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Remember that data science course you signed up for but never finished? Well, you’re not alone.
Most data science beginners enroll in one or more courses, either free or paid. However, data science courses typically take several weeks to complete because they cover a wide range of topics, from programming to data analysis and visualization. And even if they start off well, most learners start to feel overwhelmed after the first few modules and stop making progress. Join a Kaggle (micro) course.
If you have trouble taking long courses, Kaggle’s series of microcourses is a good alternative. These are great resources for learning data science skills like Python, Pandas, and machine learning without getting overwhelmed. The course is designed to be completed in just a few hours and includes a tutorial and practice component. Now, let’s take a look at the courses for beginners and their contents.
Python is one of the most widely used languages in data science. Python is not only useful for a career in data, but also if you want to go into software engineering one day. Kaggle’s Python courses will help you learn:
- Python basics (syntax and variables)
- function
- Boolean values and conditional statements
- Lists, loops, and list comprehensions
- strings and dictionaries
- Working with external libraries
If you feel like you need an even easier introduction to programming before getting into Python, check out our introductory programming course.
Do not skip the Python course if you are new to programming in Python, as subsequent courses on Pandas and data visualization require familiarity with the content of this course.
Link: Learn Python
Once you’re familiar with basic Python, you can learn pandas, a powerful data analysis and manipulation library.
Through a series of short lessons and hands-on coding exercises, pandas helps you learn how to perform the following operations on pandas dataframes.
- create, read, write
- Indexing, selection and allocation
- Rename and combine
- Summary functions and maps
- Grouping and sorting
- Data types and missing values
Link: Learn pandas
Now that you know how to analyze data using Python and pandas, it’s time to build on that by learning how to visualize data.
The Data Visualization course teaches you the basics of creating useful plots and charts using the Python library Seaborn. This course covers:
- line graph
- Bar graphs and heatmaps
- Scatter plot
- Histogram and density plot
- Select plot type
You will also need to work on a final project to apply what you have learned.
Link: Learn data visualization
SQL is the most important data science skill you can learn. To understand why SQL is so important for data science, read “Why SQL is the language to learn for data science” by KDnuggets contributor Nate Rosidi.
The Introductory SQL course teaches you how to query datasets with SQL using the BigQuery Python client, and covers SQL basics, filtering, and writing readable SQL queries.
- Introduction to SQL and BigQuery
- choice, from where, where
- Grouping, owning, and counting
- How to order
- that way
- Joining data
Link: Learn SQL overview
Now that you’re familiar with the basics of SQL, you can take advanced SQL courses to further improve your SQL skills. This course builds on the Introductory SQL course and covers the following topics about joining data from multiple tables and performing more complex operations:
- Joins and Unions
- analytical functions
- nested repeating data
- Writing efficient queries
Link: Learn advanced SQL
If you have already studied the above courses, you should be familiar with programming and data analysis using Python and SQL. Now you’re ready to start machine learning.
The introductory machine learning course includes:
- How ML models work
- Basic data exploration
- Model validation
- Underfitting and overfitting
- random forest
You can also enter Kaggle contests for beginners.
Link: Learn an overview of machine learning
The Intermediate Machine Learning course builds on the Introductory Machine Learning course and teaches you how to handle missing values and categorical variables when training machine learning models and avoid the thorny problem of data leakage.
Topics covered include:
- missing value
- categorical variable
- ML pipeline
- cross validation
- XG boost
- data leak
Link: Intermediate Machine Learning
We hope you found this course summary useful.
As mentioned earlier, they are all free. And it only takes a few hours to learn important data science skills. Therefore, he can start his data science journey with one micro course at a time. Let’s have fun learning!
Rose Priya C I’m a developer and technical writer from India. She loves working at the intersection of math, programming, and data. She loves working at the intersection of science and content creation. Her interests and expertise include DevOps, data science, and natural language processing. She loves reading, writing, coding, and coffee. Currently, she is committed to learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion articles, and more.
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