Become a Data Scientist_
Businesses are relying on data insights and actionable intelligence more than ever to make informed decisions. As a Data Scientist, you will turn data into stories and informed presentations that will drive their success. Learn exactly what your future employer needs and become a critical business asset in just 20 weeks.
Virtual Transitioning Into Data Science Panel_
Looking for your career in data science? Join us for our Transitioning Into Data Science panel on April 7th at 6 PM (CST). Hear from our alumni, who come from various backgrounds and learn about how they successfully became a data scientist. Come ready with all your questions because our staff and alumni are here to help you on your career journey!
- Applied Statistics
- Time Series Analysis
- Anomaly Detection
- Language Processing
- Dist. Machine Learning
- Advanced Topics
- Storytelling with Data
- Domain Expertise Dev.
- Career Simulation
- Capstone Project
What does a Data Scientist do? Learn the types of questions that data scientists answer every day, and how they answer them. Discover how to use the data science pipeline and machine learning methodologies to provide value to businesses and their decision makers. These include statistical and mathematical concepts, Python, Jupyter Notebooks, Linux / MacOS Terminal, SQL, Microsoft Excel & Google Sheets, Tableau, Git, and Hadoop technologies. Prerequisite: Admission to program
Learn how to use applied statistics to solve practical problems. Decide what data to collect and how to collect it. Then, analyze and interpret your data and present your results. Explore the fundamentals of applied statistics in Data Science using Excel, including measures of central tendency, tests of significance, common distributions, and variable independence. Prerequisite: Fundamentals of Data Science
How your data is stored matters. Use MySQL to gather, parse, clean, aggregate, and store data. Learn how to read data from SQL databases and perform basic joins, aggregates, and group-bys. Write tables, export data, and explore database structure and schema. Prerequisite: Applied Statistics
Learn to use Python to make raw data more valuable for analytics through, data wrangling, data analysis, data visualization, and machine learning. Explore how to use packages such as Pandas, NumPy, SciPy, Scikit Learn, and Matplotlib. Prerequisite: SQL
Learn to prepare data with regression algorithms to predict numerical events. You will build on your previous skills from past modules to gather and prepare, parse, package, and analyze data from a relational database. You’ll learn new skills such as indexing, selecting, plotting, and linear regression, as well as how to evaluate the performance of regression models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case.
Learn to predict data classes with classification algorithms. Gather data from multiple source types such as csv and SQL. Develop a classification model to predict categorical events. Explore topics such as grouping, aggregations, computational tools, text data, missing data, DataFrame objects, plotting, data visualization, and how to evaluate classification models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case. Prerequisite: Regression
Use unstructured machine learning and clustering algorithms to find unknown patterns in data. Use clustering algorithms to identify similar groups using Python and Scikit-Learn and explore how they can be used for further analysis and modeling. Prerequisite: Classification
Use structured machine learning to employ forecasting methods and predict events over time.. You will practice with a practical application and raw data to learn how to develop, evaluate, and improve performance of the model. Explore concepts such as time dependency, accounting for seasonality, and how to effectively split data into training and test sets. By the end of the module, you will develop a time series model and deliver a final report, model, and predictions. Prerequisite: Clustering
Learn how to detect rare or anomalous events.Students will learn methods for detecting rare or anomalous events. You’ll practice building an anomaly detection model using Python. Explore streaming and unstructured data (such as logs), regular expressions, and how to apply data science to cyber security. Prerequisite: Time Series Analysis
Use Natural Language Processing techniques to perform common tasks such as sentiment analysis and topic modeling. Use Python’s NLTK package (or an equivalent) to analyze the sentiment of tweets related to a particular subject. You will also learn how to access data from a public API, such as Twitter. Prerequisite: Anomaly Detection
Learn how to access distributed data from cloud platforms. Work through the data science pipeline — from gathering data through the deployment of a machine learning model. You will apply previously learned machine learning methodologies using technologies such as Spark and Hive. Understand the Hadoop framework and how to access data to develop data products, such as machine learning models. Prerequisite: Natural Language Processing
With a solid understanding of the fundamentals, experience advanced data science topics such as model pipelines, A/B testing in machine learning, graph analysis, recommendation engines, R, deep learning, deployment of production models to the cloud, and NoSQL databases. Learn use cases, key concepts, and resources for diving deeper into these topics. Prerequisite: Distributed Machine Learning
Data Science is often defined as the intersection of programming, mathematics & statistics, and business or domain expertise. However, most data scientists will switch industries during their career, and the ability to adapt quickly into a new domain is critical to maintaining the data science trifecta. In this module, you will learn frameworks for learning what domain knowledge is most relevant to their work and for quickly acquiring the skills needed to start adding value. Prerequisite: Storytelling with Data
Throughout the program, you’ll learn a variety of skills related to career preparation and professional development. By the time you graduate, you will possess soft skills such as team-building and communication, as well as career development skills such as resume writing, online branding, and interviewing. We also teach best practices in project / time management, ethics, big data architecture, and portfolio development in Kaggle, Data.world, and Github. Prerequisite: Fundamentals of Data Science
Work in a small team to complete a real-world capstone project that uses each skill you’ve learned throughout the course. Prerequisite: Domain Expertise Development
A Day In The Life_
Before class, grab yourself a free cup of coffee and a snack, review the upcoming curriculum, practice some programming exercises, and hang out with your classmates!
At 9 AM, Instructors will get you warmed up with morning announcements, review problems, and challenge questions.
Instructors will guide you through the day’s new content through a combination of overview, explanation, live coding demonstrations, and practice problems.
– 11AM Lab
At the end of each lesson, the class will work through a set of exercises to practice and reinforce the content they’ve just been taught. Instructors and students collaborate to problem solve, give feedback, and complete the problem set.
From 12:30-1:30, enjoy your hour lunch in our student break area or at a nearby downtown restaurant.
During Thursday lunches, we bring in a guest speaker to talk about the tech industry. You’ll hear from developers, recruiters, hiring managers, and Codeup alums.
Presenting your findings in front of colleagues is crucial in the data science world. At the end of each module, you’ll have the opportunity to model and present data that showcases your new-found skills. Practice makes perfect!
Wrap up the class day at 5 PM with some review, Q/A, and heads up for the next day.
Spend an hour reviewing the day’s content and practicing more problems. Or, meet up with fellow students or alumni to form a study group or practice presentations.
DOWNLOAD OUR OUTCOMES REPORTS_
Looking for a new and exciting career opportunity in tech? Check out our data science reports to learn more about how you can launch a career as a data scientist with Codeup!
Guided Admissions Process_
Our guided admissions process is the perfect time to find out if Codeup is the right fit for you and your goals. Our students come from all walks of life with a variety of educational backgrounds and work experiences, and there is no programming experience required for our Data Science program. If you are a motivated, professional, critical-thinker, we’re excited to meet you.
In the past year, 88% of our students have received financial assistance. Discover a variety of financial solutions and scholarship opportunities to fund your future.