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https://github.com/mxagar/data_science_udacity

My personal notes, code and projects of the Udacity Data Science Nanodegree.
https://github.com/mxagar/data_science_udacity

dashboard data-analysis data-engineering data-science machine-learning-pipelines

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My personal notes, code and projects of the Udacity Data Science Nanodegree.

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# Udacity Data Science Nanodegree: Personal Notes



These are my personal notes taken while following the [Udacity Data Science Nanodegree](https://www.udacity.com/course/data-scientist-nanodegree--nd025).



The Nanodegree asssumes basic data analysis skills with python libraries (pandas, numpy, matplotlib, sklearn, etc.) and has 5 modules that build up on those skills; each module has its corresponding folder in this repository with its guide Markdown file:



1. Introduction to Data Science: [`01_Intro_Data_Science`](./01_Intro_Data_Science/DSND_Introduction.md).

2. Software Engineering: [`02_SoftwareEngineering`](./02_SoftwareEngineering/DSND_SWEngineering.md).

3. Data Engineering: [`03_DataEngineering`](./03_DataEngineering/DSND_DataEngineering.md).

4. Experimental Design & Recommendations: [`04_ExperimentalDesign_RecSys`](./04_ExperimentalDesign_RecSys/DSND_ExperimentalDesign_RecSys.md).

5. Data Scientist Capstone (Spark): [`05_Capstone_Project`](./05_Capstone_Project/DSND_Capstone.md).



Additionally, it is necessary to submit and pass some projects to get the certification:



- Create a data science project and write a blog post: [airbnb_data_analysis](https://github.com/mxagar/airbnb_data_analysis).

- Disaster response prediction pipeline deployed on a Flask app: [disaster_response_pipeline](https://github.com/mxagar/disaster_response_pipeline).

- Recommender System which suggests new articles to the users of the IBM Watson Studio Platform: [recommendations_ibm](https://github.com/mxagar/recommendations_ibm).

- Capstone project: Prediction of customer churn of a music streaming service using Spark: [sparkify_customer_churn](https://github.com/mxagar/sparkify_customer_churn).



A regular python environment with the usual data science packages should suffice (i.e., scikit-learn, pandas, matplotlib, etc.); any special/additional packages and their installation commands are introduced in the guides. A recipe to set up a [conda](https://docs.conda.io/en/latest/) environment with my current packages is the following:



```bash

conda create --name ds pip python=3.10

conda activate ds

pip install -r requirements.txt

```



As a side note, I list here some related **free Udacity courses** on several topics:



- **Big Data**

  - [Intro to Hadoop and MapReduce](https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617)

  - [Deploying a Hadoop Cluster](https://www.udacity.com/course/deploying-a-hadoop-cluster--ud1000)

  - [Real-Time Analytics with Apache Storm](https://www.udacity.com/course/real-time-analytics-with-apache-storm--ud381)

  - [Big Data Analytics in Healthcare](https://www.udacity.com/course/big-data-analytics-in-healthcare--ud758)

  - [Spark](https://www.udacity.com/course/learn-spark-at-udacity--ud2002)

- **Databases and APIs**

  - [Data Wrangling with MongoDB](https://www.udacity.com/course/data-wrangling-with-mongodb--ud032)

  - [SQL for Data Analysis](https://www.udacity.com/course/sql-for-data-analysis--ud198)

  - [Designing RESTful APIs](https://www.udacity.com/course/designing-restful-apis--ud388)

- **Interview Preparation**

  - [Data Science Interview Prep](https://www.udacity.com/course/data-science-interview-prep--ud944)

  - [Machine Learning Interview Preparation](https://www.udacity.com/course/machine-learning-interview-prep--ud1001)



Mikel Sagardia, 2022.  

No guarantees.