Are you looking forward to enrolling at Udacity Data Science Nanodegree but you are confused about it? Then this Udacity Data Science nanodegree review article is the right place to clear your doubts.
Udacity Data Science Nanodegree is a program that many students look forward to learning data science as it is one of the fastest-growing career choices these days.
With its partnership with some of the most giant tech corporations like Google, Amazon, Facebook, etc., it provides all the required up to date knowledge about data science and hands-on practices for better understanding.
Udacity Data Science Nanodegree has been able to give solid results since its foundation and hence it is still considered by millions of people around the world.
This review article highlights the relevancy of the Udacity data science nanodegree program in 2021, its courses, duration, fees structure along with the pros, cons and if it’s worth enrolling oneself in it.
Let’s get started!
Who can enroll for Udacity Data Science Nanodegree?
Anyone can apply for the Udacity Data Scientist Nanodegree program regardless of their experience or background.
However, it is ideal for professionals, students and anyone who has at least basic knowledge of programming.
The students who wish to enroll for the course should have prior knowledge of programming like Python programming and its libraries. So, that they feel comfortable throughout their learning process.
You also need to have decent knowledge about SQL programming and mathematical topics like statistics, calculus, linear algebra, wrangling and visualizing data.
If there’s someone who’s just starting to get into programming but doesn’t have basic knowledge of it, then Udacity’s free courses will help such beginners to get a hands-on learning experience.
What type of Content and how many Projects are covered in Data Scientist Nanodegree by Udacity?
The Udacity Data Scientist Nanodegree Program has several concepts that guide the students to pursue a successful career in data science.
However, it is important that the students are familiar with several factors of data science such as programming, probability and statistics, visualizing data using matplotlib, ML, maths, and data wrangling.
For better understanding, it is required that the students are well versed in each of the main components of the mentioned topics above.
Some of the main concepts are –
- creating functions, logic, common applications and managing control flow, libraries and arrays of Python, etc
- Managing database, JSON, CSV, pandas, and Sklearn by data wrangling
- Probability theory, calculus, linear regression and algebra, matrices, etc
- Supervised and unsupervised learning of ML
There are a total of 5 courses that prepare the students for their data science careers. These courses are divided into 2-5 lessons depending on the type of course selected.
These courses are offered based on the current era of data science so that the students get all the knowledge that is best suitable for their motive.
The names of the courses are as follows –
- Course I: Solving Data Science Problems
- Course II: Software Engineering For Data Scientists
- Course III: Data Engineering For Data Scientists
- Course IV: Experiment Designs And Recommendations
- Course V: Data Science Projects
Let’s get to know more about these courses in detail.
Course I: Solving Data Science Problems
Solving Data Science Problems prepares the learners to know in-depth information on data science and creating top-notch data visualization using the same.
There are two lessons that can be found in this course; the data science process and effective communication with stakeholders.
Project:
The learners will get to select a dataset and find the appropriate answers or data after correctly identifying three questions.
This process involves the usage of CRISP-DM, data wrangling, machine learning for predicting the correct audience, statistics and well-versed communication skills.
Along with the project, you need to develop a GitHub repository and create a data science blog to attract the potential community.
The data science blog post needs to have solid information about the codes, the best practices of data science and how its implementation is turning itself towards a great future.
Data science nanodegree project not only ensures to give adequate knowledge about data science but also the importance of communication that results in generating a successful relationship with the stakeholders.
Course II: Software Engineering For Data Scientists
Data Scientist Nanodegree Program prepares the budding data scientists by making them converse in some of the most important aspects of data science like the creation of unit tests and classes.
There are three lessons that can be found in this course; software engineering practices, object-oriented programming and web development.
In lesson one, you will be able to get their hands on developing efficient, modular and accurate code. You will also get to know the process of creating unit tests to check programs and analyse the results with logging.
In lesson two, this course will measure and use object-oriented programming correctly.
It will also cover topics like learning magic methods, modular Python packages like pandas and Scikit-learn and using a variety of classes to develop a program.
Finally, in lesson three, you will get to know the steps in building a website. This includes knowing the structure of a web application and using Flask, Plotly and Bootstrap to develop the same.
At the end of the lesson, the users are given the task to create a data dashboard with the help of a dataset and finally upload it to a web app.
Course III: Data Engineering for Data Scientists
This Udacity data science course enables the exploring data scientists to get practical knowledge about developing and transforming models, data, etc. They will also be able to run pipelines and deploy cloud solutions.
There are three lessons in this course; ETL pipelines, natural language processing (NLP) and machine learning pipelines.
In lesson one, the readers will get to know and manage ETL pipelines, CSV, JSON, APIs and databases.
They would be able to manage missing and copied data as well as understand the ways of encodings and columns along with creating dummy values and SQLite databases.
