Congratulations and welcome to Lambda School! We're so excited you took the first step toward changing your life, and the skills you learn here will set you up for success in your future career, so let's dive in! Loading data is a fundamental, and deceptively challenging, step. Getting it in the right “shape” and format for analysis and modeling is always a challenge. In the first week of Unit 1 we’ll practice these skills, and learn to appreciate the many tools Python gives us for these tasks.
Next, we'll dive into statistical tests and experiments. An important application of statistics is designing and evaluating experiments. In the context of web applications, often this means an A/B test where users are exposed to different versions of a site and their behavior/outcomes compared.
How do you design a good, and valid, experiment? How long should you run your experiment? How do you evaluate the outcome of an experiment? How do you balance all this math and science with the practical business and product concerns you’re working with? These are the sorts of questions we’ll discuss in this sprint.
Then, we'll begin to learn Linear Algebra, which is the foundation of nearly all the numerical routines used for practical statistics and machine learning.
Lastly, you'll apply the knowledge you've gained in Unit 1 to complete your own data storytelling project! Create a notebook and publish a blog post for your personal portfolio. Great job! You completed Unit 1!
You will learn...
Statistical Tests & Experiments
Get to know your field
Welcome to the beginning of your professional journey into Data Science. Over the next few months it’s important you learn more about the track you’re pursuing as a whole, not just the technical skills needed to perform work in your field. This includes beginning to follow blogs and individuals within your field. Here’s a few resources to help get you started.
Data Science exists at the intersection of many different skills and abilities, including coding, math, and industry-specific domain knowledge. If possible, take some time to focus on each of these areas in isolation before trying to put them all together.
Learning to program is the same as learning a new language – its impossible to learn by reading about it in a book. You need to speak it (or in this case write it). You will need to get comfortable with not know all the answers and making mistakes. Most of all, if you want to be successful in Unit 1, one get ready to work hard and keep putting in the practice hours even if you don't yet understand all of the concepts completely.
Now that you've gotten warmed up, let's add more skills to your toolbox!
Unit 2 is about Predictive Modeling, also known as "supervised machine learning" with labeled, tabular data! We’ll begin our study of predictive modeling with linear models for regression tasks, including ordinary least squares regression and ridge regression. We can also make models to predict discrete classes, and answer questions like “Is this A or B or C?” We’ll continue our study of predictive modeling with a linear model for classification tasks, called logistic regression.
Next, we'll continue our study of predictive modeling with tree-based models, such as decision trees and random forests. We’ll also learn how to clean data with outliers, impute missing values, encode categoricals, and engineer new features. This sprint, your project is about water pumps in Tanzania. Can you predict which water pumps are faulty?
Then you will choose your own labeled, tabular dataset, train a predictive model, and publish a web app or blog post with visualizations to explain your model. You will use your chosen dataset for all assignments during the Applied Modeling sprint.
You’ll learn how to define machine learning problems, begin the modeling process, choose targets, choose evaluation metrics, and avoid leakage. You’ll improve your model predictions with powerful models like gradient boosting and feature selection techniques like hyperparameter optimization. You’ll improve your model interpretation with insightful visualizations like partial dependence plots and Shapley value force plots.
You will learn about...
Networking prep and informational interviews
In Unit 1 we encouraged you to start getting to know your field. Now, in Unit 2, you should begin reaching out to people in the tech industry to start building relationships with professionals who can mentor you. The relationships you build today will be essential in connecting you with job opportunities when you're ready to job search at the end of the program. One way you can begin to build these relationships is through informational interviewing. Use the resources below to help guide you.
You've made it to Unit 3 – awesome job! Being a data scientist means applying statistics and analysis of data, writing real working code that runs and gets results. You’ve been doing that your entire time at Lambda School, but much of our work has been in the land of Python notebooks, a useful but limited environment intended for exploration, not engineering. Some place a divide between science and engineering – theory and practice, ideas and application. A skilled data scientist masters both: science informs engineering, and engineering increases the rigor of science by making it reproducible and scalable. In this unit, we will go beyond Python notebooks, into the world of modules, packages, containers, and more.
Next, we'll begin learning SQL. Most modern data is generated automatically by human interaction with a web-backed application – every app they download, every click they make, all travels over a network and is saved by the server. Though in the rawest of forms this may be a log file, in most cases where it really goes is a database. These databases are commonly accessed using SQL – Structured Query Language – a standard based on relational algebra, and a useful tool known not just by data scientists but by software engineers, MBAs, and more. Lastly, in the final part of this unit you'll learn about the cloud and what it really means to build and deploy something "to the cloud." You'll finish off Unit 3 by working with other Lambda students to build a real application that incorporates data science.
You will learn...
SQL and databases
Productization and Cloud
In the last unit, you focused on an individualized approach to networking. For this unit let’s expand your networking skills to events. There are likely groups or regular events available in the cities or towns where you’re hoping to apply for jobs. Start making yourself a part of the tech communities you hope to be a professional in. Here are some resources to support you as you take this step.
Now that you have completed a few informational interviews, spend some time going to meetups or conferences on topics that interest you. This is a great way to learn more about the diverse field of data science.
Raw Python proficiency should be your top priority. The more comfortable you are with Python the more free you will be to focus on higher-level data science intuitions rather than code-specific symbols and syntax.
Unit 4 means you're officially halfway through the Lambda curriculum! Take some time to stretch, step away from your screen, and remember why you're working so hard, and then it's back to class. In Unit 4, you'll begin learning about Natural Language Processing and what that means for a data scientist. Human language is the main form of content on the internet, and the ability to computationally process it at scale can lead to many compelling products. A brand may want to track the sentiment of users towards them on social media before and after an advertising campaign, or a news service may want to recognize key entities in a news story to generate a high-quality automated summary. But text is not numbers, and even representing it as ASCII/Unicode values doesn't capture the meaning, just the abstract labeling of symbols. How can we hope to achieve these sorts of tasks?
