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CORE: Data Science and Machine Learning.

Roadmap Resource Index

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A prioritized list of core skills, reading material, personal portfolio projects and practice assignments every new data scientist should have.

This repo is a companion site for the course CORE: Data Science and Machine Learning.

The goal here is very ambitious; to be the only reference site you need on your inital learning journey as a data scientist. Getting started in data science is hard. There is an overwhelming number of resources and suggestions. Many people give up!

The goal of this road map is to get you started (or re-directed) on your journey the right way with NO knowledge gaps. Everything from original content to linked resources has been vetted through experience and practice.

Data jobs come in three general flavors (but are often called different things in practice); Data Analyst, General Data Scientist, and Machine Learning Engineer. The skills, course, and resources are divided into these categories for ease of reference. Pay close attentioin to recommended books to add to you library, several are must reads.

Table of Contents START LEARNING HERE - Combined Core Skills List Certification Checklist Portfolio Project Checklist Foundations Library Recommendations Data Science Communities Public Data Resume Tools Interview Prep Data Analyst Library Recommendations Cheatsheets Spreadsheets SQL Business Intelligence General Data Scientist Library Recommendations Cheatsheets Probability and Statistics R Resources web-development Machine Learning Engineer Library Recommendations Cheatsheets Python Resources Math for Machine Learning Model Deployment Deep Learning Next Steps Latest News Combined Core Skills List

Start learning at the top of this list and check each skill until you are done. When done you will have gained all core skills required for data science!

This table is the set of skills that should be common to every data scientist. There are many things excluded that become important as individuals specialize. This list represents the absolute foundation.

