Short course
Python
In around ten years, Python has become the reference language for data analysis, automation and data science. Its syntax is close to everyday English, which makes it the most accessible language for someone who has never programmed. It is free, runs on every system, and has a huge community producing libraries for almost every need.
- Duration
- 5 sessions, 18 hours
- Format
- In person or online
- Credential
- Certificate of attendance
Tuition65,000 XOF
By the end of the programme, you will be able to
- Work in a Google Colab notebook and install Anaconda for offline use
- Write, run, correct and document a script in a notebook, online and offline
- Master the basics of the language: variables, types, conditions, loops, functions
- Read and write Excel, CSV and JSON files with code
- Manipulate a dataset with the pandas library: select, filter, sort, group
- Clean a dataset: missing values, duplicates, inconsistent types, badly typed text
- Merge several data sources and produce summary tables
- Calculate descriptive statistics and carry out common statistical tests
- Produce professional charts with matplotlib and seaborn
- Automate a repetitive task: process every file in a folder in one go
Who it is for
Target audience
The course is open to anyone, whatever their profession: student, researcher, monitoring and evaluation officer, statistician, health worker, civil servant, consultant or private sector employee. No profile is favoured and the fee is the same for everyone.
Prerequisites
Three requirements:
- Hold at least a three-year degree (BAC+3), completed or in the final year, in any field
- Failing that level, already be familiar with data analysis in a professional context: processing survey datasets, producing statistics, monitoring indicators
- Have a personal laptop and a Gmail address, as a Google account is needed to access the online notebooks
No programming experience is required: the course assumes the participant has never written a line of code. However, the level requirement above is not a formality. In eighteen hours, it is not possible to build both the logical reflexes of programming and a command of data analysis. Prior experience of Excel, in particular structured tables, formulas and the IF function, is the best passport for following this course comfortably.
Detailed program
5 sessions · 18 h of teaching
- Introduction to Google Colab: opening a notebook, running a cell, saving to your Google account
- What Python can do, and where it clearly outperforms Excel
- The notebook: code cells, text cells, execution, execution order and its pitfalls
- Writing and running your first instruction, displaying a result
- Variables: naming, assigning, reassigning, naming conventions
- The fundamental types: integer, decimal number, string, boolean
- Arithmetic operations and string operations
- Comments and why they matter more than the code itself
- Reading an error message: the traceback, the faulty line, the type of error
- The most common beginner mistakes and how to fix them
- Installing Anaconda to work offline, and switching between environments
- Guided exercise: write a small program that calculates indicators
- Lists: creating, accessing by index, slicing, adding, removing, iterating
- Dictionaries: the key-value logic and its practical uses
- Tuples and sets, and when they are preferable
- Conditions: if, elif, else, and building a readable test
- Comparison operators and logical operators
- The for loop: repeating a process for each element of a collection
- The while loop and the risk of an infinite loop
- Functions: defining, calling, passing parameters, returning a result
- Why write a function rather than copy the same block three times
- Libraries: importing, using, understanding dot notation
- Reading and writing a text file, a CSV file, an Excel file
- Automatically going through every file in a folder
- Handling an error cleanly with exception handling
- Guided exercise: automate identical processing of twelve monthly files
- The pandas library and the concept of a data frame
- Importing a dataset from Excel, CSV, JSON or a web address
- Import pitfalls: encoding, separator, header row, wrongly detected types
- Exploring a dataset: dimensions, first rows, column types, statistical summary
- Selecting a column, several columns, a range of rows
- Filtering observations on one or more conditions
- Sorting on one or more columns
- Creating a new column calculated from others
- Recoding a variable and grouping a continuous variable into classes
- Applying a custom function to a whole column
- Spotting and handling missing values: counting, removing, replacing
- Detecting and removing duplicates
- Cleaning text: extra spaces, case, accents, stray characters
- Converting dates and extracting the year, month and day of the week
- Grouping and aggregating: the logic of grouping by category
- Building a pivot table in a single line of code
- Merging two datasets on a common key, stacking several files
- Exporting the result to Excel across several sheets
- Guided exercise: clean and summarise a survey dataset of several thousand rows
- Descriptive statistics with pandas: counts, percentages, means, dispersion
- Statistics by subgroup and multi-level summary tables
- The numpy library and calculations on large volumes
- Statistical tests with scipy: chi-square, t-test, ANOVA, correlation
- Reading a p-value correctly and writing the interpretation
- Introduction to linear regression and reading its coefficients
- Visualisation with matplotlib: figure, axes, titles, legends
- Essential charts: bars, lines, histogram, box plot, scatter plot
- The seaborn library and statistical charts in one line
- Comparing several groups on the same chart
- Readability rules: palette, scale, information density, explicit titles
- Exporting a chart as a high-resolution image for a report or presentation
- Automatically generating a series of charts, one per region or category
- Assembling a reproducible analysis report in a single notebook
- Guided exercise: produce the complete, illustrated analysis of a real dataset, from raw file to report
- Presentation of a complete, realistic case shared by the whole group
- Group scoping: which processing steps, in what order, with which libraries
- Writing the script step by step, with each participant coding on their own computer
- Regular checkpoints to compare solutions and discuss differing approaches
- Group correction of the errors encountered, reading other people's code being one of the best ways to learn
- Documenting and formatting the script so it remains reusable
- Guided adaptation of the script to a second dataset, to check it is truly generic
- The trainer moves between workstations throughout the session
- Final summary: the standard approach to repeat with any new file
Assessment and certification
There is no final assessment in this course. As the training is short and intensive, it aims to build practical skills, not to validate a level through an exam.
A training attestation is given to each participant at the end of the fifth session. It is issued by Waaldé Impact, signed by the trainer, and states the course title, the 18-hour duration, the dates and the list of modules followed.
Upcoming sessions
Next dates being prepared
Leave your email: we will let you know as soon as registration opens.
Frequently asked questions
65,000 XOF per participant. Training for a team or an organisation is quoted on request.
5 sessions, 18 hours.
Yes: you can attend onsite or online, as you prefer.
Three requirements: hold at least a three-year degree (BAC+3), completed or in the final year, in any field ; failing that level, already be familiar with data analysis in a professional context: processing survey datasets, producing statistics, monitoring indicators ; have a personal laptop and a Gmail address, as a Google account is needed to access the online notebooks ; no programming experience is required: the course assumes the participant has never written a line of code. However, the level requirement above is not a formality. In eighteen hours, it is not possible to build both the logical reflexes of programming and a command of data analysis. Prior experience of Excel, in…
A certificate of attendance is issued at the end of the course.
Fill in the online enrolment request: no payment is taken on the site. The firm reviews your application, then contacts you to confirm your place and the payment terms. No session is scheduled yet: leave your email on this page to be told when registration opens.
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