Udacity Data Analyst vs Data Scientist- Which One is Better? [2025]

Do you want to know which is better between Udacity Data Analyst and Data Scientist? If yes, then this comparison of Udacity Data Analyst vs Data Scientist will clear your doubts.

I have compared both programs on the following criteria- Projects, Topics, Content Quality, Rating, and support provided. And at the end of this article, you will get my final recommendation on whether you should go with the Udacity Data Analyst or Udacity Data Scientist

So read this comparison, Udacity Data Analyst vs Data Scientist, and then decide which one is better for you.

Udacity Data Analyst vs Data Scientist

Udacity Data Analyst and Udacity Data Scientist are the Nanodegree Programs offered by Udacity. Both Nanodegree programs have their own identification and popularity. That’s why many people get confused about which one to choose.

In this article, your confusion will be cleared.

Before we dive into projects and topics covered in both programs, let’s have a quick comparison between both Nanodegree Programs-

Quick Comparison between Udacity Data Analyst vs Data Scientist

	Udacity Data Analyst	Udacity Datta Scientist
Rating-	4.6/5	4.7/5
Price-	For 4 months they cost around $1199.	For 4 months they cost around $1199.
Time to Complete-	4 months(If you spend 10 hours/week)	4 months(If you spend 10 hours/week)
Programming language–	Python Programming	Python Programming
Number of Courses-	4	5
Topics Covered-	Data Wrangling, Matplotlib, Bootstrapping, Pandas & Numpy, Statistics	Machine Learning, Deep Learning, Data Engineering, and Software Engineering
Projects Covered-	1. Explore Weather Trends 2. Investigate a Dataset 3. Analyze Experiment Results 4. Wrangle and Analyze Data 5. Communicate Data Findings	1. Write a Data Science Blog Post 2. Build Disaster Response Pipelines with Figure Eight 3. Design a Recommendation Engine with IBM 4. Data Science Capstone Project
Suitable for-	Those who have previous knowledge in SQL and Python.	Those who have previous knowledge in SQL, Python, and Statistics.
	Check Udacity Data Analyst Nanodegree	Check Udacity Data Science Nanodegree

So, this is a quick comparison of Udacity Data Analyst vs Data Scientist. Now let’s see the topics covered in both Nanodegree programs-

Topics Covered in Udacity Data Analyst Nanodegree Program

Udacity Data Analyst Nanodegree program has 4 courses and 5 projects. And each course has some lessons.

Courses Details-

1. Introduction to Data Analysis
2. Practical Statistics
3. Data Wrangling
4. Data Visualization with Python

Course 1- Introduction to Data Analysis

This is the first course. This first course will teach you how to set up and use Anaconda and Jupyter Notebook. And how to use NumPy and Pandas to perform data analysis processes (wrangling, exploring, analyzing, and communicating data).

In this course, there are 2 projects– Explore Weather Trends and Investigate a Dataset.

Course 2. Practical Statistics

This course is all about statistics. And as we know that statistical knowledge is essential in data analysis. This is the lengthiest and math-heavy course in the Udacity Data Analyst Nanodegree program. In this course, there are 13 lessons.

Throughout this course, you will learn some essential statistical concepts like Simpson’s Paradox, Probability, binomial distribution, conditional probability, Bayes theorem, Standardizing, Hypothesis Testing, T-Tests, and A/B Tests, and Regression(multiple linear regression & logistic regression).

In this course, you have an opportunity to apply these concepts using Python. There is one project in this course- Analyze Experiment Results.

Course 3. Data Wrangling

This is a short course with 4 lessons, but this course covers the essential part of data analysis. In this course, you will learn data wrangling in detail. You will learn data gathering, data assessing, and data cleaning.

You will get a chance to gather data from multiple sources, including gathering files, programmatically downloading files, web-scraping data, and accessing data from APIs. And you will import data of various file formats into pandas, including flat files (e.g. TSV), HTML files, TXT files, and JSON files.

Data quality and tidiness will also be discussed, along with techniques to handle data errors. You will learn how to clean data using Python and pandas. In this course, there is one project- Wrangle and Analyze Data.

Course 4. Data Visualization with Python

This is the last course of this Nanodegree program, where you will learn how to apply visualization principles to the data analysis process. In this course, you will get to know what distinguishes exploratory analysis from the explanatory analysis.

This course also covers different encodings that can be used to depict data in visualizations and various pitfalls that can affect the effectiveness and truthfulness of visualizations.

Then you will learn Univariate, Bivariate, and Multivariate Exploration of Data using matplotlib and seaborn. And how to create a slide deck using a Jupyter Notebook to convey your findings. In this course, there are one project-Communicate Data Findings.