Role Skill Type Tool Foundational Define data science Soft Any Explain why data science is important Soft Any Give examples of data science projects Soft Any Know how to get public datasets Soft Any Participate in the data science community Soft Any Build a project portfolio Soft Any Data Analyst Explain what a data analyst is Soft Any Understand summary statistics: location, shape, spread, and dependence Math Any Mathematical modeling (linear programming) Math Any Setup MS Excel on Desktop and Cloud Spreadsheet Excel Use operators Spreadsheet Excel Use built-in functions Spreadsheet Excel Import a text file Spreadsheet Excel Use data tables w/ summary stats Spreadsheet Excel Import data from various sources Spreadsheet Excel Lookups and Matches Spreadsheet Excel Understand data visualization concepts Soft Any Data visualization Spreadsheet Excel Build a dashboard w/ KPIs Spreadsheet Excel Import data with Power Query Spreadsheet Excel Use pivot tables Spreadsheet Excel Use the analysis tool pack Spreadsheet Excel Use VBA and macros to automate tasks Spreadsheet Excel Explain what a database is Database Any Understand what tools are required to write SQL Database Text Editor Understand SQL Syntax Database Text Editor Build a SQLite database from scratch Database Text Editor Use SQL Statements: SELECT, FROM, WHERE Database Text Editor Use SQL Statements: BETWEEN, LIKE Database Text Editor Use SQL Statements: AND, OR, NOT, EXISTS, NULL Database Text Editor Use SQL Statements: ORDER BY, DISTINCT Database Text Editor Use SQL Aggregate Functions Database Text Editor Use SQL WITH statement and subqueries Database Text Editor Use SQL for modifying data with inserting, updating and deleting Database Text Editor Understand SQL views Database Text Editor Connect Excel to SQLite and execute SQL from within Excel Database Excel Explain what Business Intelligence is Soft Any Install Tableau Business Intelligence Tableau Use Tableau data types Business Intelligence Tableau Build Tableau visualizations Business Intelligence Tableau Create Tableau filters Business Intelligence Tableau Connect Tableau to external data sources Business Intelligence Tableau Join data in Tableau Business Intelligence Tableau Understand Tableau dates Business Intelligence Tableau Build Tableau visualizations for comparisons Business Intelligence Tableau Build Tableau visualizations for distributions Business Intelligence Tableau Build Tableau visualizations for multiple axis Business Intelligence Tableau Understand Tableau formatting Business Intelligence Tableau Build calculations and parameters in Tableau Business Intelligence Tableau Understand data story telling concepts Soft Any Build Tableau dashboards and stories Business Intelligence Tableau Share Tableau dashboards and stories (with Tableau Public) Business Intelligence Tableau Understand the difference between Tableau Public and Pro Business Intelligence Tableau Data Scientist Explain what a data scientist is Soft Any Explain why using a scripting language is important Soft Any Explain what R, CRAN, and RStudio are Soft Any Install base R R base R Install RStudio R RStudio Use base R calculations R RStudio Understand objects in R R RStudio Understand functions in R R RStudio Understand what an R script is R RStudio Use base R datasets R RStudio Use the help functions in R R RStudio Use base R plots R RStudio Install R packages R RStudio Understand atomic vectors R RStudio Understand object attributes R RStudio Use matrix and array objects R RStudio Understand classes R RStudio Understand factors R RStudio Understand coercion R RStudio Use lists R RStudio Use data frames R RStudio Load and save data R RStudio Select values from a data frame R RStudio Change values in a data frame R RStudio Subset a data frame R RStudio Deal with missing values R RStudio Understand control flow R RStudio Conduct an Exploratory Data Analysis (EDA) using summary stats, and viz R RStudio Explain the difference between base R and the Tidyverse R RStudio Use ggplot mapping aesthetics R RStudio Use ggplot facets R RStudio Use ggplot multiple geom R RStudio Use ggplot stat transforms R RStudio Use ggplot position adjustments R RStudio Use ggplot coord systems R RStudio Use dplyr filter R RStudio Use dplyr arrange and select R RStudio Use dplyr mutate R RStudio Use dplyr pipes, group_by, and summaries R RStudio Use stringer for text manipulation R RStudio Explain what Markdown and RMarkdown are Soft RStudio Build and share an EDA using RMarkdown R RStudio Understand useful probability concepts Math RStudio Understand probability distributions Math RStudio Understand statistical hypothesis testing (comparison on means) Math RStudio Understand A-B testing Math RStudio Understand bootstrap statistical methods Math RStudio Understand the difference between frequentists and Bayesian stats Math RStudio Understand conjugate priors and Thompson sampling Math RStudio Understand Monte Carlo simulations Math RStudio Understand simple and multiple linear regression for inference Math RStudio Understand timeseries modeling Math RStudio Use web hosting tools to share analysis Web Development GitHub Use Git version control to manage code (GitHub) Git GitHub Create interactive analysis web hosted tools R R Shiny Machine Learning Engineer Explain what a Machine Learning Engineer is Soft Any Understand what the cloud and cloud service providers are Soft Any Create a cloud hosted virtual machine Cloud AWS Use a Command Line Interface (CLI) CLI Ubuntu/Terminal Understand what docker is Containers Docker Deploy a docker container on a cloud VM Containers Docker Explain project jupyter, jupyterlab, and the docker stacks Containers Docker Explain what python is Soft base Python Understand what a Jupyter Notebook is Soft Jupyter Use basic math operations Python Jupyterlab Use basic data types Python Jupyterlab Use variables Python Jupyterlab Use built-in functions Python Jupyterlab Use comparison operators Python Jupyterlab Use Boolean operators Python Jupyterlab Combine comparison and Boolean operators Python Jupyterlab Understand control flow and code chunks Python Jupyterlab Import modules Python Jupyterlab Create functions Python Jupyterlab Understand the difference between local and global variables Python Jupyterlab Use lists Python Jupyterlab Use additive operators Python Jupyterlab Use methods on lists Python Jupyterlab Use dictionaries Python Jupyterlab Understand classes and methods Python Jupyterlab Interact with files Python Jupyterlab Explain why python is good for data science Soft base Python Use matrix operations and linear algebra Math Jupyterlab Explain what numpy is Soft base Python numpy for matrix operations Python Jupyterlab numpy indexing and slicing Python Jupyterlab numpy Boolean indexing Python Jupyterlab numpy reshape and transpose Python Jupyterlab numpy pseudorandom numbers Python Jupyterlab numpy unary and binary functions Python Jupyterlab numpy aggregate functions Python Jupyterlab numpy saving and loading data Python Jupyterlab Explain what pandas is Soft base Python pandas read data Python Jupyterlab pandas for basic data exploration Python Jupyterlab pandas at and iat Python Jupyterlab pandas reshaping data Python Jupyterlab pandas subsetting Python Jupyterlab pandas summarizing Python Jupyterlab pandas group_by Python Jupyterlab pandas handling missing data Python Jupyterlab pandas and plotting Python Jupyterlab Explain what matplotlib and seaborn are Soft base Python Use matplotlib for data viz Python Jupyterlab Use seaborn for data viz Python Jupyterlab Use pandas with seaborn Python Jupyterlab Explain they various types of machine learning Soft Any Understand why training data is so important Soft Any Understand trade-offs in model selection Soft Any Understand what hyperparameters are Soft Any Understand over/under fitting Soft Any Understand bias-variance trade-off Soft Any Understand how and way training data is split Soft Any Understand how supervised models are evaluated for quality Soft Any Calculate regression measures of quality Math Any Calculate classification measures of quality Math Any Use a heuristic to create a model Python Jupyterlab Understand the supervised model training paradigm of improvement through iteration Soft Any Understand role of a cost function for optimizing parameter selection Soft Any Use linear regression and the OLS cost function Math Jupyterlab Use logistic regression and the cross-entropy cost function Math Jupyterlab Use CART models for regression and classification Math Jupyterlab Use ensemble models - random forest Math Jupyterlab Use ensemble models - xgboost Math Jupyterlab Conduct feature engineering using unsupervised learning Math Jupyterlab Explain what deep learning is Soft Any Use deep learning APIs Soft OpenAI/AWS Package an ML model as a microservice Containers Docker Certification Checklist

