DataCamp Course List

Foundation

Introduction to Python

Python basics: Lists, Functions, Packages, NumPy

Foundation

Data Types for Data Science

Lists, Tuples, Dictionaries and some more advanced data containers. Dates/Times

Foundation

Python Data Science Toolbox (Part 1)

User-Defined Functions, Scope, Lambda Functions and Error-Handling

Foundation

Python Data Science Toolbox (Part 2)

Iterators, List Comprehension and Generators

Foundation

Software Engineering for Data Scientists in Python

Modules, Classes, Maintainability

Data Manipulation

Importing Data in Python (Part 1)

Importing data from files (e.g. csv, excel etc.)

Data Manipulation

Importing Data in Python (Part 2)

Importing data from APIs

Data Manipulation

Cleaning Data in Python

Outliers, missing values, duplicates, pivots, combining, string and pattern matching

Data Manipulation

Introduction to Databases in Python

SQL queries in Python, SQLAlchemy, database creation and update

Data Manipulation

pandas Foundations

Data import, exploration, quick visualisation, time series

Data Manipulation

Manipulating DataFrames with pandas

Index, slice, filter, advanced indexing, pivoting/reshaping, splitting, grouping

Data Manipulation

Merging DataFrames with pandas

Shared indices, concatenation, merging dataframes

Data Manipulation

Analyzing Police Activity with pandas

pandas case study

Data Visualisation

Introduction to Data Visualization with Python

Matplotlib, plot customisation and improvements, seaborn

Data Visualisation

Data Visualization with Seaborn

seaborn recap, complex plots, mutiplots, customisation

Data Visualisation

Interactive Data Visualization with Bokeh

Basic plots with bokeh, multiplots and layout, tooltips and annotations, bokeh server applications for visualisation

Data Visualisation

Visualizing Geospatial Data in Python

2 layer maps, scatterplots, GeoJSON, projections and coordinate transforms, spatial joins, street layer map, geopandas and folium

Data Visualisation

Improving your Data Visualizations in Python

Improving plotting and data visualisation, appropriate colour choice, showing uncertainty

Analysis and Modelling

Statistical Thinking in Python (Part 1)

Summary stats and data exploration, inference and probability, probability distributions and discrete variables

Analysis and Modelling

Statistical Thinking in Python (Part 2)

Finding optimal parameters, bootstrap confidence intervals, hypothesis testing

Analysis and Modelling

Supervised Learning with Scikit-Learn

k-nearest neighbours, linear regression, cross-validation, regularisation, logistic regression, ROC/AUC, train/test split and holdouts, encoding and normalising data, pipelines

Analysis and Modelling

Unsupervised Learning in Python

Clustering, hierarchical clustering and t-SNE, principal components analysis, dimension reduction with non-negative matrix factorisation

Analysis and Modelling

Machine Learning with Tree-Based Models in Python

scikit-learn: CART, bias-variance tradeoff, cross-validation, random forests, hyperparameter tuning, boosting

Foundation

Introduction to R

R basics; vectors, matrices, factors, data frames, lists

Foundation

Intermediate R

Conditionals and control flow, loops, functions, apply, utilities

Foundation

Intermediate R: Practice

Exercises to refresh and improve understanding of the content from the “Intermediate R” course

Foundation

Writing Functions in R

When and how functions should be written, functional programming in R (purrr), error handling, robust function writing in R

Data Manipulation

String Manipulation in R with stringr

String basics, introduction to stringr, pattern matching with regular expressions, dealing with unicode

Data Manipulation

Working with Dates and Times in R

Dates and Times in R, manipulating dates (lubridate), arithmetic with dates, common problems with parsing dates in R

Data Manipulation

Importing Data in R (Part 1)

Importing data with utils, using readr and data.table, importing excel data, working with XLConnect

Data Manipulation

Importing Data in R (Part 2)

Importing data from databases, importing data from the web, importing data from statistical software packages (SAS, SPSS etc.)

Data Manipulation

Cleaning Data in R

Exploring raw data, tidying and preparing data in R

Data Manipulation

Importing & Cleaning Data in R: Case Studies

Four sample case studies

Data Manipulation

Data Manipulation in R with dplyr

Introduction to dplyr and tbls, select and mutate, filter and arrange, summarise and pipe operator, working with databases

Data Manipulation

Data Analysis in R, the data.table Way

Introduction to data.table, general querying, sub-setting and updating, indexing and using keys in joins

Data Visualisation

Data Visualisation in R

Introduction to base graphics in R, basic plot types, adding further detail, visualisation best practices

Data Visualisation

Data Visualisation with ggplot2 (Part 1)

Introduction to ggplot2, how to structure data in ggplot2, working with the aesthetics and geometrics layers

Data Visualisation

Data Visualisation with ggplot2 (Part 2)

Using ggplot2 for graphical data analysis, using the coordinates and facets layers, theme layer, best practice using ggplot2

Data Visualisation

Data Visualisation with ggplot2 (Part 3)

Plotting for specific data types, including statistical models, maps and using the grid / ggpronto packages

Data Visualisation

Communicating with Data in the Tidyverse

Use of custom ggplot2 themes, creating unique visualisations, introduction to R markdown, report customisation

Analysis and Modelling

Exploratory Data Analysis

Exploring categorical and numerical data in R, summarising data in R

Analysis and Modelling

Machine Learning Toolbox

Fitting and evaluating performance of regression and classification models, tuning model parameters, preparing data for modelling

Analysis and Modelling

Supervised Learning in R: Classification

Using R for basic classification algorithms, including k-Nearest Neighbours, Naive Bayes, Logistic Regression and classification trees

Analysis and Modelling

Supervised Learning in R: Regression

Training and evaluating regression models, dealing with non-linear responses, using tree based methods

Analysis and Modelling

Unsupervised Learning in R

Using R for unsupervised learning, including hierarchical clustering, dimensionality reduction and Principal Component Analysis