We have searched the web for the best data engineering classes. We analyzed over 9,661 data engineering courses found on popular education sites like Udemy and Coursera and rated them based on course instructors, number of reviews, ratings, and more. Below is a list of our top 10 favorite data engineering classes. At the very bottom of the post you can check our revision history.
1. Complete Machine Learning and Data Science: Zero to Mastery
Do you wish to pursue data engineering in your future as a profession? If yes, then this course is designed for beginners but takes you to an advanced level. In details, explore neural networks, machine learning and python. Also, learn to set up your environment and use software tools like Pandas, TensorFlow 2.0, Keras, Hadoop and Kafka.
You have 2 instructors for this course – Andrei Neagoie and Daniel Bourke. Andrei is a senior software developer and has trained students who have moved on to work with companies like Apple, Amazon, Tesla, IBM, JP Morgan, Google and UNIQLO. Daniel, on the other hand, is a machine learning specialist and has worked for many websites.
- Introduce yourself to machine learning and data science framework.
- Setup the environment to learn data analysis on Pandas, NumPy and Matplotlib.
- Create machine learning models using Scikit-learn.
- Learn to code in python along with more data engineering and neural networks.
2. Feature Engineering for Machine Learning
Are you a data scientist willing to expand your skill set? If yes, then this course is going to teach you some machine learning models. This includes working with missing data, variables, outliners, strongs and time zones. You’ll be utilizing your existing knowledge in python, Numpy, Pandas, machine learning algorithms and Scikit-Learn.
You’ll be tutored by Soledad Galli. She is the leading data scientist and founder of Train in data with major experience with finance and insurance companies. In 2018, she received the data science leader award after which in 2019, she became LinkedIn’s voice in data science and analytics.
- Start with the types and characteristics of variables.
- Introduce yourself to missing data imputation and categorical variable encoding.
- Learn about categorical variable encoding, discretisation, feature scaling and outlier handling.
- Study the engineering of mixed variables, datetime variables and pipelines.
3. Data Science 2020 : Complete Data Science & Machine Learning
Are you a complete beginner in the field of data science and machine learning? If yes, then this is a detailed course that teaches you plenty of things from scratch. These include python programming language, advanced mathematics and its application in machine learning, advanced statistics and its application in data science and data visualization & processing.
The course has been designed by Jitesh’s Data Science and Machine Learning A-Z team. Jitesh Khurkhuriya, the founder of this organization has been a data scientist for more than 10 years now and has more than 20 years of experience with technology in general. After working with several Fortune 500 and government companies, his team now teaches data science online.
- Introduce yourself to data science and machine learning with python programming & mathematics.
- Learn essential statistics, data pre-processing and data regression.
- Move on to classification, feature selection and dimensionality reduction.
- Study regularization, model selection, deep learning and clustering.
4. An Introduction to Machine Learning for Data Engineers
Are you preparing for the Google certified data engineering examination? If yes, then this course will help you clear it in one go. Data engineering and machine learning is very much in demand and in order to stand out as a professional, this course will help you gain the edge you need over others, mainly due to the Google certification.
Mike West is a machine learning evangelist and your guide for this course. For about 20 years, he has been working with databases for companies like The Home Shopping Network, reed Construction Data, NetCertainty, Atlanta Gas & Light, SwingVote, SunTrust, Northrup Grumman and several Fortune 500 companies.
- Start with the basics of machine learning and python programming.
- Move on to learn data wrangling and several machine learning algorithms.
- Build a single perceptron model using the perceptron and linear function code.
- Briefly study about neural networks, gradient descent and feature engineering.
5. Data Analysis in Pandas & Scikit-learn For Machine Learning
Are you willing to become a data scientist? If yes, then utilize your basic knowledge in programming to learn how to do data analysis in Pandas. The second part of this course deals with machine learning which you’ll be programming on Scikit-Learn, applying all your concepts of data types and visualization, time series analysis and text processing.
You’ll be trained by the team of Data Science & Machine Learning Academy by Ankit Mistry. Ankit has completed his masters in machine learning & artificial intelligence from IIT Kharagpur and has more than 8 years of experience in the software industry. Through his online academy, he provides courses on machine learning, python, Pandas, Numpy and data science.
- Introduce yourself to data analysis and Pandas software.
