What do you learn in the Python for Data Analysis course?

You will learn to import, clean, analyse and visualise data files with Python. You will work with numpy, pandas, pyplot and seaborn, and get to know time series, predictive analysis and web scraping with Beautiful Soup.

What prior knowledge do you need for this Python course?

You have a basic knowledge of Python and some programming experience. You know what variables, lists, functions and loops are. A knowledge of statistics is an advantage, but not a requirement.

Which Python libraries are covered?

You will work with numpy for numerical calculations, pandas for dataframes, pyplot and seaborn for visualisation, Bokeh for interactive charts and Beautiful Soup for web scraping.

Do you work on your own laptop during the course?

You can work on your own laptop, with Python and Jupyter Lab installed in advance. Would you rather not? Then Learnit will provide a practice laptop for you.

Do you receive a certificate after this course?

Yes, on completion you receive a Learnit certificate of participation. This certificate confirms that you have taken the Python for Data Analysis course.

What is the difference between data analysis in Python and in Excel?

Excel works perfectly well for smaller datasets and simple analyses. Python is faster with large datasets, repeatable (you can run your code again on new data) and offers far more possibilities for statistics, machine learning and automation.

Why is pandas so popular for data analysis?

Pandas offers the DataFrame: a table-like structure in which you can quickly filter, group, merge and edit rows and columns. It reads CSV and Excel effortlessly and works seamlessly with numpy and visualisation libraries such as seaborn.

What is a time series and when do you use one?

A time series is a sequence of measurements over time, for example daily sales figures or hourly temperatures. You use time series to discover trends, seasonal influences and patterns, and to forecast future values.

What is data cleaning and why is it important?

Data cleaning is the tidying up of raw data: filling in or removing missing values, taking out duplicate records and making formats uniform. Without clean data, analyses and predictions are unreliable. This is often where the largest part of your analysis time goes.

What is web scraping with Beautiful Soup?

Web scraping is the automatic retrieval of data from websites. With Beautiful Soup you parse HTML pages and extract specific text, tables or links. Useful when no API is available and you still need data from a website for your analysis.

What does the Python for Data Analysis course cost?

You will find the current price at the top right of this course page, together with the schedule, location and delivery format. For in-company or bespoke courses we are happy to provide a quotation on request.

Python for Data Analysis course

Name: Python for data analysis
Price: 1875.0 EUR
Availability: InStock

Python is a powerful language for processing and analysing data. With a few lines of code you can extract insights from large data files that you would never find so quickly in Excel.

Description

Do you work with a lot of data and want to go beyond Excel and PowerPoint? With Python you analyse faster, repeatably and on a larger scale. You read in CSV and Excel files, clean them up, perform calculations and visualise the result in clear charts.

On day 1 you refresh your Python knowledge and dive into numpy for fast calculations on arrays. Day 2 focuses on pandas: editing, merging and cleaning dataframes and visualising them with pyplot and seaborn. On day 3 you work with time series, forecasting and web scraping with Beautiful Soup.

You work in Jupyter Lab and practise with realistic datasets. By the end of the course you can turn a raw dataset into an explanatory analysis and a predictive model.

Topics

Working with Jupyter Lab and notebooks
NumPy arrays, operations and slicing
Universal functions and vector calculations
Creating charts with pyplot
Adding titles, labels and a legend
Visualising with seaborn
Introduction to Bokeh plotting
Pandas Series and dataframes
Reading and writing CSV and Excel files
Creating charts with pandas
Data cleaning
Time series and index manipulation
Trends, seasonal effects and noise
Splitting training and test data
Forecasting time series
Web scraping with Beautiful Soup

Result

After the course you will be able to read in data files, clean them up and analyse them with numpy and pandas. You will create clear charts with pyplot and seaborn, work with time series and build simple predictive models. You will use web scraping with Beautiful Soup to retrieve data from websites for further analysis.

Target audience

The course is intended for developers, analysts, consultants and anyone who wants to analyse and visualise data with Python. You translate raw data into usable insights, charts and interactive presentations. Basic knowledge of and experience with programming in Python are required.

Teaching method

Learnit runs this course in classroom sessions in small groups, led by an experienced trainer. Theory and exercises alternate, so that you apply what you have learned directly to realistic datasets. You work on your own laptop or on a practice laptop from Learnit. Afterwards you receive a Learnit certificate of participation.

Prior knowledge

You have a basic knowledge of Python and some programming experience. Knowledge of statistics is an advantage, but not required.

Comments

On completion of the course, a Learnit certificate is issued as proof of attendance.

In-company

Would you like to organise this training for a whole group or several groups? This can be cost-effective. For example, we can tailor the content entirely to your organisation, and training a whole group is also cheaper.

Are you looking at large numbers of participants or a complete academy? That involves a great deal. Consider planning and communication, for example, or identifying and agreeing the objectives and content of the training. Or differences in level, feedback mechanisms and safeguarding. Fortunately, Learnit has extensive experience in this field (more than 25 years) and we are happy to help you through this process.

Request a quote

Language

The training is given in Dutch as standard. The trainer has a good command of English. English-language course material can be used. With a minimum enrolment of 3 participants, the training can also be given entirely in English.

Number of days:

3 days

Course times:

from 10 am to 5 pm

Study load:

21 hours

Education level:

This training is suitable for all levels.

Exemptions:

Exemptions do not apply to this course.

Preparation time:

No preparation time applies to this course.

Certificate:

At the end of the course, a certificate is issued as proof of participation.

The best training for you

At Learnit you will always find a course that takes you further. From (technical) IT skills to communication or personal leadership. You can also choose the training format that suits you best: a classroom course, a tailor-made course at your own office, an online course or an e-learning. We have the right course and the right format for everyone. Because learning in the way that suits you best delivers the best results.

Moving forward together

Taking a training course at Learnit means learning together. Together with your experienced trainer, together with your course group or together with your colleagues. And whether you take the training at home, at your office or at one of our training locations, we are always available to help you. To share our knowledge with you in a way that is genuinely useful to you. Getting further together through personal attention and understanding each other well; that is what we believe in.

Apply it directly in your work

Learning theory from a book that you can then hardly use in your work? Not at Learnit! Whichever training you take with us, you can put it to use at your workplace straight away. Because of the unique learning objectives you set together with the trainer at the start of the training. Because of the alternation between theory and (interactive) practical assignments. And by bringing the challenges you face in your daily work along to the training.