Data Science Bootcamp: Become a Data Scientist. Job Guaranteed.

Data scientists spend a lot of time on data wrangling (i.e., acquiring raw data, cleaning it, and getting it into a format amenable for analysis), usually with the help of semi-automated tools. In this unit, you'll learn the most common tools and workflows in Python that simplify and automate this complicated process.

• Use Pandas to wrangle and clean data
• Work with different file formats, from plain text, to CSV, to JSON
• Get an overview of relational and non-relational databases and gain SQL skills
• Collect data by using Application Programming Interfaces (APIs)

Data science is not just about the math, the algorithms, and the analysis. It's also about telling a good story. In real life, data scientists don't work in a vacuum—there's always a client, internal or external, waiting to act based on the results of their work.

In this unit, you’ll practice the concepts you've learned so far by creating a story out of a data set. You’ll come up with interesting questions you can ask of your data set and use plotting techniques to reveal insights you can use to create a narrative.

Statistics is the mathematical foundation of data science. Inferential statistics helps data scientists identify trends and characteristics of a data set. Not only are these techniques useful for exploring data and telling a good story, but they pave the way for deeper analysis and predictive modeling.

• Master the basics of inferential statistics and parameter estimation
• Use hypothesis testing to determine if a phenomenon is statistically significant
• Learn how correlation and regression can help identify useful features
• Build A/B split tests
• Conduct exploratory data analysis

Machine learning (ML) combines aspects of computer science and statistics to extract useful insights from data. It’s what lets us make useful predictions and recommendations, or automatically find groups and categories within complex data sets. In this unit, you’ll learn the major machine learning algorithms (supervised and unsupervised).

• Use scikit-learn to implement supervised and unsupervised algorithms
• Learn top ML techniques: linear and logistic regression, naive Bayes classifiers, support vector machines, decision trees, and clustering
• Review ensemble learning with random forests and gradient boosting
• Validate and evaluate machine learning systems

As a data scientist, no matter how many algorithms you design or how much data you crunch, ultimately you’ll be writing software. Some companies expect their data scientists to contribute directly to the code base, while others have engineers around to help translate prototype code to production.

In this unit, you’ll learn how to be a good citizen of the code base, with a focus on writing better code, testing and debugging, and working with production systems.

• Work with MapReduce, one of the most popular algorithms for large-scale data manipulation
• Understand NoSQL databases and how they differ from SQL
• Learn Spark, the industry standard in distributed computing frameworks
• Learn SparkML and MLlib to implement Machine Learning at scale on Spark

• Recommendation systems, social network analysis, and time-series analysis.
• Natural Language Processing (NLP): Help teach computers to identify, understand, and interpret human languages.
• Fundamentals of Deep Learning: Uncover the techniques powering machine translation, self-driving cars, and more.