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Mikiko Bazeley edited this page Feb 1, 2019 · 8 revisions

Springboard Data Science Career Track

Hi!

My name is Mikiko Bazeley and this is my repo wiki 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.

Regards, Mikiko

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Resources List by Unit of Study/Chapter

For a comprehensive 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)

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. Career Resources

Topics covered:

  • Anatomy of a tech company
  • Job search strategies that top candidates use
  • How to build your network and effectively use it to land interviews
  • Create a high-quality resume, LinkedIn profile and cover letter
  • Interview coaching and practice, including mock interviews for both technical and non-technical topics
  • Negotiation success tips
  • Practice interview questions for each technical topic
  • Algorithms and data structures to ace your coding interviews

Capstone Project: Building a Data Product

The capstone project is a key part of our curriculum that every student must complete. The projects are designed to provide you with the experience of working in a realistic data science scenario. Working with your mentor, you'll pick a data set and a problem of interest. From start to finish, your project will be targeted to a specific client (real or imaginary). Using the data science techniques, you've learned, you'll not only come up with a reasonable solution to the problem, but learn to present it to them as a compelling story.

You will work on two capstone projects that involve the following:

    1. Formulating a problem based on exploratory data analysis;
    1. Building a model and transforming data so that it can be input to an algorithm;
    1. Iteratively evaluating performance, and adapting model/data input to figure out if more data or a different algorithm is needed to best solve the problem. If you choose one of our specialization tracks, your second capstone project will be related directly to the specialization of your choice.

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