So, this is all about the Udacity Data Analyst Nanodegree program. Now let’s see the topics covered by Udacity Data Science Nanodegree Program-

Topics Covered in Udacity Data Science Nanodegree Program

Udacity Data Science Nanodegree program has 5 courses. And each course has some lessons.

1. Solving Data Science Problems
2. Software Engineering for Data Scientists
3. Data Engineering for Data Scientists
4. Experiment Design and Recommendations
5. Data Science Projects

Course 1. Solving Data Science Problems

This is the first course of the data science Nanodegree program and in this course, you will learn about the data science process, how to build effective data visualization, and how to communicate with different stakeholders.

In this course, there is one project associated named Write a Data Science Blog Post.

So in the data science process, you will learn the CRISP-DM process for business applications, and how to wrangle, explore, and analyze a dataset.

Along with this, you will also learn how to apply machine learning algorithms for prediction, and how to apply statistics for descriptive and inferential understanding.

In lesson 2 Communicating with Stakeholders, you will learn how to write a great data science blog.

Course 2. Software Engineering for Data Scientists

In the second course, you will develop software engineering skills. Software engineering skills are essential for data scientists like creating unit tests and building classes. In this course, there is no project required.

There are 3 lessons in this course- Software Engineering Practices, Object-Oriented Programming, and Web Development.

In the first lesson Software Engineering Practices, you will learn how to write clean, modular, and well-documented code, how to create unit tests to test programs, how to track actions and results of processes with logging, etc.

The second lesson Object-Oriented Programming will teach you how to build and use classes, do portfolio exercises where you will build your own Python package, etc.

In the last and third lesson Web Development, you will learn how to build a web application that uses Flask, Plotly, and the Bootstrap framework.

Course 3. Data Engineering for Data Scientists

In the third course, you will learn how to work with data throughout the entire data science process. Where you will learn about running pipelines, transforming data, building models, and deploying solutions to the cloud.

In this course, there is one project associated named Build Disaster Response Pipelines with Figure Eight.

There are three lessons in this course- ETL Pipelines, Natural Language Processing, and Machine Learning Pipelines.

The first lesson ETL Pipelines will teach you how to access and combine data from CSV, JSON, logs, APIs, and databases, how to normalize data and create dummy variables, how to handle outliers, missing values, and duplicated data, and how to build an SQLite database to store cleaned data, etc.

In the second lesson Natural Language Processing, you will learn how to prepare text data for analysis with tokenization, lemmatization, and removing stop words, how to use scikit-learn to transform and vectorize text data, and how to build an NLP model to perform sentiment analysis, etc.

The last and the third lesson Machine Learning Pipelines will teach you how to use feature unions to perform steps in parallel and create more complex workflows. And you will build a complete case study to build a full machine learning pipeline that prepares data and creates a model for a dataset.

Course 4. Experiment Design and Recommendations

In this fourth course, you will learn how to design experiments and analyze A/B test results. Along with this, you will also explore approaches for building recommendation systems such as content-based filtering and collaborative filtering.

In this course, there is one project associated named Design a Recommendation Engine with IBM.

There are 5 lessons in this course- Experiment Design, Statistical Concerns of Experimentation, A/B Testing, Introduction to Recommendation Engines, and Matrix Factorization for Recommendations.

In the first lesson Experiment Design, you will learn how to define control and test conditions and how to choose control and testing groups.

The second lesson Statistical Concerns of Experimentation will teach you about the applications of statistics in the real world and establishing key metrics.

In the third lesson A/B Testing, you will learn how A/B testing works and its limitations, Sources of Bias such as Novelty and Recency Effects, and Multiple Comparison Techniques (FDR, Bonferroni, Tukey).

The fourth lesson will teach you how to distinguish between common techniques for creating recommendation engines including knowledge-based, content-based, and collaborative filtering-based methods.

In the last lesson, you will learn how to create recommendation engines using matrix factorization and FunkSVD.

Course 5. Data Science Project

In this last course of the Udacity data science Nano Degree Program, you will leverage what you’ve learned throughout the program to build your own open-ended Data Science project. This is my favorite part of the Nanodegree program because you can choose the project of your choice from the list they offer.

They offer the following projects-

• Dog Breed Classification( Neural Networks)
• Starbucks( Customer Segmentation)
• Arvato Financial Services (Likely Supervised Learning)
• Spark for Big Data (Customer churn with PySpark)
• Any other project of your choice.

So, you knew what topics are covered in both the programs Udacity Data Analyst and Udacity Data Scientist.

