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https://github.com/mmbazel/springboard-datasciencetrack-student

Springboard Program: Data Science Career Track - NLP
https://github.com/mmbazel/springboard-datasciencetrack-student

capstone data-science data-wrangling datasciencedreamjob dsdj mikikobazeley nlp python springboard

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Springboard Program: Data Science Career Track - NLP

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README 

        
![alt text](

       https://github.com/MMBazel/springboard-program/blob/master/0.jpg

      )



# Springboard Data Science Career Track



Hi!



My name is Mikiko Bazeley and this is my repo for the Springboard Data Science Track. 



From Oct 2018 to April 2019 I completed a number of projects, including two capstones, as part of the DS track. 



All of the documentation, code, and notes can be found here, as well as links to other resources I found helpful for successfully completing the program. 



For questions or comments, please feel free to reach out on [LinkedIn](https://www.linkedin.com/in/mikikobazeley/). 



If you find my repo useful, let me know OR ☕ consider buying me a coffee! https://www.buymeacoffee.com/mmbazel ☕.



Regards,

Mikiko



![alt text](

       https://github.com/MMBazel/springboard-program/blob/master/Additional%20Resources/profile_pic_jpeg.jpg?raw=true

      )



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# Project List by Unit of Study



For a comprehensve list of the projects and corresponding skills needed, please see the list below.



## 1. The Python Data Science Stack

Topics covered: 

* Python      

* Matplotlib, Seaborn—visualization tools in Python  

* Writing clear, elegant, readable code

       in Python using the PEP8 standard



## 2. Data Wrangling

Topics covered:

* Deep dive into Pandas for data wrangling

* Data in files: Work with a variety of file formats from plain text (.txt) to more structured and nested formats files like csv and JSON

* Data in databases: Get an overview of relational and NoSQL databases and practice data querying with SQL

* APIs: Collect data from the internet using Application Programming Interfaces (APIs)



Projects:

* =====> [Mini Project: SQL Practice](https://github.com/MMBazel/springboard-program/tree/master/mini-projects/Ch%205%20-%20Data%20Wrangling/5.3%20SQL%20Practice)



## 3. Data Story



## 4. Statistical Inference

Topics covered:

* Theory of inferential statistics

* Statistical significance

* Parameter estimation

* Hypothesis testing

* Correlation and regression

* Exploratory data analysis

* A/B testing



## 5. Machine Learning

Topics covered:

* Scikit-learn

* Supervised and unsupervised learning

* Top machine learning techniques:

	* Linear and logistic regression 

	* naive bayes

	* support vector machines

	* decision trees 

	* clustering

* Ensemble learning with random forests and gradient boosting

* Best practices

* Evaluating and tuning machine learning systems



## 6. Capstone Project 1: Building a Data Product

	      

* =====> My Capstone Project: [Predicting Qualifieds from First Call](https://github.com/MMBazel/springboard-program/tree/master/capstone1)



## 7. The Natural Language Processing (NLP) Track



Topics covered:

* How to work with text and natural language data

* NLP in Python, using common libraries such as NLTK and spaCy

* Basics of Deep Learning in NLP using word2vec and TensorFlow 

* Data Science at Scale using Spark

* Software Engineering for Data Scientists



## 8. Second Capstone Project: NLP