Aspire Journeys

Data Scientist: Natural Language Processing Specialist

  • 27 Courses | 11h 30m 26s
  • 45 Labs | 44h 55m
By transforming human language into data, natural language processing helps you make sense of it. In this career path, you'll learn to extract meaning from text, create chatbots, and build neural networks. Along the way, you'll build portfolio-worthy projects that will help you prepare for a career in data science.

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."

  • 4 Courses | 3h 30m
  • 2 Labs | 2h

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.

  • 1 Course | 20m
  • 5 Labs | 5h

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.

  • 2 Courses | 20m
  • 4 Labs | 4h

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.

  • 1 Course | 10m
  • 4 Labs | 4h

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.

  • 2 Labs | 2h


Natural Language Processing: Getting Started with NLP
Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.
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