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Data Science Essentials

Build your data expertise and earn a spot in our next bootcamp

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Give yourself a head start

Data Science Essentials from The Data Incubator is an 8-week online class designed to strengthen your data skills, whether it's to improve your core data wrangling and analysis or to qualify for our data science Fellowship program.

We've developed this class with insights from our industry partners, using the same rigorous methodology as our data science Fellowship program to give you the boost your data skills need.

Successfully complete Data Science Essentials and you'll automatically earn admission into the next TDI Fellowship cohort - and $1000 off of your tuition! *

* The Data Essentials tuition discount applies to direct payment only and cannot be combined with any other enrollment discounts or offers. Students funding the program through loans are ineligible for the Data Essentials tuition discount.

Experienced Teachers

Learn from The Data Incubator's experienced data science instructors dedicated to teaching data analytics.

Jumpstart Your Career

Learn the most sought-after tools and techniques in the industry to help jumpstart your data analyst career.

Designed for Working Professionals

This class was designed for the busy lives of working professionals with evening sessions and recorded lectures.

Build a Series of Miniprojects

Gain hands-on experience applying the tools employers value to real-world data sets.

Live Lectures

Live lectures allow you to ask your instructor questions and interact with your classmates.

Small Class Sizes

Small class sizes ensure you have plenty of access to your instructor and can receive personalized feedback on your progress.

Curriculum

Who's it for?

Data Science Essentials is the perfect class for current and recent college and graduate-level students, working professionals, and other data enthusiasts who are looking for an immersive, hands-on experience in fundamental data science techniques.

So if you're:

Interested in data science and want to learn essential skills
New to Python and are looking for a more robust, structured learning program
A data analyst, economist, or researcher who works with large data sets and wants to make analysis easier and more effective with Python
A software or data engineer interested in learning the fundamentals of quantitative analysis
Interested in applying to TDI's data science Fellowship

Then this class is for you.

When is it?

The next class starts on May 25.

It will run for 8 weeks, with classes twice a week on Tuesdays and Thursdays. Classes start at 7 PM ET and run for 2 hours. And we offer open office hours for a half hour each day.

About Data Science Essentials

Prerequisites:

Elementary programming knowledge
Familiarity with statistics

Upon completion of this class, you'll be able to:

Extract, clean, and analyze data using Python
Leverage Python's powerful libraries to build predictive models to make better decisions
Help your employer solve business problems using data-driven insights

Class Outline

Files and Data Formats

Our goal File reading and writing Parsing the data Data formats Conclusion

Object-Oriented Programming

Everything is an object Why classes? Defining a Python class Adding attributes and methods Inheritance Another example of a custom class Multiple inheritance Conclusion

NumPy

NumPy's performance improvements The ndarray class NumPy indexing Operations on NumPy arrays Saving NumPy data files Conclusion

Introduction to Pandas

Pandas Series Pandas DataFrame Loading data into pandas Pandas indices and selecting and slicing data Adding and dropping data Filtering data Combining multiple data frames Sorting data frames Dealing with missing values Conclusion

Data Transformation and Aggregation with Pandas

Data transformations and function applications String operations and transformations Aggregating data by groups Automating and repeating the analysis Styling data frames Exporting data frames to CSV or Excel files Pandas best practices Conclusion

Time Series with Pandas

Datetime objects Time series data Loading time series data Moving window functions Time series forecasting Conclusion

Data Visualization

Histograms and distribution plots Bar plots Scatter plots Heatmaps Scatterplot matrices Visualizing time series Exporting plots Conclusion

Text and Regular Expressions

Our goal Regular expressions Basic patterns Raw strings and metacharacters Repetitions Capturing Specifying alternatives Text substitution A more complicated regex example Verbose regular expressions Conclusion

What is Machine Learning?

A single state Repeating our calculation for each state Machine learning in Python

Introduction to Machine Learning

Machine learning basics Types of machine learning Training a supervised ML model Choosing a family of models Defining a cost function Finding the optimal model Conclusion

Scikit-learn API

Classes vs. objects Predictors Transformers Pipelines Conclusion

Regression

Linear regression Linear regression with a single feature Regression metrics Linear regression with multiple features Optimization Adding features Regularization Conclusion

Bias, Variance, and Overfitting

Train a decision tree model In-sample error Out-of-sample error Bias-variance tradeoff Cross-validation strategies Grid search for tuning hyperparameters Test data set: evaluating the final model Conclusion

Introduction to Classification

Customer classification Logistic regression Classification metrics: accuracy Classification metrics: precision and recall Other classification metrics Probabilistic models Multiclass classification problems Conclusion

