The Best DataCamp Data Science and Machine Learning Courses in 2023

A directory of the best DataCamp training courses for data science and machine learning, compiled by the editors at Solutions Review.

Data science is one of the fastest-growing fields in America. Organizations are employing data scientists at a rapid rate to help them analyze increasingly large and complex data volumes. The proliferation of big data and the need to make sense of it all has created a vortex where all of these things exist together. As a result, new techniques, technologies, and theories are continually being developed to run advanced analysis, and they all require development and programming to ensure a path forward.

With this in mind, the editors at Solutions Review have compiled this list of the best DataCamp courses for data science and machine learning. DataCamp’s mission is to “democratize data skill for everyone” by offering more than 350 different data science and analytics courses and 12 distinct career tracks. More than 2,000 companies, 3,000 organizations, and 8 million users from 180 countries have used DataCamp since its founding. DataCamp’s entire course catalog is interactive which makes it perfect for learning at your own pace.

The Best DataCamp Courses for Data Science and Machine Learning

TITLE: Introduction to Predictive Analytics in Python

OUR TAKE: This 4-hour long training is made up of 14 videos and 52 exercises. It shows you how to use and present logistic regression models for making predictions. The first chapter on building logistic regression models is free.

Description: In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders. The course is instructed by Nele Verbiest, a senior data scientist at Python Predictions. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities.

More “Top-Rated” DataCamp paths: Intermediate Predictive Analytics in Python, Predictive Analytics using Networked Data in R

TITLE: Data Science for Everyone

OUR TAKE: This introduction to data science (with no coding involved) can be completed in only two hours through 15 videos and 48 different exercises. 200,000 DataCamp students have passed through this training.

Description: In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data.

More “Top-Rated” DataCamp paths: Data Science for Business, Introduction to Data Science in Python, Linear Algebra for Data Science in R

TITLE: Introduction to Predictive Analytics in Python

OUR TAKE: This 4-hour long training is made up of 14 videos and 52 exercises. It shows you how to use and present logistic regression models for making predictions. The first chapter on building logistic regression models is free.

Description: In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders. The course is instructed by Nele Verbiest, a senior data scientist at Python Predictions. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities.

More “Top-Rated” DataCamp paths: Intermediate Predictive Analytics in Python, Predictive Analytics using Networked Data in R

TITLE: Introduction to Spark with sparklyr in R

OUR TAKE: This course will show you how to analyze large Spark datasets using Apache Spark and R using the sparkly package. It features 4 hours of video content and 50 exercises. Nearly 17,000 students have taken this training.

Description: R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The sparklyr package lets you write dplyr R code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both the dplyr interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you’ll explore the Million Song Dataset.

More “Top-Rated” DataCamp paths: Machine Learning with PySpark, Introduction to Spark SQL in Python, Cleaning Data with PySpark

TITLE: Introduction to R

OUR TAKE: Introduction to R helps you master the basics of data analysis by manipulating common data structures like vectors, matrices, and data frames.

Description: Part of DataCamp’s robust R course directory, this module will enable you to master the basics of this widely used open-source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science.

More “Top-Rated” DataCamp paths: Intermediate R, Exploratory Data Analysis in R

TITLE: R For SAS Users

OUR TAKE: Through 56 different video exercises, this course shows you how to translate your SAS knowledge into R and analyze data using a powerful software language. It should take 4 hours to complete via 15 videos.

Description: This course is a gentle introduction to the R language with every chapter providing detailed mapping of R functions to SAS procedures highlighting similarities and differences. You will orient yourself in the R environment and discover how to wrangle, visualize, and model data plus customize your output for the final presentation. Throughout the course, you will follow a consistent workflow of data quality checking and cleaning, exploring relationships, modeling, and presenting results. You will leave this course with coded examples that provide a template to use immediately with a dataset of your own.

TITLE: Introduction to Deep Learning in Python

OUR TAKE: This course lets you start for free and shows you the fundamentals of neural networks and how to build deep learning models using Keras 2.0. It features 4 hours of content and 50 exercises.

Description: Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

More “Top-Rated” DataCamp paths: Introduction to Deep Learning with PyTorch, Introduction to Deep Learning with Keras, Advanced Deep Learning with Keras

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