Flexible online learning
At your own time and pace
With personal guidance
Take a look at some data analyst vacancies and you'll see. Almost every description shows that, as a data analyst, you must have gained knowledge and experience with Python. It is therefore not without reason that it is the most popular programming language in this field. Almost anything is possible with Python. For example, you can automate tasks, develop games, make advanced data analyses and develop web applications.
Most likely, you're asking yourself what specific Python skills are important to learn if you want to become a data analyst. In this blog, we cover the 4 most essential Python skills for every data analyst.
Why is python the most popular programming language among data analysts? Well, that's partly because Python is used to automate repetitive tasks. And there are quite a few repetitive tasks you face as a data analyst. By automating these tasks, you can save a lot of time and costs and thus work more efficiently. By automating tasks, you can also reduce errors and, in many cases, even eliminate them.
In addition to automating the tasks, you can also use Python for another very important part of data analytics, namely data visualization. But that's far from all why this language is so popular among data analysts. It has a relatively simple syntax and is therefore easy to understand by others. It allows you to process large data sets and you can get started with deep learning and machine learning.
Start with the basics of Python (the fundamentals)
Of course, everything we tell you in the paragraph above all sounds very interesting. But like in any field, you need to master the basics before you can go in-depth. Learn to master the fundamentals below before you learn how to visualize data or automate data collection.
These are the Python fundamentals that you need to master before continuing to learn the four essential python skills:
Learn these four essential python skills for the data analyst
Okay, now you have the basics to really get started. Now is the time to learn the most important skills. Don't expect to be able to do it in a day. You will have to keep practicing, practicing and practicing again.
Data is everywhere and companies are only too happy to use it. They collect data in large numbers and analyze it with the aim of converting this data into useful information that can be used to make better business decisions.
Analyzing data is one of the most important parts of data analytics. That is why, as a data analyst, you have a lot to do with this discipline, where you will interpret and edit data. Of course, you could do this the old-fashioned way in excel, but doing your data analysis in Python is not only more convenient and faster.
Here are the most used Python libraries for data analysis:
If you learn to visualize data with Pyton, you can now transform user data into clear analyses.
In short, data analysis, web scraping, machine learning and data visualization are the building blocks in Python that can significantly increase your job opportunities. Would you like to be able to master all these building blocks? Then follow the data analytics course with Python. After this course, you are ready to work as a junior data analyst.
Data is everywhere, but by far the most useful information can be found online. Just imagine, there are 8.83 billion people in the world. Of these, 4.66 billion people are active on the internet. These people are all distributing a lot of data. In 2020, there were approximately 44 zettabytes (21 zeroes) of data on the Internet. The amount of data continues to grow rapidly.
Every second of every day, there is approximately 6.59 GB of internet traffic that adds up to the data counter. It is expected that the amount of data will only increase further. According to Seagate, the Internet will consist of 175 zettabytes of data by 2025. I can already hear you thinking: Zettawattes? 1 Zettabyte equals 1 000 000 000 000 gigabytes, to think that an average email is only 0.2 mb, checking and uploading a photo on Instagram is 3.6 mb and sending a WhatsApp message is only 0.01 MB per message.
So the amount of information you can find on the internet is almost endless. An inexhaustible source of information is, of course, a gold mine for any data analyst. How do you make use of such an inexhaustible source of accessible information? Well, you do that by 'scraping' the web. Web scraping is a technique that removes data from the internet (websites). Sure, you could also get the information manually from websites, but by automating it, you can collect data much faster and more cost-efficiently.
You could write a web scrape script yourself, but you could also use the many packages that exist in Python for data scraping. For example, consider:
Data analytics is all about being able to communicate your findings and data visualization plays a very important role in this. Python is a great tool for visualizing your data. If you can make data visualizations in Python, you are able to create the most complex and/or very specific visualizations.
There are also several packages available in python for visualizing data. Think about:
If you learn to visualize data with Pyton, you can now transform user data into clear analyses.
In short, data analysis, web scraping, machine learning and data visualization are the building blocks in Python that can significantly increase your job opportunities. Would you like to be able to master all these building blocks? Then follow the data analytics course with Python. After this course, you are ready to work as a junior data analyst.
Do you really want to increase your job opportunities? Then you would do well to learn machine learning. Major companies, including Google, indicate that machine learning is the future. This makes it one of the most valuable skills of the moment.
But what exactly is machine learning? Machine learning is an application of artificial intelligence without human supervision. Using machine learning techniques, you teach computers to make valuable predictions. These predictions contain important new data that you can use as a data analyst.
