Study Data Analytics Online and Launch Your Tech Career

Once you become an official Ironhack Data Analytics student, you will be given online learning material focusing on command line, Git, Python, MySQL, and statistical analysis. To get you ready for learning Data Analytics, this essential prework will familiarize you with the basics of programming and statistics necessary for the bootcamp. You'll also learn how tools like ChatGPT work, and how to properly leverage them to enhance your learning and problem-solving abilities, which will serve you well in both your bootcamp and your career.

With the prework under your belt you’ll be more than ready to take on your bootcamp, no matter how much or how little tech knowledge you walked in with.

In Module 1, we'll dive into Python programming, data wrangling, and retrieval techniques to lay a solid foundation for your data analytics journey.

In the first module, you will immerse yourself in Python programming, including data structures, flow control, functions, error handling, and object-oriented programming. You will also gain proficiency in regular expressions, Git version control, and advanced Python concepts like lambda functions and comprehensions. This foundation sets the stage for the practical application of your programming skills in data analysis.

The second part of the module will focus on data analysis using Pandas, showing you the key skills in data cleaning, transformation, organization, and manipulation. You will learn about web structure, APIs, scraping data from websites, conducting basic exploratory data analysis, and identifying data-driven business problems.

The module culminates in a hands-on project where you will utilize your newfound knowledge to tackle a data wrangling challenge, employ web scraping techniques, and harness the power of APIs. Along the way, you will also develop proficiency in Git and command-line tools, essential for collaborative data projects.

Module 2 focuses on SQL for querying data and dives into data analysis techniques, including advanced exploratory data analysis (EDA) and inferential statistics.

In this module, you'll explore databases and SQL. You'll start with basics like what a database is and how to create one using MySQL. You'll learn about tables, relationships, and how to write SQL queries. As you progress, you'll tackle more advanced topics like subqueries, views, and functions. You'll also discover how to connect Python to databases and work with data in new ways.

At the end, you'll be able to analyze data using SQL to gather insights. Going back to Python, you will go deeper into doing Data Analysis. You'll learn how to examine data, visualize it, and draw conclusions using descriptive and inferential statistics. You'll explore Tableau for making dashboards and presenting the data to business stakeholders for decision making.

The module concludes with a project where you'll apply your skills to manage data, perform analysis, and create reports using Tableau.

Module 3 explores the fundamentals of machine learning (ML) and artificial intelligence (AI) to take your data analytics skills to the next level.

You'll finish off your bootcamp by learning the fundamentals of machine learning. You’ll develop an understanding of the ML workflow, supervised and unsupervised learning, and the essentials of popular ML algorithms. Then you will build, train and evaluate models by learning to use the scikit-learn ML library. You'll learn how to build an end-to-end ML project, by processing a data set, extracting features, training and using a model to make predictions on new data. By the end of this module, you will be prepared to apply ML to real-world data sets!

This is also where you'll be introduced to cloud platforms, and deepen your understanding of AI. You'll learn about NLP, LLMs and tools like ChatGPT, understanding how to use them to improve your projects and your transferable skills. You will also learn about Deep Learning, which is the ground for all AI, and Time Series analysis. Your final project will be your most complex, challenging you to apply your newfound knowledge in innovative ways.

Once your project is complete, you will compete alongside your fellow students in our Hackshow, and will have something concrete to put in your new portfolio and show off in your upcoming job interviews.

In Module 4, we'll take what you've learned about Machine Learning and dive into how it's used in real-world applications with Unsupervised Learning and Deep Learning. You'll also get hands-on experience with tools like Streamlit, which helps you create simple user interfaces for your projects, and Google BigQuery, a powerful cloud-based tool for managing and analyzing large amounts of data.