maximilians-UniversitÃ¤t MÃ¼nchen LMU**

*Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g, Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models. The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences, engineering, as well as economics and other fields.*

## #29.Fitting Statistical Models to Data with Python

**University of Michigan**

*In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.*

## #30.Inferential Statistical Analysis with Python

**University of Michigan**

*In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.*

## #31.Machine Learning with Python

**IBM**

*This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.*

## #32.Foundations of Data Science: K-Means Clustering in Python

**University of London**

*Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.*

## #33.Applied Data Science with Python

**University of Michigan**

*The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.*

## #34.Applied Machine Learning in Python

**University of Michigan**

*This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.*

## #35.Applied Plotting, Charting & Data Representation in Python

**University of Michigan**

*This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.*

## #36.Applied Social Network Analysis in Python

**University of Michigan**

*This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.*

## #37.Applied Text Mining in Python

**University of Michigan**

*This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.*

## #38.Capstone: Retrieving, Processing, and Visualizing Data with Python

**University of Michigan**

*In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.*

## #39.Python and Statistics for Financial Analysis

**The Hong Kong University of Science and Technology**

*Course Overview: https://youtu. be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: – Import, pre-process, save and visualize financial data into pandas Dataframe – Manipulate the existing financial data by generating new variables using multiple columns – Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts – Build a trading model using multiple linear regression model – Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.*

## #40.Python Data Products for Predictive Analytics

**University of California San Diego**

*Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills. Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.*

## #41.Using SAS Viya REST APIs with Python and R

**SAS**

*SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package – the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.*

## #42.Global Warming II: Create Your Own Models in Python

**The University of Chicago**

*This class provides a series of Python programming exercises intended to explore the use of numerical modeling in the Earth system and climate sciences. The scientific background for these models is presented in a companion class, Global Warming I: The Science and Modeling of Climate Change. This class assumes that you are new to Python programming (and this is indeed a great way to learn Python!), but that you will be able to pick up an elementary knowledge of Python syntax from another class or from on-line tutorials.*

## #43.The Raspberry Pi Platform and Python Programming for the Raspberry Pi

**University of California, Irvine**

*The Raspberry Pi is a small, affordable single-board computer that you will use to design and develop fun and practical IoT devices while learning programming and computer hardware. In addition, you will learn how to set up up the Raspberry Pi environment, get a Linux operating system running, and write and execute some basic Python code on the Raspberry Pi. You will also learn how to use Python-based IDE (integrated development environments) for the Raspberry Pi and how to trace and debug Python code on the device. Please note that this course does not include discussion forums.*

## #Additional Resources

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