But the most important question is Udacity Data Analyst vs Udacity Data Scientist– Which One is Better for you?

To answer this question, first, you have to understand the difference between Data Scientist and Data Analyst. So let’s understand the difference between Data Scientist and Data Analyst-

Data Scientist vs Data Analyst

A data scientist is a person, who works on a vast amount of data. This data may be structured as well as unstructured. They use their all skills like statistics, programming, and machine learning for creating a tactical plan. A data scientist has the power of all data editor activities. For business-related decision-making, a data scientist has a higher probability to make a decision.

A data analyst takes the data, performs analysis on this data, and helps companies to make better decisions. Data analysts perform analysis on numeric as well as other kinds of data. Then convert it into the English language so that anyone can understand. After translating it to the English language, the upper management uses this data to make better decisions, which helps them in the business.

Now let’s see what skill sets are required for Data Scientists and Data Analysts-

Data Scientist: Skill Set

A Data scientist required the following skills sets-

1. A Data scientist must be a master in Statistical and Analytical skills.
2. They should have in-depth knowledge of Machine Learning and Deep Learning.
3. A Data scientist must have data mining skills.
4. In-depth knowledge of programming languages( Python, R, SAS).

Data Analyst: Skill Set

1. Data warehousing. (means to deal with data processing like collection, cleaning, and other processes).
2. Adobe and Google Analytics.
3. Must have programming Knowledge. (It is not mandatory but a plus if you have).
4. Statistical Skills.
5. Data Visualization Skills.
6. Database Knowledge like SQL.
7. Spreadsheet Knowledge.

I hope now you understood the basic difference between Data Scientists and Data Analysts. Now let’s see who should enroll in which Nanodegree program between Udacity Data Analyst and Udacity Data Scientist.

Who Should Enroll in Udacity Data Analyst?

Those who have prior knowledge of the following topics-

Python & SQL

If you are a beginner in Python, then don’t directly jump to this program. To succeed in the Udacity Data Analyst Nanodegree program, you should have working experience with data in Python (specifically NumPy and Pandas) and SQL.

If you meet the following prerequisites, then you can enroll in the Udacity Data Analyst Nanodegree program. If not, then first, you should learn Python and SQL.

Who Should Enroll in Udacity Data Scientist?

Udacity Data Science Nanodegree Program is an advanced-level course. That’s why you need to have the following skills before enrolling in the program-

1. Machine Learning-

In machine learning, you should know Supervised and Unsupervised methods. I would suggest you, brush up on the following concepts- classification, decision trees, PCA, regression models, and Clustering.

2. Python & SQL-

If you are a beginner in Python, then don’t directly enroll in this program. To get 100% from Udacity Data Science Nanodegree Program, you should know how to write functions and how to build basic applications. Along with that, you should know common Python libraries like NumPy and Pandas.

Along with Python knowledge, you should be familiar with SQL programming, like querying databases and using joins, aggregations, and subqueries. At the same time, you should be comfortable with Terminal and Github.

3. Probability and Statistics-

You should have basic knowledge of Statistics and Probability.

4. Mathematics-

In math, you should know calculus (maximizing and minimizing algebraic equations) and Linear Algebra which includes matrix manipulation and multiplication.

5. Data Wrangling and Visualization-

You should also know how to clean and transform the data using pandas and Sklearn. And Data Visualization with matplotlib.

Now it’s time to know which one is better for you.

My Recommendation: Udacity Data Analyst vs Data Scientist – Which One is Better?

I recommend Udacity Data Analyst Nanodegree for Beginners.

Why?

Because if you are a beginner, you have to learn the basics of Statistics, Data Wrangling, and Data Visualization. And these topics are covered in the Udacity Data Analyst Nanodegree.

Once you gain these required skills, then you can enroll in Udacity Data Science Nanodegree. But if you are a beginner, I would recommend Udacity Data Analyst Nanodegree.

Udacity Data Science Nanodegree is an advanced-level program. If you already knew these concepts, then you can go for this Nanodegree Program.

NOTE- Completing any course will not make you a data scientist or Data Analyst. These courses will only provide the necessary knowledge of data science and data analytics and a few hands-on projects. So after completing these courses, you have to work on projects with the skills you learned in these courses and expand your portfolio with some other unique projects.

Conclusion

I hope this Udacity Data Analyst vs Data Scientist comparison has cleared your doubts and now you can easily choose the one which suits you. If you have any questions, feel free to ask me in the comment section. I am here to help you. And If you found this article helpful, share it with others to help them too.

• Check Udacity Data Analyst Nanodegree
• Check Udacity Data Science Nanodegree

All the Best!

Happy Learning!

Thank YOU!

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