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.

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

Regards, Mikiko

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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

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

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