# Tag: matplotlib

### Comprehensive learning path – Data Science in Python

Curricula / References, Learning Guides, Etc.From Analytics Vidhya: “So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. … Continue Reading

### Python Data Science Handbook: Essential Tools for Working with Data

Books / physical books or multiple formatsFrom Amazon: “For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this … Continue Reading

### DataCamp – Introduction to Data Visualization with Python

Courses / Interactive tutorial style courseFrom DataCamp: “This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. The course provides a broader coverage of the Matplotlib library and an overview of Seaborn (a package for statistical graphics). Topics covered include customizing graphics, plotting two-dimensional arrays (e.g. pseudocolor plots, contour plots, images, etcl), statistical graphics (e.g., visualizing distriubtions & regressions), and working with time series and image data.” This … Continue Reading

### DataCamp – Intermediate Python for Data Science

Courses / Interactive tutorial style courseFrom DataCamp: “The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib’s functions and get to know new data structures such as the dictionary and the Pandas DataFrame. After covering key concepts such as boolean logic, control flow and loops in Python, you’re ready to blend together everything you’ve learned to solve a case study using hacker statistics.” This course consists of 5 … Continue Reading

### Coursera Applied Data Science with Python Specialization

Courses / Scheduled online courseFrom Coursera: 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 basic a 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.” The … Continue Reading

### Applied Plotting, Charting and Data Representation in Python

Courses / Scheduled online courseThis course is #2 of 5 in the Applied Data Science with Python Specialization from the University of Michigan. From Coursera: “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 … Continue Reading

### Metis Data Science Bootcamp

In-Person Bootcamp / ProgramsLearn Data Science in 12 weeks with 100% in-person instruction from expert data scientists.

### Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Books / physical books or multiple formatsThis is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems.

### Data Science from Scratch: First Principles with Python

Books / physical books or multiple formatsIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

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