During your first 60 hours as an official Ironhack Data Analytics student you will be laying the foundation for success in Remote. During the prework phase you will:
1. learn the fundamentals of command line, Git, Python, MySQL and statistical analysis
2. familiarize yourself with the basics of programming and statistics and
3. connect with your peers and expert academic staff by utilizing our Slack channel.
Once you’ve completed the prep materials and synced up with your class, you’ll be ready to dive into the course!
The first two weeks will introduce you and your classmates to the world of data analytics. Then you will establish your development environment for the classroom as you settle into our tight-knit data community.
- Introductions to data wrangling/cleaning.
- APIs and web scraping.
- Intermediate levels of Git, SQL and Python.
Week three calls for your first project as an apprentice data analyst! Apply your new Python skills by conducting data analysis with real datasets.
In weeks four and five you’ll take a deeper look into the mathematics behind data analytics.
- Utilizing Python to understand inferential statistics and probability.
- Incorporating Python into the fundamentals of business intelligence.
- Learning story-telling techniques in order to visualize your data and insights in presentations.
Week six will mark the start of your second project: a complete data analysis. This will be constructed from data that you will have processed, cleaned and visualized from real datasets!
The final module will introduce you to the fundamentals of machine learning in weeks seven and eight. We’ll start things off by teaching you to understand the machine workflow, and the lessons will only expand from there.
- Both supervised and unsupervised learning.
- The essentials of popular machine learning algorithms.
- Building, training and evaluating models with the Scikit-Learn machine learning library.
For the last week of this module and course, you will face your final and most challenging task: building an end-to-end machine learning project. You will need to process a dataset, extract features, train a model, and use that model to make predictions on new data. When your project is complete, you will compete alongside your fellow students in our Hackshow.
We are committed to helping you start your career in tech. Our financing options range from traditional loans to pay-for-success models so you can choose the option that best fits your needs. Simply get in touch with our admissions team and they will give you more information about the available options.
Himanshu has a master’s in industrial engineering with a focus on data analysis. He has over four years of experience in tech and has worked on projects across a diverse spectrum of industries including education, healthcare, finance, information and technology, tourism, and manufacturing. This has led Himanshu to develop strong skills in statistics, big data analytics, machine learning, time series analysis, operations research, database management, optimization and data visualization.
In the online Data analytics bootcamp, you will study from 9 a.m. to 6 p.m., from Monday to Friday. Your schedule will consist of four 45-minute Instructor-Led Class Sessions, three 45-minute Independent Student Work sessions, and one 60-minute block of open office hours.
9:00 - 9:45Instructor-led Class Session
These Instructor-Led sessions last for 45 minutes. These are led by the instructor and include a mixture of lecture, demos, and short checks for understanding (Kahoot, mini-activity, cold-calling students, etc)
9:45 - 10:00Break
During your break, both students and staff should leave their computer, stretch and have a snack!
10:00 - 10:45Independent Student Work Session
During the self-study sessions, you should focus on doing your independent work. Take this time to meet with your project team, read through content in the student portal, write a summary or work on labs. Our TAs will be available via Slack to provide support and help.
10:45 - 11:00Break
11:00 - 11:45Instructor-led Class Session
11:45 - 12:00Break
12:00 - 12:45Independent Student Work Session
12:45 - 14:00Lunch
14:00 - 14:45Instructor-led Class Session
14:45 - 15:00Break
15:00 - 15:45Independent Student Work Session
15:45 - 16:00Break
16:00 - 16:45Instructor-led Class Session
16:45 - 17:00Break
17:00 - 18:00Open Office Hours
During these 60 min sessions, TAs will be available online. Student attendance is not mandatory and instructor attendance is not required. This is an opportunity for students to drop-in and ask questions or seek help with homework and/or labs.
Studying a remote Data analytics bootcamp has a lot of benefits! Read the reviews of our bootcamp students on our transition to remote learning
Would you like to find out what is it like to study a remote Data analytics Part-time course? Hear from our Part-time students on our transition to remote learning
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