Our Python for Data Science course takes you through a real data-science workflow in Jupyter notebooks: Python essentials, NumPy, data cleaning and analysis with pandas, visualisation with matplotlib and seaborn, exploratory analysis, and machine learning basics with scikit-learn. The course is taught through recorded lessons you can study at your own pace, and you can book one-to-one sessions with a verified data science tutor, who works through datasets with you and reviews your code. Suitable for university students, researchers and professionals with basic programming experience.
Reviewed by K. Sardar, Bachelor's degree in Data Science; data science and AI specialistLast updated
$13 – $33
per hour
Free demo class · pay only if you continue
What you will learn
Prerequisites
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Course structure
In short: this course gives one-to-one online tutoring in Python for data science. A verified tutor teaches pandas, NumPy, data cleaning, visualisation, statistics and an introduction to scikit-learn, working through real datasets in Jupyter.
Python for data science is the use of the Python language and its libraries, such as NumPy, pandas, matplotlib and scikit-learn, to load, clean, analyse and visualise data and build predictive models. It is the most widely used toolset in data science, analytics and machine learning courses and jobs.
This online course teaches Python for Data Science one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Data science help.
Engineering students who also use MATLAB can take our MATLAB course; for building web apps, see the Web Development course.
| Topic | Key content |
|---|---|
| Python refresher | Data types, list comprehensions, functions, basic OOP, Jupyter notebooks |
| Data wrangling | NumPy arrays, pandas DataFrames, selecting and filtering, merging and grouping, missing data |
| Visualisation | matplotlib, seaborn, choosing the right chart, plotting best practice |
| Statistics for data science | Distributions, sampling, hypothesis testing, correlation |
| Machine learning | scikit-learn pipelines, linear and logistic regression, decision trees, train-test split, model evaluation |
| Approach | pandas code | When to use |
|---|---|---|
| Find missing values | df.isna().sum() | Always check first |
| Drop rows | df.dropna() | Few missing rows, missing at random |
| Fill with a value | df.fillna(0) | Missing means zero |
| Fill with mean or median | df['col'].fillna(df['col'].median()) | Numeric columns, skewed data use median |
| Forward fill | df.ffill() | Time series |
Here is a typical task: from a sales table, find average revenue per region.
| Step | pandas code |
|---|---|
| Load the data | df = pd.read_csv("sales.csv") |
| Check missing values | df.isna().sum() |
| Group and average | df.groupby("region")["revenue"].mean() |
| Sort the result | .sort_values(ascending=False) |
Students most often lose marks in Python for Data Science through the habits below. Each is easy to fix with practice and feedback on your own code.
| Mark-loser | What to do instead |
|---|---|
| Ignoring missing values | Check them with isna() before analysing |
| Chained assignment warnings | Use .loc for assignments |
| Unlabelled charts | Add titles, axis labels and units |
| Notebooks that won't re-run | Restart and run all before submitting |
| Results without interpretation | Explain what each figure shows |
Data science modules are usually assessed by notebook-based assignments and projects analysing real datasets, sometimes with a written exam on methods.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Machine Learning, Master Statistics and our Data Science subject page.
A Python for data science tutor online gives you live 1-to-1 lessons on a shared screen, working through NumPy, pandas, charts and scikit-learn with real datasets.
This course suits students, analysts and career changers who know basic Python and want to work with data.
Prerequisites: basic Python: variables, loops and functions.
Course length: 40 lessons of 30 minutes across 5 modules.
| Stage | Topics | What you can do afterwards |
|---|---|---|
| 1 | Python refresher | Write clean Python for data tasks |
| 2 | Data wrangling | Clean and reshape data with pandas |
| 3 | Visualisation | Plot data with matplotlib and seaborn |
| 4 | Statistics for data science; machine learning | Test ideas and train models with scikit-learn |
Where it leads: see the Machine Learning course for the next step, or our machine learning tutoring page.
Basic Python helps, but the course includes a refresher.
A Python library for working with tabular data in DataFrames.
NumPy handles fast numerical arrays; pandas adds labelled tables built on NumPy.
matplotlib for full control and seaborn for quick statistical charts.
A Python library for machine learning, with consistent tools for training and evaluating models.
Yes. Lessons use Jupyter or Google Colab.
Tutors explain methods and review your code; the project you submit must be your own.
Yes. It covers the core Python toolkit used in data roles.
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
Use df.groupby("column") followed by an aggregation such as .mean() or .sum().
Drop it with dropna() or fill it with fillna(), choosing the method that suits the data.
A two-dimensional table of labelled rows and columns in pandas.
Use pd.merge() with the key columns and the join type you need.
Line charts for trends, bar charts for comparisons and scatter plots for relationships.
loc selects by label; iloc selects by integer position.
If you study a few hours each week, most beginners learn enough Python, pandas and plotting for basic data analysis in two to three months. Machine learning with scikit-learn and real projects take longer. Your tutor builds the plan around your course, project or job goal and checks progress each week.
Yes. Tutors review your own notebook and explain improvements.
Python is the most common language for data science, but it is rarely enough on its own. You also need SQL to work with databases, statistics to interpret results, and skills in cleaning data and presenting findings. Your tutor teaches Python together with these skills through real datasets.
Yes. Lessons are online, so you can book times that suit your time zone.
Yes. You can learn with a 1-to-1 Python for data science tutor online through live lessons with a shared screen. Your tutor helps you clean real datasets, build charts and train first models, debugs your code with you and helps you understand project work that you complete yourself.
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