In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this!
Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework.
This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data.
This course is also a part of our Data Analyst Nanodegree.
Data Extraction Fundamentals
Assessing the Quality of Data,Intro to Tabular Formats,Parsing CSV
Data in More Complex Formats
XML Design Principles,Parsing XML,Web Scraping
Sources of Dirty Data,A Blueprint for Cleaning,Auditing Data
Working with MongoDB
Data Modelling in MongoDB,Introduction to PyMongo,Field Queries
There are two main parts of that, SQL and MongoDB, SQL lecturer gave more details for students to understand but not for Data Wrangling and MongoDB, skipping lot of details and not well explanation for the codes. Sure that students need to invest more time to study but not recommend for those who do not have python knowledge, it's the worst course so far from data analyst nanodegree.
Anonymous completed this course.
I do not know how anyone can rate the class more than 1 star. The learning materials were pathetic, and not usually on topic. I have had to go through the materials at least 3 times and consulted with Senior Data Analysts at my current employer....
I do not know how anyone can rate the class more than 1 star. The learning materials were pathetic, and not usually on topic. I have had to go through the materials at least 3 times and consulted with Senior Data Analysts at my current employer. Their comments range from "That is completely unhelpful" to just quiet sighs.
At the start, the forums for the entire Nanodegree, were supported by mentors who would respond, often unhelpfully, but sometimes gleaning a nugget of knowledge. Late in 2017, I was informed that the forums are no longer being monitored. I was assigned a mentor, I don't want a mentor, I want help when I need help not at their convenience.
The "instructor" (notice the quotes) from MongoDB , although very knowledgeable, is a not a very good communicator of that knowledge. He talks FAST, jumps around from subject to subject, utilizes at time random data modules. He may be a good person to ask a question of, but to teach, he is severely lacking.
Now attempting to complete the project, it is too a joke. the instructions are very unclear and unstructured as to expectations.
If you are on the fence about learning Data Analysis, look elsewhere, this one is poorly designed.
Scott Stevens is taking this course right now, spending 12 hours a week on it and found the course difficulty to be very hard.
Where to start.
Although self paced, the course was without direction and expectations. I have worked through the lessons so many times and still the materials are not on target for the exercise. The "project Rubric", was a waste of a mouse click as it showed no specifics as to what is expected.
I have spent more than 5 months learning from other sites, (read non-Udacity) on how to proceed but I feel like a ship with out a rudder and no destination. It boggles the mind that there is no desired outcome for the project... Just ... Explore that data... what does that mean anyways?
I would highly recommend this course. Since this course is self paced, it is possible to finish it very quickly, as the material is not tough to comprehend. I didn't find any "Final Project Instructions" in the course though.