10 Best Udacity Courses, Nanodegrees & Certifications
Udacity is one of the leading platforms for learning new skills. It offers a plethora of online courses on various skills and technology available. We have reviewed hundreds of courses and certifications offered by this powerful platform. We are proud to present some of the best Courses, Nanodegrees & Certifications that Udacity has to offer in some of the most influential and wealthy education fields.
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Top Udacity Courses, Nanodegrees & Certifications List
1. Become a Data Scientist
Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science career.
In this course, you will learn how to:
- understand the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders.
- develop software engineering skills that are essential for data scientists, such as creating unit tests and building classes.
- work with data through the entire data science process, from running pipelines, transforming data, building models, and deploying solutions to the cloud.
- design experiments and analyze A/B test results. Explore approaches for building recommendation systems.
- build your own open-ended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist.
The course includes:
- Solving Data Science Problems
- Software Engineering for Data Scientists
- Data Engineering for Data Scientists
- Experiment Design and Recommendations
- Data Science Projects
You will learn to master the skills necessary to become a successful Data Scientist. You will work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.
- Course rating: 4.7 out of 5.0 (781 Ratings total)
- Duration: 160 Hours
- Certificate: Certificate of completion
2. Become a Machine Learning Engineer
Learn advanced machine learning techniques and algorithms -- including how to package and deploy your models to a production environment.
In this course, you will learn how to:
- write production-level code and practice object-oriented programming, which you can integrate into machine learning projects.
- deploy machine learning models to a production environment using Amazon SageMaker.
- apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and custom-made Amazon SageMaker models.
- select a machine learning challenge and propose a possible solution.
The course includes:
- Software Engineering Fundamentals
- Machine Learning in Production
- Machine Learning Case Studies
- Machine Learning Capstone
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment.
Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry.
- Course rating: 4.6 out of 5.0 (41 Ratings total)
- Duration: 120 Hours
- Certificate: Certificate of completion
3. Become an Android Developer
Start your career as an Android developer. Learn best practices for mobile development, build a portfolio of apps, and publish your own app to Google Play.
In this course, you will learn how to:
- create an Android app, along with some helpful resources to get you started.
- build a cloud-connected Android app. Blending theory and practice, learn how to build great apps the right way.
- make your apps more responsive, and create a total user experience with home screen widgets and third-party libraries.
- deeply integrate rich media, test user interfaces, and publish to Google Play.
- customize your Gradle build, and explore advanced topics like app testing, configuring free vs. paid apps, and creating and integrating libraries.
- apply the design principles that define Android's visual language to your apps, using material design elements, transitions, and graphics, across multiple form factors.
The course includes:
- Developing Android Apps
- Advanced Android App Development
- Gradle for Android and Java
- Material Design for Android Developers
- Capstone Project
This course will help you with intermediate programming skills that are important for you to become a professional Android developer. By the end of this program, you will have a diverse portfolio of projects to show employers, including your own app on Google Play.
- Course rating: 4.7 out of 5.0 (311 Ratings total)
- Certificate: Certificate of completion
4. Business Analytics
Gain foundational data skills applicable to any industry. Collect and analyze data, model business scenarios, and communicate your findings with SQL, Excel, and Tableau.
In this course, you will learn how to:
- optimize your classroom.
- people use data to answer questions and find their own insights from a data dashboard.
- use statistics and visuals to find and communicate insights.
- develop Excel skills to manipulate, analyze, and visualize data in a spreadsheet.
- build Excel models to analyze possible business outcomes.
- use Structured Query Language (SQL) to extract and analyze data stored in databases.
- apply design and visualization principles to create impactful data visualizations, build data dashboards, and tell stories with data.
The course includes:
- Introduction to Data
- SQL for Data Analysis
- Data Visualization
In this program, you will learn foundational data skills that apply across functions and industries. You will learn to analyze data and build models with Excel, query databases using SQL, and create informative data visualizations with Tableau.
- Course rating: 4.7 out of 5.0 (1194 Ratings total)
- Duration: 120 Hours
- Certificate: Certificate of completion
5. Become a Digital Marketer
Gain real-world experience running live campaigns as you learn from top experts in the field. Launch your career with a 360-degree understanding of digital marketing.
In this course, you will learn how to:
- organize and plan your marketing approach with the framework provided.
- apply what you learn in both B2C and B2B contexts.
- plan your content marketing.
- develop content that works well for your target audience, and how to measure its impact.
- understand more about the main social media platforms.
- manage your social media presence, and how create effective content for each platform.
- understand the opportunities for targeted advertising in social media.
- execute advertising campaigns that resonate with your audience.
- optimize your search engine presence through on-site and off-site activities.
- develop your target keyword list, optimize your website UX and design, and execute a link-building campaign.
- create, execute, and optimize an effective ad campaign using Ads by Google.
- display advertising works, how it is bought and sold (including in a programmatic environment).
- set up a display advertising campaign using Google Ads.
- create an email marketing strategy, create and execute email campaigns, and measure the results.
- use Google Analytics to evaluate your audience, measure the success of your acquisition and engagement efforts, evaluate your user’s conversions to your goals, and use those insights to plan and optimize your marketing budgets.
