Pursue Your Data Science Goals with Key Advice on Udacity's Nanodegree Program

Tilak Zade

08 June 2023

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Course Overview

Data Scientist Nanodegree Program offered by Udacity provides real-world data science experience with the help of projects designed by industry experts. We recommend this data science nanodegree course to learners who are familiar with Machine Learning (ML) concepts. In addition, learners should be familiar with Python programming, probability, and statistics. Anyone who meets the above eligibility and prerequisite of data science can join this course.
Industries, tech firms, start-ups, and government agencies, are all fields where one can work as a data science engineer.

The industry experts will assign you projects and teach you how to set up data pipelines, create recommendation systems, run experiments, deploy cloud solutions, and manage them. The nanodegree course teaches the data science prerequisites and skills required to be a successful data scientist.

My instructor was Luis, who was formerly an ML Engineer at Google. He holds a Ph.D. in mathematics from the University of Michigan and a Postdoctoral Fellowship at the University of Quebec at Montreal. He did a good job of delivering technical concepts with utmost clarity, and that too from scratch.

"The course focuses on job-specific tools, skills, and techniques for a successful machine learning engineering career."

- Tilak Zade

Course Structure

It is a self-paced, well-curated advanced-level course spread over 4 months. It requires learners to put in 10 hours per week. The course is taught online by experienced faculty members. The professors associated with this course have degrees from renowned institutions in cloud development. Thus, the nanodegree program offers an excellent opportunity to study under international professors.

The learners get to be a part of a Capstone project, an innovative way to enhance the understanding of learnt concepts. They gain the opportunity to build skills through industry-relevant projects and get personalized feedback from 900+ project reviewers. The syllabus of data scientist course's curriculum includes 5 distinguished modules, ranging from the basics of data science (which includes demarcating issues to be solved) followed by Cloud deployment and Amazon Web Services (AWS) to Data pipeline and advanced data wrangling techniques. It ends with an analysis of vivid experiment designs and recommendations.

At the beginning of the course, the topics discussed are the data science process, including how to build effective data visualization and communicate with various stakeholders. Afterward, learners are taught the software engineering skills essential for data scientists, such as creating unit tests and building classes.

At the later stages, learners work with data through the entire data science process, from running pipelines, transforming data, building models and deploying solutions to the cloud. They also get to understand how to design experiments, analyze A/B test results and explore approaches for building recommendation systems.

Insider Tips

In order to get the best out of this nanodegree course, I have included some important tips below that I think you might find useful:

Assessment and Grading Criteria

The assessment method requires completing assignments. Post every video, there is a small assessment at the end to validate your learnings. There are numerous mini-assessment that test your knowledge, but the limit of attempts is restricted to 5. The final assessment ends with a Capstone project.

We worked on the below assessments :

Assessment 1 Dog Breed Classification - This assessment required us to use convolutional neural networks to classify different dogs according to their breeds and deploy our model to allow others to upload images of their dogs and send them back to the corresponding breeds.
Assessment 2 Starbucks - Here, we were asked to use purchasing habits to arrive at discount measures to obtain and retain customers and identify groups of individuals most likely to be responsive to rebates.
Assessment 3 Arvato Financial Services - We worked through a real-world dataset.
Assessment 4 Spark for Big Data - We took a course on Apache Spark and completed a project using a massive, distributed dataset to predict customer churn. Also, we learned to deploy our Spark cluster on either AWS or IBM Cloud. Assessment 5 Any project of our choice.

Capstone project

The capstone project helps learners understand the practical aspect of the program. It lets you leverage what you have learned throughout the program to build a data science project of your choice.

The project will let you define the problem you want to solve, identify and explore the data, perform your analyses and develop a set of conclusions.

The resulting analysis and conclusions will be presented in a blog post and GitHub repository. This project will demonstrate your ability as a data scientist and will be an important component of your job-ready portfolio.

Prerequisites

This program is designed for learners with previous programming and data analysis experience. They should be well aware of various topics before starting the program.

To complete this program, you should meet the following prerequisites to learn data science:

Python programming, including common data analysis libraries (NumPy, Pandas, Matplotlib).
SQL programming
Statistics (Descriptive and Inferential)
Calculus
Linear Algebra
Wrangling and visualizing data experience

Final Take

Currently, I’m working at Wipro Technologies as a Software Development Engineer (SDE), and my responsibility is to manage the dashboard by using advanced ML tools. I opted for this Data Science nanodegree as I already have a background in this field. The course helped me learn ML and Analytics. Having prior knowledge of ML, Python Programming, and SQL helped me throughout the course. This program prepares students for machine learning engineering careers.

As both data scientist and machine learning jobs require machine learning knowledge, each of these two programs begins with a focus on machine learning. The curriculum diverges in later sections, where you focus on more job-specific tools, skills, and techniques.

Key Takeaways

Employed principles of statistics and probability to design and execute A/B tests and recommendation engines to assist businesses in making data-automated decisions

Deployed a data science solution to a basic flask app

Manipulated and analyzed distributed datasets using Apache Spark

Communicated obtained results effectively to stakeholders

Learn how to set up data pipelines, create recommendation systems, run experiments, deploy cloud solutions, and manage them