Certifications are a tricky thing. They don't really demonstrate mastery but can make the difference on getting an interview. Here are our minimum recommended certifications. However, if you cannot afford to complete these certifications don't worry! Use the Kaggle courses and LinkedIn Assessments instead and let your project portfolio show your competence!

Category Name Link Notes All 2023 CORE: Data Science and Machine Learning Link Data Analyst LinkedIn Excel Link Data Analyst Kaggle SQL Link and Link Data Analyst Tableau Data Analyst Link General Data Scientist LinkedIn R Assessment Link Machine Learning Engineer Andrew Ng's Intro ML Course Link Cloud - ML AWS Certified Machine Learning - Specialty Link Only need 1 of 3 Cloud - ML Google Professional Machine Learning Engineer Link Only need 1 of 3 Cloud - ML Azure Data Scientist Associate Link Only need 1 of 3 Portfolio Project Checklist

We recommend you use GitHub Pages and blogdown to host your protfolio as shown in the course. Recommended minimal list of hosted pojects:

2x MS Excel dashboards - hosted as webpages 1x Tableau Public dashboard 1x Tableau Public story 2x EDA of a dataset using RMarkdown - published on Kaggle as well 2x EDA of a dataset and ML modeel development using Python - published on Kaggle as well 1x deploy an ML model to the clouding using AWS (or similar) EC2 and a docker container

The course walks you through or gives resources needed to complete each of these. Make sure you use novel datasets in your portfolio! If you only use the data from the course it will be very similar to everyone else...

Next Steps

If you have completed the certification checklist, built a resume and hosted project protfolio you are ready to start work! The next step in your learning journy should be to decide which of the job types you want to dive deeper into. Here are the recommended next learning resources for each:

Data Analyst - Work to become one of the Tableau Visionaries General Data Scientist - Create and publish an R Package to CRAN Machine Learning Engineer - Complete the fast.ai course 'Deep learning for coders' Everyone - compete in a competition on Kaggle Latest News

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