The course includes:
- Marketing Fundamentals
- Content Strategy
- Social Media Marketing
- Social Media Advertising with Facebook Blueprint
- Search Engine Optimization (SEO)
- Search Engine Marketing with Google Ads
- Display Advertising
- Email Marketing
- Measure and Optimize with Google Analytics
Learn to create marketing content, use social media to amplify your message, make content discoverable in search, run Ads campaigns, and advertise on Facebook. Additionally, learn how display and video ads work and how to market with email, and measure and optimize with Google Analytics.
- Course rating: 4.7 out of 5.0 (957 Ratings total)
- Duration: 120 Hours
- Certificate: Certificate of completion
6. Become a Self-Driving Car Engineer
Self-driving cars are transformational technology, on the cutting edge of robotics, machine learning, and engineering. Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world.
In this course, you will learn how to:
- self-driving cars work and the services available to you as part of the Nanodegree program.
- use a combination of cameras and software to find lane lines on difficult roads and to track vehicles.
- build deep neural networks and train them with data from the real world and from the Udacity simulator.
- train convolutional neural networks to classify traffic signs, and then train a neural network to drive a vehicle in the simulator.
- program fundamental mathematical tools called Kalman filters.
- understand the principles of Markov localization to program a particle filter, which uses data and a map to determine the precise location of a vehicle.
- apply model-driven and data-driven approaches to predict how other vehicles on the road will behave.
- construct a finite state machine to decide which of several maneuvers your own vehicle should undertake.
- generate a safe and comfortable trajectory to execute that maneuver.
- work with a team of Nanodegree students to combine what you have learned over the course of the entire Nanodegree Program to drive Carla, a real self-driving car, around the Udacity test track.
The course includes:
- Computer Vision
- Deep Learning
- Sensor Fusion
- Localization
- Planning
- Control
- System Integration
You will first apply computer vision and deep learning to automotive problems, including detecting lane lines, predicting steering angles, and more.
Next, you will learn sensor fusion, which you will use to filter data from an array of sensors in order to perceive the environment. Then, you will work with a team to program Carla, Udacity’s real self-driving car.
- Course rating: 4.7 out of 5.0 (34 Ratings total)
- Duration: 360 Hours
- Certificate: Certificate of completion
7. Become a Computer Vision Expert
Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.
In this course, you will learn how to:
- master computer vision and image processing essentials.
- extract important features from image data, and apply deep learning techniques to classification tasks.
- apply deep learning architectures to computer vision tasks.
- combine CNN and RNN networks to build an automatic image captioning application.
- locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight.
The course includes:
- Introduction to Computer Vision
- Advanced Computer Vision and Deep Learning
- Object Tracking and Localization
Learn cutting-edge computer vision and deep learning techniques—from basic image processing to building and customizing convolutional neural networks.
Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects.
- Course rating: 4.7 out of 5.0 (450 Ratings total)
- Duration: 156 Hours
- Certificate: Certificate of completion
8. AI Programming with Python
Learn Python, NumPy, pandas, Matplotlib, PyTorch, Calculus, and Linear Algebra—the foundations for building your own neural network.
In this course, you will learn how to:
- start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.
- use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib.
- understand the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
- understand the foundations of calculus to understand how to train a neural network: plotting, derivatives, and the chain rule.
- gain a solid foundation in the hottest fields in AI: neural networks, deep learning, and PyTorch.
The course includes:
- Introduction to Python
- Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib
- Linear Algebra Essentials
- Calculus Essentials
- Neural Networks
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
- Course rating: 4.6 out of 5.0 (592 Ratings total)
- Duration: 120 Hours
- Certificate: Certificate of completion
9. Deep Learning
Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.
In this course, you will learn how to:
- get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.
- understand neural network basics, and build your first network with Python and NumPy.
- use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.
- build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.
- build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.
- understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.
- Train and deploy your own PyTorch sentiment analysis model.
- build a model, deploy it, and create a gateway for accessing it from a website.
The course includes:
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Deploying a Sentiment Analysis Model
Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch.
Build convolutional networks for image recognition, recurrent networks for sequence generation, and generative adversarial networks for image generation, and learn how to deploy models accessible from a website.
- Course rating: 4.7 out of 5.0 (909 Ratings total)
- Duration: 192 Hours
- Certificate: Certificate of completion
10. Become a Data Analyst
Use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions.
In this course, you will learn how to:
- understand the data analysis process of wrangling, exploring, analyzing, and communicating data. Work with data in Python, using libraries like NumPy and Pandas.
- apply inferential statistics and probability to real-world scenarios, such as analyzing A/B tests and building supervised learning models.
- understand the data wrangling process of gathering, assessing, and cleaning data.
- use Python to wrangle data programmatically and prepare it for analysis.
- apply visualization principles to the data analysis process.
- explore data visually at multiple levels to find insights and create a compelling story.
The course includes:
- Introduction to Data Analysis
- Practical Statistics
- Data Wrangling
- Data Visualization with Python
Advance your programming skills and refine your ability to work with messy, complex datasets. You will learn to manipulate and prepare data for analysis and create visualizations for data exploration. Finally, you will learn to use your data skills to tell a story with data.
- Course rating: 4.6 out of 5.0 (1,184 Ratings total)
- Duration: 160 Hours
- Certificate: Certificate of completion
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