The editors at Solutions Review compiled this list of the best DataCamp courses for data science, analytics, big data, and data engineering.
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 online course and training leader also touts a growing list of new modules worth exploring.
It’s with this in mind that the editors at Solutions Review compiled this directory of the best DataCamp courses and online training to consider, in the fields of data science, analytics, big data, and data engineering. Editor picks included in this list represent our complete coverage of the best DataCamp courses and online training from across our library of e-learning content.
Description: In this course, you’ll go from zero to hero, as you discover how to use this popular business intelligence platform through hands-on exercises. You’ll first learn how to confidently load and transform data using Power Query and the importance of data models, before diving into creating visualizations using Power BI’s drag-and-drop functionality. You’ll also learn how to drill-down into reports and make your reports fully interactive. Lastly, you’ll level-up your skills using DAX formulas (Data Analysis Expressions) to create customized calculated columns and fields to better analyze your data.
Description: You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. This module features 29 videos and 70 exercises, and should take around 4 hours to complete. Chapter 1, Getting Started with Tableau, is currently free.
Description: In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times, and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.
More “Top-Rated” DataCamp paths: Data Analysis in Spreadsheets
Description: In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You’ll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you’ll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN’s 100 most famous athletes, and the greatest hip-hop songs of all time.
More “Top-Rated” DataCamp paths: Data Visualization in R, Data Visualization in Spreadsheets, Introduction to Data Visualization in Python
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
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
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
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
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
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.
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
Description: In this course, you’ll learn how to leverage powerful technologies by helping a fictional data engineer named Cody. Using Amazon Kinesis and Firehose, you’ll learn how to ingest data from millions of sources before using Kinesis Analytics to analyze data as it moves through the stream. You’ll also spin up serverless functions in AWS Lambda that will conditionally trigger actions based on the data received.
Description: In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data.
More “Top-Rated” DataCamp paths: Building Data Engineering Pipelines in Python, Introduction to Data Engineering
Description: The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so, the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify, or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Description: This course covers the fundamentals of Big Data via PySpark. Spark is a “lightning-fast cluster computing” framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk than Hadoop. You’ll use PySpark, a Python package for spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc., to interact with works of William Shakespeare, analyze Fifa football 2018 data, and perform clustering of genomic datasets.
