Picture this: You need a data science bootcamp to jump start your career. Where do you start? Join me as I give an insider review of TripleTen’s data science bootcamp. But why TripleTen? Good question! For starters, their data science bootcamp is accessible to absolutely anyone, as there are no prerequisites. And it’s not just about learning data science — it’s a comprehensive journey from fundamental concepts to advanced data manipulation and machine learning techniques, all while getting hands-on to gain real-world experience. But one of the biggest standouts for me is the fact that they have an 86% hiring rate and a money back guarantee if you haven’t landed a job within 6 months of graduating. Whether you want to formalize existing skills to make the transition to a bonafide data scientist, or you’re passionate about data and have no existing skills, TripleTen’s bootcamp can get you where you need to be in just 8 months. So, let’s dive in to see just what this data science bootcamp is like behind the scenes! Get More Information
Overview of TripleTen’s Data Science Bootcamp
To kick things off, I’ll cover the structure of TripleTen’s data science bootcamp, including what you can expect to learn and when. And remember, this bootcamp is designed for anyone with an interest in data, which means you don’t need any coding, math, or data science background to take and pass the program. Just 8 months of consistent effort, and you can go from zero knowledge to a fully-fledged data scientist.
I think that’s very cool, and that’s not to mention various other benefits like a money-back guarantee if you aren’t able to find a job. But more on that later… Ready to sprint..?
First, rather than opting for standard modules, you will tackle the various segments in sprints. Depending on your previous experience, this might be new to you, but in tech, it is an Agile management technique that tends to be the de facto standard. Put simply, a sprint is a short, time-boxed period during which a specific set of work has to be completed and made ready for review. And typically, a sprint lasts from one to four weeks, with two weeks being common, hence why the TripleTen sprints are each set at two weeks long. I really love this idea because sprints help you focus on specific tasks within a defined timeframe by breaking down work into smaller, manageable chunks. It also means that you get some real-world practice working in a pseudo-Agile way, which is ideal for prepping for a job in the actual industry. So, now you know that you’ll be working in 2-week sprints, what will you be learning? Well, there are a total of 17 sprints in the main boot camp to be completed, but before you dive into those, there are 4 preparatory sprints designed to help students with zero background in math or coding. Remember when I said this is designed for anyone and everyone with a passion for data science? With this in mind, the idea of these prep sprints is to help you gain the foundational skills and knowledge you’ll need to succeed in the main boot camp.
Here’s a summary of what these 4 intro sprints cover:
1. Basic Python: Begin with the essentials of Python programming, focusing on key concepts for data science, including basic syntax, data types, control structures, and practical applications. This sprint lays the groundwork for data science projects by emphasizing hands-on learning with coding tasks and case studies.
2. Computer Literacy: Get a comprehensive introduction to the world of computers, including hardware, software, operating systems, and maintaining computer health. You’ll also cover internet basics, effective browsing techniques, and online safety.
3. Math Course: Get to grips with essential math concepts, including natural and integer numbers, variables, exponentiation, logarithms, common fractions, scientific notation, and percentages. This is ideal if you haven’t done any math for a long time because, after all, data science relies on math!
4. Sandbox: Get to grips with the TripleTen coding environment with guided case studies on Python fundamentals, working with data types, arithmetic operations, creating functions, and handling data issues like missing values and duplicates.
I really appreciate the extra thought that TripleTen has put into these introductory sprints, as they’ve been clearly structured to help absolute beginners build a solid foundation in programming, math, and computer literacy. Plus, they require you to get practical and hands-on right away. I am a big advocate of this approach, as data science is a hugely practical discipline.
Main content
Okay, now let’s cover the 17 main sprints that comprise the bulk of this boot camp. As I mentioned earlier, the idea is that you’ll take 2 weeks per sprint. This means you’ll need around 34 weeks to complete the main bootcamp. But I should be clear: this is not 34 full-time work weeks of 40 hours. TripleTen recommends that you set aside 20 hours per week to stay on track. Of course, if you want to take these at a faster or slower pace you can, as you won’t be attending live classes or seminars. But if you can, I’d recommend following the suggested pace, as this gives you plenty of time to absorb the content without running the risk of burning out if you go too fast or losing interest if you go too slowly. That said, I know that many of our readers have varied circumstances, whether they have a full-time job, parenting duties, or other commitments. So, my advice will always be to choose the pace you can sustain consistently, as this will be the key to your success.
Let’s dive into these 17 sprints and what they cover. I should also point out that each sprint culminates in a project to cement your newly gained skills while building your portfolio.
Sprint 1 – Working with Data in Python: Learn how to handle, manipulate, and analyze data with Python by covering dictionaries, lists, and complex data structures before diving into functions and built-in methods to create reusable and efficient code. You’ll also get an intro to Jupyter Notebook, a pivotal tool in data science for writing and sharing code.
Sprint 2 – Exploratory Data Analysis (EDA): Hone EDA skills in Python by focusing on essential data handling techniques, master data cleaning, learn how to tackle missing values and duplicates, and create dynamic visualizations with matplotlib. You’ll also dive into DataFrame manipulation, enrich datasets with new columns, and master aggregation and data merging strategies.
Sprint 3 – Statistical Data Analysis: Explore the essentials of statistical analysis by learning to work with continuous and discrete data while also getting hands-on with techniques like location, variance, and standard deviation. You’ll also expand your knowledge of probability and probability distributions while enhancing your skills in statistical inference via hypothesis testing and population means analysis. Plus, it’s nice to see a focus on professional skills like communication and teamwork.
Sprint 4 – Software Development Tools: Dive into essential software development tools and practices, emphasizing the terminal and command line across MacOS, Windows, and Linux. You can expect to dive deep into file management, command execution, and advanced editing techniques at the command line while also getting to grips with version control with Git.
Sprint 5 – Integrated Project 1: By this stage, you’ve reached the first major milestone in this boot camp. The idea here is to consolidate the first 4 sprints with a comprehensive real-world project where you apply everything you’ve learned while also testing your ability to create documentation, handle errors and debugging, and effectively synthesize information.
Sprint 6 – Data Collection and Storage (SQL): Gain expertise in web data management by exploring web mining, GET requests, and parsing HTML using regular expressions. You’ll then transition to SQL to learn database fundamentals, execute statements, and tackle advanced operations like data slicing, aggregate functions, window functions, and SQL joins. You’ll wrap up with an introduction to PySpark for managing large datasets and executing complex SQL queries within DataFrames.
Sprint 7 – Introduction to Machine Learning (ML): Now it’s time to dive into ML. You’ll start by learning how to define business tasks and how to use training datasets. Then, you’ll cover supervised learning via classification and regression with Scikit-Learn before tackling challenges like randomness in algorithms, managing test datasets, and mastering evaluation metrics. You’ll also…
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