Data & AISelf-study + mentor

Data Analysis with Python

Load messy data, clean it, summarise it with pandas, chart it and share clear findings, one practical step at a time.

Level:: Beginner to intermediate
Format:: Free self-study lessons + 1:1 mentoring

Free, no-obligation chat · Affordable plans that fit your budget · Start this week

1:1 personal attentionOne mentor, one student, at your pace.
Flexible timingsSessions that fit your week and time zone.
Affordable plansPlans that fit your budget.
Practise between sessionsShort problems that build confidence.

About this course

Learn Data Analysis with Python online

Data analysis turns raw tables into answers, and Python with pandas is the tool most analysts reach for. Beginners often get stuck on the same things: reading files with odd formats, missing values, the difference between a Series and a DataFrame, chained indexing warnings, and choosing a chart that actually shows the point.

This course follows the standard analyst workflow: Python and notebook basics, loading data, exploring and selecting, cleaning, transforming, grouping and aggregating, merging tables, working with dates and text, visualising with matplotlib and seaborn, and writing up findings. Each module lists its topics and what you will be able to do.

Our free Python lessons cover the programming basics you need, and an affordable 1:1 mentor guides the data analysis work with real datasets and reviews your notebooks with you. Message us on WhatsApp or email, or fill in the short form, for a free, no-obligation chat. We will suggest a plan that fits your budget and flexible timings, and you can start this week.

Who this course is for

Beginners who want to start a career as a data analyst
Students who need to analyse data for coursework or research
Professionals who work in spreadsheets and want to scale up with Python
Learners who know basic Python and want to apply it to data
Anyone preparing to move on to data science

Outcomes

What you'll learn

Work in Jupyter notebooks and load data from CSV, Excel and JSON files
Select, filter and sort data with pandas using loc, iloc and boolean masks
Clean data by handling missing values, duplicates, wrong types and outliers
Summarise and reshape data with groupby, pivot tables and merges
Work with dates, times and text columns
Create clear charts with matplotlib and seaborn
Run a complete analysis from question to written findings
Connect to a database with SQL and read query results into pandas

Skills you'll gain

Python
pandas
NumPy basics
Jupyter notebooks
Data cleaning
groupby and pivot tables
matplotlib
seaborn
Exploratory analysis
Time series basics
SQL basics
Reporting

Detailed syllabus

Data Analysis with Python syllabus

Every module lists its topics and what you will be able to do. Where we have a matching free lesson, it is linked so you can study or revise on your own.

