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Remember that data science course you signed up for but couldn’t complete? Well, you’re not alone.
Most aspiring data scientists enroll in one or more courses: free or paid. However, because data science courses typically cover a wide range of topics – from programming to data analysis, visualization, and more – they take several weeks to study. Even if they start well, most students begin to feel overwhelmed after the first few modules and do not make progress. Join Kaggle (micro)courses.
Python is one of the most widely used languages in data science. In addition to helping you in your data career, Python is also helpful if you want to pursue software engineering at some point. Kaggle’s Python course will assist you learn the following:
- Python basics (syntax and variables)
- Functions
- Logical values and conditions
- Lists, loops and list expressions
- Strings and dictionaries
- Cooperation with external libraries
If you feel you need an even simpler introduction to programming before diving into Python, you may want to check out the section introduction to programming course.
Since subsequent Pandas and Data Visualization courses require knowledge of the content of this course, you should not skip the Python course if you are novel to programming in Python.
To combine: Learn Python
Once you’re familiar with basic Python, you can learn pandas, a powerful library for data analysis and manipulation.
- Creating, reading and writing
- Indexing, selecting and assigning
- Renaming and merging
- Summary functions and maps
- Grouping and sorting
- Data types and missing values
To combine: Learn Pandas
Now that you know how to analyze data with Python and pandas, it’s time to take that knowledge and learn how to visualize data.
The Data visualization The course covers the basics of creating useful charts and graphs using the Seaborn Python library. The course covers the following topics:
- Line charts
- Bar charts and heat maps
- Scatterplots
- Histograms and density plots
- Selection of plot types
You also need to work on the final project to apply what you’ve learned.
To combine: Learn data visualization
SQL is the most significant data science skill you can learn. To understand why SQL is so significant in data science, read “Why SQL is the language to learn in data science” by KDnuggets contributor Nate Rosidi.
The Introduction to SQL The course will teach you how to query data with SQL using the BigQuery Python client and covers SQL basics, filtering, and writing readable SQL queries:
- Getting started with SQL and BigQuery
- Choose from and where
- Group by, possession and counting
- Order via
- How and
- Data linking
To combine: Learn an introduction to SQL
Now that you know the basics of SQL, you’re ready to get started Advanced SQL a course that will assist you further develop your SQL skills. This course builds on the introduction to SQL course and covers the following topics on combining data from multiple tables and performing more intricate operations:
- Joins and unions
- Analytical functions
- Nested and repeated data
- Writing proficient queries
To combine: Learn advanced SQL
If you have already gone through the above courses, you should be comfortable with programming and data analysis in Python and SQL. Now you can start using machine learning.
The Introduction to machine learning includes courses:
- How ML models work
- Basic data mining
- Model validation
- Underfitting and overfitting
- Random forests
You can also enter Kaggle’s beginner-friendly competition.
To combine: Get an introduction to machine learning
The Machine learning at the intermediate level The course builds on the “Introduction to Machine Learning” course and teaches you how to deal with missing data, categorical variables, and avoid the tough problem of data leakage when training machine learning models.
Topics covered include:
- Missing values
- Categorical variables
- ML Pipelines
- Cross-validation
- XGBoost
- Data leak
To combine: Machine learning at the intermediate level
I hope you found this course summary helpful.
As already mentioned, they are all free. And it only takes a few hours to master the necessary data analysis skills. So you can start your data science journey one micro-course at a time. Have a nice studying!
Bala Priya C is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She likes reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates fascinating resource overviews and coding tutorials.