Top 7 Free Kaggle Micro-Courses for Data Science Beginners

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This introductory post serves as a sneak peek into the top 7 free Kaggle micro-courses tailored for data science beginners. In the upcoming blog post, we’ll delve deeper, exploring each course’s key features, learning objectives, and potential benefits.

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Top 7 Free Kaggle Micro-Courses for Data Science Beginners

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Here are Free Kaggle Micro-Courses for Data Science

1. Python

One of the most popular languages in data science is Python. Python is useful not only for data professionals but also for those who hope to pursue a career in software engineering in the future. The following knowledge can be acquired with the Kaggle Python course:

Basics of Python (variables and syntax)Uses

Conditionals and Booleans
List comprehensions, loops, and lists
Dictionary and string
Utilizing third-party libraries
You can look into the start to programming course if you think you need a more straightforward introduction to programming before learning Python.

If you are new to programming with Python, you should not miss the Python course because the succeeding courses on Pandas and data visualization require you to be acquainted with the material of this course.

Course Link Click Here

2. Pandas

After mastering the fundamentals of Python, you can study pandas, a potent toolkit for data analysis and manipulation.

The pandas will assist you in learning how to execute the following operations on pandas data frames through a sequence of brief courses and practical coding exercises:

Producing, perusing, and penning

Sorting, allocating, and indexing

Combining and renaming

Maps and summary functions

Sorting and grouping

Missing values and data types

Course Link Click Here

3. Data Visualization 

It’s time to expand on your knowledge of data analysis with Python and pandas by learning how to visualise your data.

The principles of using the Python package Seaborn to create useful plots and charts are covered in the Data Visualisation course. The following topics are covered in the course:

Line diagrams

Heat maps and bar charts

Diskplots

Density plots and histograms

Selecting storylines

To put everything you’ve learned into practice, you must also work on a final project.

Course Link Click Here

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4. Intro to SQL

The most important data science skill you can acquire is SQL. Read “Why SQL is the Language to Learn for Data Science” by KDnuggets writer Nate Rosidi to learn why SQL is so crucial for data science.

The Intro to SQL course covers SQL foundations, filtering, and crafting legible SQL queries. It will also show you how to query data sets with SQL using the BigQuery Python client.

Beginning to use BigQuery and SQL

Choose, where, and from

Sort by, possess, and tally

As and with Joining data in order

Course Link Click Here

5. Advanced SQL

Once you have mastered the fundamentals of SQL, you can advance your knowledge of the language by enrolling in the Advanced SQL course. Building on the introduction to SQL course, this course covers the following topics on merging data from different tables and carrying out increasingly intricate operations:

Unions and Joins

analytical processes

Repeated and nested data

Composing effective queries

Course Link Click Here

6. Intro to Machine Learning

Having completed the aforementioned courses, you need to be at ease with Python and SQL data analysis and programming. You can now begin utilizing machine learning.

Course material for Intro to Machine Learning includes:

How machine learning models operate

simple data exploration

Validation of the model

Excessive and insufficient fitting

arbitrary woods

Additionally, you can enter a beginner-friendly Kaggle competition.

Course Link Click Here

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7. Intermediate Machine Learning 

Building on the concepts covered in the Intro to Machine Learning course, the Intermediate Machine Learning course teaches you how to deal with missing values and categorical variables, and how to steer clear of the challenging data leaking issue when training machine learning models.

Among the subjects discussed are:

Absence of values

Qualitative variables

ML workflows

Cross-checking

XGBoost: Data Breach

Course Link Click Here

FAQ

What are the benefits of taking these free Kaggle micro-courses?

Learn foundational data science skills at your own pace and convenience.
Gain practical experience through interactive exercises and real-world scenarios.
Boost your resume and showcase your interest in data science to potential employers.
Lay a solid foundation for further exploration in the vast world of data science.

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