Module 1
Python and notebook foundations
The Python you need for data work, and the notebook environment you will use every day.
- Installing Python and Jupyter
- Variables, numbers, strings and booleans
- Lists and dictionaries
- Loops and conditions
- Functions
- Reading error messages
- Installing packages with pip
- Notebook habits: cells, markdown and restarting the kernel
By the end of this module you can
- Write small Python programs and run them in a notebook
- Use lists, dictionaries and functions comfortably
Module 2
Loading and inspecting data
Getting data into pandas and understanding what you have.
- Series and DataFrame
- read_csv and its options
- Reading Excel and JSON files
- head, tail, shape, info and describe
- dtypes and memory
- Index and columns
- Saving results with to_csv
By the end of this module you can
- Load a file into a DataFrame and describe its structure
- Spot data type problems from info() and describe()
Covered in 1:1 mentoring sessions
Module 3
Selecting and filtering
Picking the rows and columns you need.
- Selecting columns
- loc and iloc
- Boolean masks and combining conditions
- isin, between and str.contains
- query()
- Sorting with sort_values
- Setting and resetting the index
- Avoiding chained assignment
By the end of this module you can
- Filter a DataFrame with several conditions
- Explain the difference between loc and iloc
Covered in 1:1 mentoring sessions
Module 4
Data cleaning
Turning messy real-world data into something you can trust.
- Finding missing values with isna
- dropna and fillna
- Duplicates and drop_duplicates
- Converting types with astype and to_numeric
- Renaming and tidying column names
- Detecting outliers with IQR and z-scores
- Cleaning text with the str methods
- Validating data with simple checks
By the end of this module you can
- Clean a messy dataset and document each decision
- Choose a sensible way to treat missing values and outliers
Covered in 1:1 mentoring sessions
Module 5
Transforming data
Creating new columns and reshaping tables.
- Creating columns from calculations
- apply and map
- np.where and conditional columns
- Binning with cut and qcut
- String and category columns
- melt and pivot
- Method chaining
By the end of this module you can
- Derive new columns that answer a business question
- Reshape a table between wide and long formats
Covered in 1:1 mentoring sessions
Module 6
Grouping and aggregation
Summarising data by category, the core of analysis.
- The split-apply-combine idea
- groupby with one and several keys
- agg with several functions
- Named aggregation
- transform and filter on groups
- pivot_table and crosstab
- value_counts and ranking
- Cumulative sums and rolling windows
By the end of this module you can
- Build summary tables by category, month or region
- Use pivot_table to answer a two-way question
Covered in 1:1 mentoring sessions
Module 7
Combining datasets
Bringing data from several tables together.
- concat for stacking tables
- merge: inner, left, right and outer
- Merging on one or many keys
- Checking for duplicate keys and unmatched rows
- join on the index
- Comparing pandas merges with SQL joins
By the end of this module you can
- Merge two tables and verify that no rows were lost or duplicated
- Explain how each merge type works
Covered in 1:1 mentoring sessions
Module 8
Dates, times and text
Working with the two most awkward column types.
- to_datetime and parsing formats
- The dt accessor: year, month, weekday
- Date arithmetic and Timedelta
- Resampling time series
- Rolling averages
- Regular expressions with str.extract
- Splitting and combining text columns
By the end of this module you can
- Turn text dates into datetimes and analyse trends over time
- Extract useful fields from text columns
Covered in 1:1 mentoring sessions
Module 9
Visualisation with matplotlib and seaborn
Choosing the right chart and making it clear.
- Figures and axes in matplotlib
- Line, bar, histogram and scatter plots
- Box plots and distributions
- seaborn: countplot, heatmap, pairplot and boxplot
- Titles, labels, legends and colour
- Subplots
- Choosing the right chart for a question
- Saving figures
By the end of this module you can
- Create clear, labelled charts for different kinds of data
- Pick a chart type that matches the message
Covered in 1:1 mentoring sessions
Module 10
SQL and Python together
Pulling data from a database into pandas.
- Why analysts use SQL
- Basic SELECT, WHERE and GROUP BY
- Connecting to a database from Python
- read_sql and parameterised queries
- Writing a DataFrame back with to_sql
- Deciding what to do in SQL and what in pandas
By the end of this module you can
- Read the result of an SQL query straight into a DataFrame
- Split work sensibly between SQL and pandas
Covered in 1:1 mentoring sessions
Module 11
Exploratory data analysis and reporting
Running a full analysis and explaining the result to others.
- Starting from a question
- Univariate and bivariate exploration
- Correlation and its limits
- Finding and checking patterns
- Writing a short findings summary
- Making notebooks reproducible
- Exporting to HTML or PDF
- Presenting to a non-technical reader
By the end of this module you can
- Complete an end-to-end analysis and write the findings in plain language
- Share a clean, reproducible notebook
Covered in 1:1 mentoring sessions

Ways to learn

How you can learn Data Analysis with Python

Data Analysis with Python mentor, 1:1 online

A mentor who knows this course plans sessions around your goal, explains each idea with fresh examples and checks your understanding. Personal attention, flexible timings in your time zone and affordable plans that fit your budget.

Talk to our team

Free self-study lessons

Short lessons in plain English with examples you run in your browser, quizzes and practice. Learn at your pace, and add a mentor whenever you want help.

Python basics lessons

How it works

Your learning journey

Four simple steps to start Data Analysis with Python with a mentor. A real person replies to you; there is no booking system.

Step 1: Talk to us
Message us on WhatsApp or email, or fill in the short form. The first chat is free, with no obligation.
Step 2: Get your plan
We listen to your goal, level and deadlines, then suggest a plan that fits your budget and timings.
Step 3: Learn with your mentor
Online 1:1 sessions with a mentor who knows your course, explains clearly and checks your understanding.
Step 4: Practise & track progress
Keep going between sessions with practice, and watch your confidence grow week by week.

Learn by doing

Projects and practice

Sales performance report
Clean a year of sales data, group it by product, region and month, and chart the trends with a short written summary.
Customer survey analysis
Handle missing answers, recode categories and compare groups with pivot tables and seaborn charts.
Cleaning a messy dataset
Take a deliberately messy file with duplicates, wrong types and mixed date formats and produce a clean, documented dataset.
Public data story
Choose an open dataset, ask three questions, answer them with pandas and charts, and present your findings in a notebook.