- Install and download Anacondas, Jupyter Lab and Python code.
- Learn the python programming language and work with Pandas.
- Move on to machine learning with Scikit-Learn.
6. Complete Data Science Training with Python for Data Analysis
If you’re interested in learning python along with its practical implementation, then this course is perfect for you. By using software tools like Pandas, Anaconda, Jupyter Lab and Numpy, you’ll be learning python programming for data analysis and machine learning in detail. This includes understanding data structures, data visualization and statistical analysis.
Your course instructor, Minerva Singh happens to be a bestseller as one in the field of data science and machine learning. She did her PhD in Tropical Ecology from Cambridge University but is also a data scientist. For the work of data mining and analysis, she mostly uses python, QGIS and R.
- Introduce yourself to data science, python, Numpy and Pandas.
- Learn about data pre-processing, wrangling and visualization.
- Move on to statistical data analysis, inferences and relationships.
- Study machine learning with supervised, unsupervised and deep learning.
7. Data Analysis Bootcamp™ 21 Real World Case Studies
Are you a business analyst willing to expand your skills in data analysis? If yes, then this course is designed for beginners who want to become professionals. Starting with the theoretical concepts of data analysis, you’ll be learning python, data manipulation, Pandas, data wrangling, statistics, probability, hypothesis testing, data visualization and machine learning in detail.
You have 2 tutors for this course – Rajeev D Ratan and Nidia Sahjara. Rajeev is a computer vision engineer and data scientist with more than 8 years of experience in the field. On the other hand, Nidia is a computer science researcher as well as a branding consultant, also with an experience of more than 8 years.
- Understand the significance of data analysis and set up Google Colab.
- Learn python programming in detail.
- Use Pandas for data manipulation, feature engineering and time series data.
- Learn to use Google Data Studio for map visualizations and machine learning.
8. Data Science and Machine Learning Masterclass with R
This course has been designed for aspiring data scientists with little or no knowledge in this field. Using R programming, you’ll be understanding the usage and importance of data science, machine learning and artificial intelligence on today’s date. Work with conditional statements, functions loops, indexes, slices and subset data in R.
The course has been designed by the team of Up Degree. This team consists of IT professionals with experience in companies like Microsoft, IBM, Amazon, Flipkart, eBay, Cisco and several startups. They have 33 courses in this field and have trained more than 77,000 students so far.
- Introduce yourself to data science, R programming and R data structure.
- Learn how to import, export and manipulate data in R.
- Gain detailed insights of data visualization using R.
- Introduce yourself to statistics and hypothesis testing.
9. Data Science & Machine Learning : Hands on Data Science 2020
Anybody who knows python programming and wants to utilize it in data analysis and machine learning can take up this course. You’ll be using Pandas, Numpy, Tableau, Matpotlib and Plotly to learn data analysis using python. As you gain knowledge there, you’ll move on to machine learning using Scikit-Learn and Microsoft Azure Cloud.
This course has also been designed by the Data Science & Machine Learning Academy by Ankit Mistry (refer to course no. 6). Ankit himself has designed 15 courses in the fields of machine learning, data science, python programming, data visualization, big data and Java programming on TeamCity.
- Install Anaconda and Jupyter Notebook in your machine.
- Start with python programming and data analysis using Numpy and Pandas.
- Learn data visualization, importing data to python and data preprocessing.
- Gain insights into web scraping, data transformation & scaling, regression, probability, statistics and data science.
10. Data Science Approach from Scratch: A Shortcut Course
This is a complete beginners course on data science. You’ll be starting from the very theoretical and superficial concept of what data science is all about. From here, you’ll move on to cover concepts such as regression algorithms, naive bayes, decision trees, support vector machines, principal component analysis, linear discriminant analysis and clustering.
Your tutor, Nizamuddin Siddiqui is a data science author and career mentor. He has a master’s degree in Statistics and is certified in MySQL and R programming. For more than 3 years now, he has been helping more than 14,000 students in countries like the USA and UK to solve problems in data science and statistics.
- Introduce yourself to the basics of data science.
- Understand the various types of data and variables.
- Learn data cleaning and thinking development.
- Study feature engineering, problem definition and algorithms.
- List published 05/10/2020 with 10 products.