Data Scientist: Natural Language Processing Specialist

This no-code course introduces the foundational how’s and why’s of data. How do statistics help us make conclusions from data? Why is good design critical for communicating data stories through data viz? What are the different kinds of analysis we can perform on a dataset? This course will help you feel empowered to answer these questions (and more!) and work with data with confidence. You will learn how to evaluate data quality, interpret statistical conclusions, create and read data visualizations, and analyze data responsibly."

We live in a data-driven world: people search through data to find insights to inform strategy, marketing, operations, and a plethora of other categories. There are a ton of businesses that use large, relational databases, which makes a basic understanding of SQL a great employable skill not only for data scientists, but for almost everyone.

In this course, you'll learn how to communicate with relational databases through SQL. You'll learn—and practice with 4 projects—how to manipulate data and build queries that communicate with more than one table.

Build a foundation in programming with Python with a focus on Data Science!

Continue building your Python Skills while applying them to real data science challenges.

Learn how to use the Python pandas library and lambda functions for Data Science.

In this course, you will learn about exploratory data analysis techniques in Python, including:

- EDA for data preparation
- Summary statistics
- Data visualization techniques
- EDA prior to building a machine learning model

Prior to taking this course, you should have some knowledge of base Python and experience with pandas DataFrames.

Exploratory data analysis is an important part of any Data Scientist or Analyst's workflow, so we highly recommend this course for anyone who is interested in working with data.

Learn how and when to use the essential statistical tools Data Scientists use to analyze data.

If a picture is worth a thousand words, then a visualization is worth more than a thousand data points. Learn how to make them here!

Use your understanding of data visualization to analyze and plot data about GDP and life expectancy.

Clean, well-structured data is essential to data science but cleaning data requires both a keen eye and technical skills. Develop both here!

Communication is an important part of your work as a data scientist. Learn best practices for creating reports and effectively explaining your findings to various audiences.

Use your knowledge of data analysis to interpret data about endangered animals for the National Park Service.

Expand your knowledge of Python with Classes and Modules.

Build your mathematics foundation in preparation for Machine Learning.

Build a foundational understanding of machine learning and feature engineering.

Supervised learning is the most common type of machine learning, solving prediction and classification problems. These are the most popular algorithms because they can solve the most kinds of problems and are the easiest to interpret. The methods in this unit form the foundation for more complex and layered supervised learning methods later on.

You'll learn how and when to implement algorithms such as Linear and Logistic Regression, KNN, and Decision Trees. You'll learn about evaluation metrics such as Precision, Recall, Accuracy, and F1. By the end of this unit, you'll be able to decide when to use each method to solve problems.

Unsupervised learning is one of the most exciting areas of machine learning because it allows you to take unlabeled training data and still generate insights from it! On its own, it's a powerful way to think about data independent from human input. Combined with supervised methods, it can transform how we think about and work with data.

This unit introduces K-means clustering and Principle Component Analysis (PCA), two of the most popular unsupervised techniques. By the end of this unit, you will know how and when to apply each of those algorithms and how to interpret and evaluate the results.

Continue building your machine learning knowledge with Support Vector Machines, Random Forests, and Naive Bayes Classifiers.

Use your knowledge of machine learning to build, train, and test predictions you draw about data from OKCupid.

Explore how deep learning and neural networks are leveraged for machine learning!

Delve into the exciting world of Natural Language Processing (NLP) with this overview of major topics in the field.

Find out how to prepare your text data for most NLP tasks.

Apply regular expressions (regex) and other natural language parsing tactics to find meaning and insights in the texts you read every day.

Learn different ways to represent language numerically, including bag-of-words, tf-idf, and word embeddings.

Learn about seq2seq and LSTM neural networks commonly used in NLP work and how to implement them for machine translation.

Build your very first chatbot with Python and say "hello" to your next cutting-edge skill!

You've learned a lot and it's time to show off your skills. Analyze text message data any way you like in this portfolio project.