Free practice on MentorNxt

Your progress

What you'll be able to do

Milestones from the start, middle and end of the syllabus.

Write small Python programs and run them in a notebook
Early on
Build summary tables by category, month or region
Halfway
Complete an end-to-end analysis and write the findings in plain language
By the end

FAQs

Data Analysis with Python: frequently asked questions

Is there a self-study option?

For the Python programming part, yes. Our free Python lessons teach the basics you need before pandas. The data analysis modules themselves are taught in 1:1 mentoring, so you get help with real datasets.

Do I need to know Python first?

Basic Python helps, but the first module covers what you need. If you have never coded, your mentor can pace the course so you learn Python and pandas together.

Do I need advanced maths?

No. Data analysis needs school-level maths such as averages, percentages and simple charts. Statistics ideas are explained in plain words when they appear.

Which tools and libraries do you teach?

Python, Jupyter notebooks, pandas, NumPy basics, matplotlib and seaborn, plus SQL basics for reading data from databases.

What is the difference between data analysis and data science?

Analysis focuses on cleaning, summarising and explaining data. Data science adds statistics and machine learning models. This course is a strong first step, and our data science course continues from here.

How much does mentoring cost?

1:1 mentoring is personal online tutoring and is affordable, with plans that fit your budget. Tell us your goal and how often you would like sessions, and our team will suggest an option that works for you in a free, no-obligation chat.

How do I get started?

Message us on WhatsApp or email, or fill in the short form on this page. Our team replies, talks through your goal and level, and arranges a mentor at flexible timings in your time zone. You can start this week.

Free, no-obligation chat

Talk to our team about Data Analysis with Python

Tell us what you need. Our support team replies within one working day, by email or WhatsApp, whichever you prefer, and arranges your mentor.

Prefer to talk first?

Message the support team directly. We usually reply the same day.

Chat on WhatsApp Email us

Our mentors teach and explain. They never complete graded work, tests or assessments for students.

Keep going

Syllabus references

Last reviewed9 November 2026

Ready to start Data Analysis with Python?

Message us on WhatsApp or email, or fill in the short form. Our team replies with a plan that fits your goal and budget, and you can start this week.

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Data Analysis with Python

Load messy data, clean it, summarise it with pandas, chart it and share clear findings, one practical step at a time.

Level:

Beginner to intermediate

Format:

Free self-study lessons + 1:1 mentoring

Free, no-obligation chat · Affordable plans that fit your budget · Start this week

Learn Data Analysis with Python online

Who this course is for

What you'll learn

Skills you'll gain

Data Analysis with Python syllabus

Python and notebook foundations

Loading and inspecting data

Selecting and filtering

Data cleaning

Transforming data

Grouping and aggregation

Combining datasets

Dates, times and text

Visualisation with matplotlib and seaborn

SQL and Python together

Exploratory data analysis and reporting

How you can learn Data Analysis with Python

Data Analysis with Python mentor, 1:1 online

Free self-study lessons

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

Sales performance report

Customer survey analysis

Cleaning a messy dataset

Public data story

Free practice on MentorNxt

What you'll be able to do

Data Analysis with Python: frequently asked questions

Talk to our team about Data Analysis with Python

Related courses

Data Science with Python

SQL and Databases

Python Programming

Machine Learning with Python

Excel & VBA for Programming

Power BI

Syllabus references

Ready to start Data Analysis with Python?

Learn Data Analysis with Python online

Who this course is for

What you'll learn

Skills you'll gain

Data Analysis with Python syllabus

Python and notebook foundations

Loading and inspecting data

Selecting and filtering

Data cleaning

Transforming data

Grouping and aggregation

Combining datasets

Dates, times and text

Visualisation with matplotlib and seaborn

SQL and Python together

Exploratory data analysis and reporting

How you can learn Data Analysis with Python

Data Analysis with Python mentor, 1:1 online

Free self-study lessons

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

Sales performance report

Customer survey analysis

Cleaning a messy dataset

Public data story

Free practice on MentorNxt

What you'll be able to do

Data Analysis with Python: frequently asked questions

Talk to our team about Data Analysis with Python

Related courses

Data Science with Python

SQL and Databases

Python Programming

Machine Learning with Python