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Intermediate Python for Data Science

Intermediate Python for Data Science is crucial for any aspiring data science practitioner learning Python. Learn to visualize real data with Matplotlib's functions and get acquainted with 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'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Lead by Filip Schouwenaars, Data Science Instructor at DataCamp

Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

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Python Data Science Toolbox (Part 1)

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Lead by [Hugo Bowne-Anderson](https://www.datacamp.com/ins

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Python Data Science Toolbox (Part 2)

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Using iterators in PythonLand

You'll learn all about iterators and iterables, which you have already worked with when writing for loops.

• Iterators and iterables, iter() and next()

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Introduction to Importing Data in Python

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Introduction and flat files

In this chapter, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to impor

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Intermediate Importing Data in Python

In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Importing data from the Internet

The web is a rich source of data from which you can extract various types of insights and findings. In this chapter, you will learn how to get data from the web, whether it is stored in files or in HTML. You'll also learn the basics of scraping and parsing web data.

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Coursera Specializations and Courses

Preparing for Google Cloud Certification: Machine Learning Engineer

Advance your career as a Cloud ML Engineer

• Google Cloud Big Data and Machine Learning Fundamentals
• Feature Engineering
• ML Pipelines on Google Cloud

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How Google Does Machine Learning

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.

Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

Intro to Specialization

• In 5 years -- 2012-17 Google has built and deployed over 4000 ML models

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Launching into Machine Learning

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Objectives:

• Identify why deep learning is currently popular
• Optimize and evaluate models using loss functions and performance metrics
• Mitigate common problems that arise in machine learning

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Intro to TensorFlow

We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine.

Introduction

Course Objectives:

• Create machine learning models in TensorFlow

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Feature Engineering

How you can improve the accuracy of your machine learning models? How to find which data columns make the most useful features? Here we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models.

Hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Code solutions which will be made public for your reference as you work on your own future data science projects.

Course Objectives:

• Describe the major areas of Feature Engineering