In lesson two, this course makes the readers able to analyze tokenization and lemmatization of text data as well as removing stop words and using Scikit-learn for vectorization of text data.
As the lesson progresses further, users will be getting tf-idf practices and words to develop features using their own knowledge.
They will also be able to correctly use several tools such as entity recognition and speech tagging to extract features and finally make sentiment analysis through the NLP model.
In lesson three, this data science course dives deep into the advantages of machine learning and using it to make data preparation and modeling processes.
Furthermore, there’s some extensive topics covered that elaborated using Scikit-learn’s pipeline for chain data transformation, optimization of parameters through grid searches and making difficult workflows by using feature unions in parallel steps.
Project:
Towards the end of the lesson, there’s a case study that involves the making of a dataset model using a machine learning pipeline.
This means that in the project, the users will need to create a data pipeline that will be able to help spread message data from natural disasters. They will also need to prepare emergency text messages relating to the need of the sender’s communication.
Successful completion of this case study shows that the users are finally getting their hands-on the process.
- Course IV: Experiment Designs and Recommendations
This Udacity data science course is designed to be performed on IBM Watson studio’s data platform. It gives a hands-on approach to developing recommendation engines that analyzes customer behaviour and shows the best results to the potential customers.
There’s a total of five lessons in this course; experiment design, statistical concerns of experimentations, A/B testing, introduction to recommendation engines and matrix factorization for recommendations.
In lesson one, it gives a good understanding of making experiments and the differences that separate experiments and observational studies. It also gives insights on control and testing conditions and the correct way to choose these groups.
In lesson two, the users will get to know about the real-time use of statistical values and key metrics. They will be involved in SMART experiments for better clarity.
Lesson three shows the users about A/B testing and its drawbacks, bias sources like Novelty and Recency Effects and various measures of comparison methods like FDR, Bonferroni and Tukey.
At the end of this lesson, they will need to find and write a report detailing an experiment involving a technical screener from Starbucks.
Lesson four enables you to identify usual methods to separate general based, content-based and collaborative filter based recommendation engines.
At the end of this lesson, they will be given the directions to list the first task of this lesson in Python.
Finally, during lesson five, this data science course discusses traditional regression and classification techniques and their impact on recommendation engines. Additionally, the users will get to know how to use matrix factorization and FunkSVD to develop recommendation engines as well as analysing the given data for better results. Along with all these processes, they will be able to understand some of the common problems faced by recommendation engines and their solutions.
- Course V: Data Science Projects
This final topic will test the overall knowledge gained throughout all these courses and finally lead the successful users as data scientists.
In this course, there are five lessons that are divided into electives that the users can choose from to develop their very own data science projects. The project they choose to work on is known as the data science capstone project.
This final project prepares the users to create, modify, change and analyse their data science project for the correct set of results. They will need to showcase the same project on the GitHub repository which will finally show their calibre of being a data scientist.
Are the instructors for Data Science Nanodegree experienced?
The instructors for Udacity Data Science Nanodegree Program are well experienced and have worked with several renowned companies as data scientists, engineers, instructors and so on.
They hold degrees from reputed universities like the University of Michigan, the University of Quebec, UC Riverside and so on.
Many of the instructors have worked together with Udacity to create courses based on the demands of the students.
Some of the best-known instructors are Josh Bernhard, Luis Serrano, Andrew Paster, David Drummond, Judit Lantos and many more.
Udacity Data Science Nanodegree Duration and Cost:
The Udacity Data Science Nanodegree Program costs $310.76/month if you choose to enroll yourself on a monthly basis.
You can complete the course in your own comfort zone as well as cancel the enrolment anytime according to your wish.
There’s another option of paying for 4-month access to the data science course. Originally priced at $1243.04 for 4-month access, it’s now discounted and available for $1056.44 only.
In this scheme, you will get to save an extra 15% if you give a 15% upfront payment. Although the course is designed to be completed within a span of 4 months, you can switch to monthly access if you feel you need more time to complete the course.
How to apply for Financial support for the Udacity Data Science Nanodegree course?
There’s good news for students who are trying to enroll for this Udacity data science nanodegree program but are having second thoughts because of the cost.
This data science nanodegree is now available at a heavily discounted price because of the current global pandemic scenario (COVID-19) so students can use this opportunity to the fullest.
The process is quite simple. At first, they need to click on the link of the enrollment form and sign up.
The next step requires them to fill out some of the basic details like your phone number, country, current working status, company size, and personal income.
You need to enter the course in which you are interested to enroll and the motive behind your enrollment.
After filling out the above details, then you need to click on the Save & Submit option.
Udacity will go through the enrollment form and analyze it thoroughly. Based on that, they will provide personalized discounts to you.