Classification: Predicting Customer Churn

Customer data Encoding Categorical Variables One-hot encoding of all categorical columns Training a classifier Improving the model: random forest classifier Model comparison Dealing with unbalanced classes Conclusion

Dimensionality Reduction

Principal component analysis (PCA) Using real-world data PCA in practice - scaling the data Principal axes and explained variance Choosing number of components to use Interpreting the components Conclusion Appendix: PCA in detail

Clustering

K-Means clustering Metrics for clustering Choosing the number of clusters Clustering on customers data Conclusion

Scikit-learn Workflow

Remark Categorical features Combining features using ColumnTransformer Building custom scikit-learn classes Building a custom transformer using LATITUDE and LONGITUDE Using FunctionTransformer to build stateless transformers Feature unions Imputation A fancy display... Conclusion

Sign up

Sign up for the Data Science Essentials class

The next class starts on May 25 and will run for 8 weeks, with classes twice a week on Tuesdays and Thursdays. Classes start at 7 PM ET and run for 2 hours. And we offer open office hours for a half hour each day.

Tuition for Data Science Essentials is $2895. But if you purchase your class by 5/11, you'll receive a $1400 discount!

Please enter your contact information to receive more information.

Testimonials

See what some of our alumni have to say.

"The data science skills I sharpened at The Data Incubator helped me analyze diversity in STEM education, model SaaS stock prices, and compare industry growth rates. The instructor's background in quantitative finance made for relevant use cases that benefit not only data scientists, but also data-driven finance people like myself."

Christian Templeton, Google

Formerly Salesforce, Symantec. Accepted to The Data Incubator Fellowship.

"From day one I was getting my hands dirty working with data using industry-relevant tools. Having completed the program I'm now better equipped to manage engineering and product teams, and able to conduct sophisticated ad-hoc analysis. Everyone at TDI blew me away; truly exceptional."

Michelle Miller, Hack Reactor

Formerly Berkeley, Smithsonian

"Their heavy focus on applied learning meant that I was working on real data and solving real problems right from the start. While lectures were a valuable component of their courses, the real learning took place while working on projects. These projects were practical, engaging, and instructive, and generally great opportunities to get hands-on experience with what I had learned in the lectures."

David Kwon, Yelp

Formerly University of California, Berkeley

Frequently Asked Questions

Who is this class designed for?

This class is designed for anyone who would like to learn the essentials of data science. Students will gain hands-on experience working with some of the most in-demand big data technologies and leave ready to kickstart their data science careers.

When is the next session? How can I join?

You can find the dates of upcoming sessions and register via The Data Incubator's Eventbrite page.

What is the approximate time commitment for this class?

Data Science Essentials includes five hours of lectures each week. You'll also want to allot extra time for reviewing lectures and working on the associated projects; we generally recommend setting aside at least five additional hours per week for this, but it's up to you how much time you'd like to put in.

Does this class offer job search support?

As Data Science Essentials is intended to add a set of skills for students who are currently employed full time, we do not offer placement or job search support for this class.

Will this class help me be accepted to the Fellowship?

Data Science Essentials can act as prep class for students applying to our Data Science Fellowship, and builds skill sets in two areas essential to the Fellowship: the ability to code proficiently in Python and the ability to apply statistical methodology to datasets.

Successfully complete Data Science Essentials and you'll automatically earn admission into the next TDI Fellowship cohort - and $1000 off of your tuition! *

Should I take Data Science Essentials or apply to the Fellowship program?

The Data Incubator's Fellowship program is a full-time, intensive data science program spanning two months. Fellows in the program should have programming experience, a background in a highly analytical field, and the ability to work independently on programming projects. This course is for people with advanced degrees who want to make a full transition from academia to industry-focused data science.

Data Science Essentials, on the other hand, is a part-time, online program designed for people who'd like to learn the essentials of data science to augment their current skill sets. The class is centered on data wrangling and analysis using Python and is taught by a live instructor. Prior coding experience is definitely a plus, but not required (see next section).

What are the prerequisites for the Data Science Essentials program?

There's no official skill set required for this class, but a basic understanding of the Python language is strongly encouraged. If you don't have any experience with Python, we recommend you complete Codecademy's free Python basics courses before beginning the Essentials program. With that said, we've had successful students in the past who came to us with no prior coding experience.

What practical skill sets can I expect to have upon completion of the class?

Data Science Essentials students will learn how to programmatically work with large, real-world datasets to solve business problems. Over the course of eight weeks, students learn how to extract, clean, and analyze data using Python. Students will leverage Python's powerful libraries to build predictive models to make better decisions, and help employers solve business problems using data-driven insights.

With loads of data you will find relationships that aren’t real.
Big data isn’t about bits,
it’s about talent.

Forbes Magazine

The Data Incubator