In this unit we'll learn NLP (Natural Language Processing) techniques, including cleaning and preprocessing, which can then allow us to feed the data into the more traditional statistical models we are familiar with.
Next, we'll study a few of the most effective recent innovations in neural networks and deep learning and think a bit about what the future may hold. Is deep learning the path to artificial general intelligence? Probably not – but it's a pretty useful tool along the way.
At the end of the unit, you will know a variety of powerful techniques for modeling and predicting data, and will build a real working application using data science with students across the school.
You will learn...
Natural Language Processing
Neural Network Foundations
Models and Architectures
LinkedIn and interview prep
In Unit 4 you're almost done with your core curriculum. Now is a good time to get your LinkedIn up and running (if you haven’t already). There are a lot of skills you can begin to add to LinkedIn to start catching the attention of recruiters. Starting in Computer Science you'll be picking up speed with interview practice, so we’re including a few related resources on behavioral interviewing as well.
How to Build an Amazing LinkedIn Profile
Interviewing and STAR Stories
Tailoring a resume, cover letter, or your interview prep
The Complete Data Science LinkedIn Guide
Tips for Data Science Interviews
Tips for Unit 4
By practicing the foundations of a language, you are building the skills to rapidly grasp new concepts. Learning new things is based on understanding the smallest pieces.
Awesome job! You've completed the first 4 Units, and now it's time to dive into Computer Science. You've got this! In the first week you'll begin to learn Python, including functions, built-in data structures, classes, modules, and flow control.
Next, you'll learn how to formally think about and solve algorithmic problems as part of the Algorithms module. Some classic algorithmic paradigms will also be introduced, as well as time and space complexity and Big-O notation. Then you'll begin to get familiar with fundamental data structures, such as linked lists, queues, and binary search trees.
Now that you've begun to understand the basics of CS, we will be looking at one of the speediest structures in the Lambda School curriculum: Hash Tables. Additional study will include applications of hash tables and hashing functions. Then, we will explore how to implement graphs, and several of the algorithms surrounding graphs to how they can be applied to solving real-world problems. Finally, you will apply the knowledge you've gained during Computer Science to build a comprehensive program and practice interview techniques. Nice job, you've finished the CS portion of Lambda School!
You will learn...
Practice your technical interview skills
In CS you'll begin to practice real interview settings. It can feel stressful at first, but you'll get the hang of it with enough practice. Use the resources below to help you along the way. By the end of CS you should be familiar with expectations and structure of technical interviews. You'll continue to practice them in the next unit, Labs, and you'll be required to pass a technical and behavioral interview in order to graduate. Use the resources below to help. It can be hard, but practice makes perfect!
Use UPER (Understand, Plan, Execute, Reflect). It is THE tool to get you unstuck from any problem. That's what it's for. Replace frantically searching for example code with using UPER, and you will change your experience little by little each day.
Learning new languages is a practicable skill, like anything else. The key to success in this unit is to understand the problem and come up with a plan before you start coding.
Labs is all about gaining real work experience and learning to work in cross-functional teams.
In the real world, your team might have designers, mobile developers, data scientists, marketers, project managers, front-end web developers and back-end web developers. Cross-functional team members can often have conflicting priorities and objectives. To prepare you for this environment, you will be grouped in cross-unit and cross-functional teams to collaborate together toward a shared product goal.
Remember that the skills you build in labs will make great anecdotes for future job interviews, so be sure to take notes on what went well and what you learned. During Labs you will...
Contribute meaningfully to a software project with a team of peers and practice all phases of the Software Development Lifecycle.
Experience product development that solves real user problems.
Demonstrate a proficiency of teamwork, individual contribution, problem-solving, and professionalism.
During your labs experience you’ll receive career lessons related to your projects and team experience. These lessons will help you work towards professional endorsement as well, as you’ll receive a challenge to complete a required career artifact. You’ve already received lessons on the artifacts prior to entering labs, so the hope is you already have a working draft of your resume, a LinkedIn profile, and an active GitHub. Below are details of the expectations you’ll need to fulfill to achieve your professional endorsement.
Action Verbs for a Technical Resume - Data Science Focus
The best teams take ownership as early as possible in all aspects related to planning, communication, organization, and coding. Whether or not the project succeeds is dependent on what you do on Day 1.
Labs is a collaborative, team experience. The most successful students over communicate with their teams, and practice talking about their work.
Labs will be your first experience on a real product team and it's important to face the challenge head-on. Remember that Labs is not only about building a project, but it's also about building an experience.
You made it to the last stretch of the Lambda School experience! Job Search is designed to provide you with ongoing structure and accountability so you can keep sharpening your skills in real product development while searching for a job.
Congratulations! You're a Lambda grad! During Job Search you will have...
Opportunities to work as a developer alongside designers and data scientists to continue to gain real team experience;
Opportunities to apply for paid and non-profit internships and externships; and
Help with sourcing job opportunities and pre-interview coaching.
You may be done with your track experience, but now is the time to gear up for the job search marathon. Job Search is designed to provide support to active job seekers. As you begin your job search, here are some resources that will help you. Remember, the job search is a balance of upkeep technical skills, while also utilizing all the career skills you’ve been learning at Lambda in the application and interview process.
Cold Outreach & Networking During the Job Search
Lambda Job Board
List of Job Boards
Negotiating an Offer
Tips for Job Search
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