Course
Introduction to Importing Data in Python
3 hr
338.5K
So you've decided to learn Python, which makes sense, given how much of the data science world (job postings, open-source tooling, LLM-adjacent workflows) now assumes it as a baseline rather than a nice-to-have.
Now you might be wondering where to learn Python, especially when you're not starting from zero, and you already think in DataFrames, vectors, or procedures - just in a different language.
You're in good company. It's a researched topic, and we've compiled a list to help. Our list ranks platforms on four criteria:
- Direct language bridging: whether the material explicitly maps your existing language's syntax and workflows onto Python, or just teaches Python from a blank slate
- Relevance to analyst workflows: whether the focus is data wrangling, visualization, and statistical modeling, or general-purpose programming you might not use
- Hands-on practice: whether you write and run real Python code inside the lesson, or just read about it
- Depth beyond the bridge: whether there's a clear next step once you've made the initial transition, or the material dead-ends
1. Python for R Users / Python for MATLAB Users — DataCamp
DataCamp's Python for R Users and Python for MATLAB Users courses are the best option on this list because they're purpose-built for exactly this transition, not repackaged beginner content.
- Level: Intermediate; assumes existing proficiency in R or MATLAB, not new to programming
- Time: A few hours each, self-paced
- Cost: Free to start; full access included with a DataCamp subscription
- Best for: R or MATLAB analysts who want their existing mental models — data frames, vectorized operations, plotting — explicitly mapped onto pandas, NumPy, and matplotlib
Neither course wastes time on concepts you've already mastered in another language — the whole premise is translation, not re-education. Plus, DataCamp's AI Tutor understands you're coming in with prior programming experience, so when you get stuck it can explain a pandas error in terms of what you'd expect from an R data frame or a MATLAB matrix, rather than starting the explanation from scratch every time.
2. Python for SAS Users — O'Reilly
Python for SAS Users, by Randy Betancourt and Sarah Chen, is a good option for SAS programmers who want a dense, direct mapping of SAS constructs onto Python and pandas.
- Level: Intermediate; written for working SAS programmers, not programming beginners
- Time: Self-paced; ~8-hour read/work-through
- Cost: Included with an O'Reilly subscription
- Best for: SAS programmers who want line-by-line comparisons between familiar SAS procedures and their Python/pandas equivalents
The book pairs over 200 Python scripts with roughly 75 corresponding SAS programs, covering data types, indexing, groupby operations, data management, and reading/writing data, with a dedicated chapter on the saspy module for those who need Python and SAS to interoperate rather than fully replace one with the other.
3. Coming From Stata — Coding for Economists
Coming From Stata, part of Arthur Turrell's free Coding for Economists resource, is a good option for STATA users who want a direct, comprehensive syntax reference rather than a structured course.
- Level: Intermediate; assumes existing Stata fluency
- Time: Self-paced reference; work through at your own speed
- Cost: Free
- Best for: STATA analysts, particularly in economics and social science
This isn't a video course or a graded platform — it's a maintained, free reference table mapping Stata commands directly onto pandas, statsmodels, pyfixest, and other Python libraries, covering data import, sample selection, merging, reshaping, panel data, regressions, and plotting. Worth knowing upfront: because it's a reference rather than a course, there's no structured lesson sequence or built-in practice — you look up what you need as you need it, which suits some learners better than others.
4. Python for Data Analysis — UC Berkeley Extension
Python for Data Analysis is a good option for analysts who want live, instructor-led sessions rather than self-paced video or reference material.
- Level: Intermediate; assumes undergraduate-level statistics but limited programming experience
- Time: Multi-week, scheduled Live Online sessions via Zoom
- Cost: Paid, university-extension pricing
- Best for: Analysts who want real-time instructor access and a cohort to work alongside
The course covers importing and exporting data, connecting to databases, and using pandas DataFrames for selection and transformation, aimed at people who already think statistically but want Python specifically as the tool to execute that thinking at scale. Unlike DataCamp's or O'Reilly's offerings, it doesn't map syntax from a specific prior language — the value here is live instruction, not translation.
5. Dataquest - Python Courses
Dataquest is a good option for analysts who've completed a language-bridging course elsewhere and want a deeper, project-based platform to build genuine Python fluency.
- Level: Beginner to intermediate
- Time: Self-paced; full paths run 20+ hours
- Cost: Free tier available; subscription for full path access
- Best for: Analysts who want browser-based practice writing Python from scratch
Dataquest's data analyst path covers Python fundamentals, pandas, data cleaning, and visualization through project-based lessons.
Comparison Table
| Rank | Platform | Flagship Resource | Curriculum Depth | Cost / Outcomes Signal |
|---|---|---|---|---|
| 1 | DataCamp | Python for R Users / Python for MATLAB Users | Direct syntax and workflow mapping from R or MATLAB to Python | Free to start; a few hours each |
| 2 | O'Reilly | Python for SAS Users (Betancourt & Chen) | 200+ Python scripts mapped against ~75 SAS programs | O'Reilly subscription; ~8 hours |
| 3 | Coding for Economists | Coming From Stata | Stata-to-Python command translation reference | Free; self-paced reference, no course structure |
| 4 | UC Berkeley Extension | Python for Data Analysis | pandas, databases, data import/export, live instruction | Paid; multi-week Live Online sessions |
| 5 | Dataquest | Data Analyst in Python path | General Python fluency, pandas, cleaning, visualization | Free tier; subscription for full path |
Author
Josef Waples
I'm a data science editor with contributions to research articles in scientific journals. I'm especially interested in linear algebra, statistics, R, and the like.
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