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Remember that data science course you signed up for but were never able to finish? Well, you're not alone.
Most beginners in data science enroll in one or more courses: free or paid. But because data science courses typically cover a wide range of topics (from programming to data analysis, visualization, and more), they take several weeks to analyze. And even if they start off strong, most students begin to feel overwhelmed after the first few modules and fail to progress. Enter Kaggle (micro)courses.
the series of Kaggle microcourses They are a good alternative if you find longer courses more difficult to complete. They are great resources for learning data science skills (Python, pandas, machine learning, and more) without feeling overwhelmed. The courses are designed in such a way that they only take a few hours to complete and include tutorial and practice components. Now let's go over some beginner courses and what they cover.
Python is one of the most used languages in data science. In addition to helping you in your data career, Python is also useful if you want to get into software engineering at some point. The Python course on Kaggle will help you learn the following:
- Python Basics (Syntax and Variables)
- Features
- Booleans and conditionals
- Lists, loops, and list comprehensions
- Strings and dictionaries
- Work with external libraries
If you think you need an even simpler introduction to programming before diving into Python, you can check out the introduction to programming course.
Because subsequent courses on Pandas and data visualization require you to be comfortable with the content of this course, you should not skip the Python course if you are new to programming with Python.
Link: Learn Python
Once you are familiar with basic Python, you can learn pandas, a powerful data manipulation and analysis library.
Through a series of short lessons and practical coding exercises, the pandas will help you learn how to perform the following operations on pandas data frames:
- Create, read and write
- Index, select and assign
- Rename and merge
- Summary of functions and maps.
- Group and sort
- Data Types and Missing Values
Link: learn pandas
Now that you know how to analyze data with Python and pandas, it's time to take advantage of that and learn how to visualize your data.
He Data visualization The course covers the fundamentals of creating useful graphs and diagrams using the Seaborn Python library. The course covers the following:
- Line charts
- Bar charts and heat maps.
- Scatter plot
- Histograms and density graphs.
- Choose plot types
You also need to work on a final project to apply what you have learned.
Link: Learn data visualization
SQL is the most essential data science skill you can learn. To understand why SQL is very important for data science, read “Why SQL is the language to learn for data science” by KDnuggets contributor Nate Rosidi.
He Introduction to SQL The course will teach you how to query data sets with SQL using the BigQuery Python client and will cover SQL fundamentals, filtering, and writing readable SQL queries:
- Getting started with SQL and BigQuery
- Select, from and where
- Group by, have and count
- sort by
- how and with
- Joining data
Link: Learn Introduction to SQL
Now that you are comfortable with the basics of SQL, you can take the Advanced SQL course to further develop your SQL skills. This course builds on the Introduction to SQL course and covers the following topics on how to combine data from multiple tables and perform more complex operations:
- Unions and unions
- Analytical functions
- Nested and repeated data
- Write efficient queries
Link: Learn advanced SQL
If you have already completed the previous courses, you should be comfortable with programming and data analysis with Python and SQL. Now you are ready to get started with machine learning.
He Introduction to machine learning The course covers:
- How machine learning models work
- Basic data exploration
- Validation model
- Misfit and overfit
- Random forests
You can also submit an application to a Kaggle competition for beginners.
Link: Learn Introduction to Machine Learning
He Intermediate machine learning The course builds on the Introduction to Machine Learning course and teaches you how to handle missing values and categorical variables and avoid the complicated problem of data leakage when training machine learning models.
Topic covered includes:
- Missing values
- Categorical variables
- Machine Learning Pipelines
- Cross validation
- XGBoost
- data leak
Link: Intermediate machine learning
I hope you found this course overview helpful.
As mentioned, they are all free. And it only takes a few hours to learn an essential data science skill. So you can start your journey into data science one microcourse at a time. Happy learning!
Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more.