(I received a flat 75% discount based on my application so you might get the same or more depending on your application)
Tips to complete Data Science Nanodegree by Udacity in less time:
The nanodegree in data science by Udacity usually takes about 4 months to get completed. However, there are certain ways to complete the course within a span of one month.
It is important to note that although these tips are generally beneficial in completing the course in a month, it varies from person to person.
A user who has basic knowledge and experience in data science or programming can easily manage to cope up with the data science course in a month.
While someone who doesn’t have prior data science experience or background might face difficulties even with these tips.
Some of the tips by this Udacity Data Science Nanodegree review article to complete the course in a month are –
- Data science is a vast course so there are several topics that are covered serial wise. The students should build or develop programs, datasets or anything asked in the exercises right after finishing a lesson.
Practically applying all the knowledge after learning each lesson ensures the concept is intact on the students’ minds and there isn’t any need to go back and keep on reading the same thing.
- Before officially starting the data science program, it is preferable that the users first take some of the free courses to gain basic information knowledge of Python programming, data science, machine learning and so on.
It is ideal for users from non-technical or inexperienced backgrounds to effectively finish their course before the said duration.
- Investing more time than the recommended duration leads to the completion of the project or course before time.
Investing leisure time effectively in completing this data science nanodegree program not only finishes the course before time but also gives the learners more time to clear their doubts and concepts in the later part.
- It is always advisable to seek doubts and answers through the experiences of other students and past students of Udacity. The users can find relevant information through GitHub answers that would help them in completing their course faster and reliably.
Pros:
- The quality of the course is excellent. It is partnered with companies like Google, Microsoft, Accenture, etc which delivers the best results.
- Students get access to LinkedIn, GitHub and other platforms for their career support. Udacity provides cover letters, resumes that attract potential job offers.
- Learners can also get expert consultation to sort their queries reliably.
- Users get a certificate or nanodegree upon completion of a program. However, this is only applicable in paid courses.
- Easy to understand interface.
Cons:
- The courses are expensive so learners need to have a budget before purchasing any paid course.
- Although there are subtitles for those who don’t understand English, the course is primarily written in English so it might be a little confusing for non-English speakers.
Conclusion:
Is Udacity Data Science Nanodegree Worth it?
Yes, one should definitely look forward to enrolling in Udacity’s data science nanodegree program.
Its modern approach and content supported by tech giants like Google and Amazon make it ideal for users to virtually excel their data science knowledge and set a good career in it.
Additionally, the experienced instructors and technical support team deliver a smooth performance in completing the course.
This article reviews the Udacity data science nano degree program, its facilities and much more.
What are the offering Services given by Udacity Data Science Nanodegree for learners?
Udacity data science nanodegree ensures a reliable, flexible, efficient and professional virtual environment for the students to get a better understanding of the concepts.
They can clear their doubts, get one-on-one expert consultations, seek technical support and get feedback from the best instructors regarding their projects.
The following are the list of offers provided by Udacity –
STUDENT SERVICES
- Technical mentor support
- Student community
CAREER SERVICES
- Resume support
- Github review
- Linkedin profile optimization
Why should I enrol for the Udacity Data Science Nanodegree course?
It has partnerships and support from some of the prominent giant tech hubs that ensure the high quality of the courses.
The courses are flexible and can be completed within 4 months. During the course, the students will get hands-on experience and training on major data science projects through activities and guidance from experts.
They will also get all the support needed throughout the course duration.
Upon completion of the course, they are entitled to get the graduation certificate which is recommended by Google, Amazon etc. This certificate will help them to boost their career and find the perfect data science job that suits them.
Is this Udacity Data Science Nanodegree helpful for me? Then how?
The program delivers excellent knowledge and experience which enables the students to find the perfect data science job.
Some of the prominent data science jobs are Analysts, Statisticians, Engineers, Data and Analytics Managers, Data Administrators, etc.
Are there any criteria to take admission for Udacity Data Science Nanodegree?
There are no specific criteria for learners to take admission in a data science nanodegree.
Learners from all the countries, irrespective of their experience or background, are welcomed to join the course of data science nanodegree.
Are there any prerequisite courses to learn for the Udacity Data Science Nanodegree Course?
The users should have knowledge or experience about the following concepts which are essential for becoming a data scientist :
- Python programming, including common data analysis libraries (NumPy, pandas, Matplotlib).
- SQL programming
- Statistics
- Calculus
- Linear Algebra
- Experience wrangling and visualizing data
Even if a user doesn’t have the required knowledge then there’s no need to worry!
Udacity provides free courses that can make them have the desired knowledge to be able to effectively complete the program duration.
Is there any financial support provided by Udacity for Data Science Nanodegree?
Yes, there are some scholarships that financially support the students to complete the course provided by Udacity for Data Science Nanodegree.
