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Data Analyst

Further training for the job role of Data Analyst

Certificate of completion

(Cross) boarding

part time

German, English

5.355

Course description

The certified Data Analyst training - Focus Python enables you to independently clean, process and visualize data and make company-relevant predictions.

You will gain highly sought-after skills in the Python programming language online to qualify you for the job role of Data Analyst or another analytical job role such as Business Intelligence Analyst or Financial Analyst upon successful completion of the career path.

In this training you will learn

Basic Python Skills

Data Analytics with Python

Basic Statistics Skills

Develop and filter data sources
Consolidate and prepare data professionally
Independently perform advanced data analysis using descriptive
Perform statistics
Writing simple scripts in the Python programming language
Make simple predictions
Best practices for effective data visualization

Target audience

The advanced training Data Analyst - Focus Python is suitable for anyone who wants to learn Python as a programming language and perform data analyses independently. You should have a basic motivation for data analytics and programming. The Data Analyst training is also suitable for career changers.

Requirements for participation

Assessment test
Basic knowledge of mathematics & statistics

Modules

1
Python Beginners Guide

Objective: Introduction to programming with Python

Description: Participants familiarize themselves with the interactive learning environment - the StackFuel Data Lab - and the Python programming language.

Chapter 1: Python Basics
Objective: Learn the basics of programming
Description: Participants navigate through the Data Lab for the first time and get to know the basics of programming. They learn to store numbers and text as variables in Python and to store these as groups in lists. To complete these basics, participants also learn how to read error messages correctly.

Chapter 2: Programming Basics
Objective: Build fundamental programming skills
Description: Participants continue to build their fundamental programming skills. This chapter focuses on applying functions and conditional flow controls.

Chapter 3: Loops and Functions
Objective: Expand knowledge of flow control with loops
Description: The last chapter of the basics module is dedicated to flow control using loops. Participants broaden their abilities by importing additional Python packages. By the end of the chapter, participants know the most important programming concepts that are important working as a data analyst.

2
Data Analytics with Python

Objective: Independent collection, analysis and visualization of data with Python

Description: Participants learn to access, filter, and merge new data sources. They practice making company data available in attractive visualizations tailored to the target audience, and independently carry out classic data processing (importing, filtering, cleaning, and visualizing data).

Chapter 1: Data Pipelines (Pandas)
Objective: Efficient use of Pandas for data analysis
Description: This chapter teaches the efficient use of Pandas – the standard data analysis tool in Python. Participants learn to use it to read, clean, and aggregate data in CSV files.

Chapter 2: Data Exploration (Matplotlib)
Objective: Practice visualizing different types of data
Description: Participants practice visualizing different types of data using marketing data. Numeric data is represented as histograms and scatter plots, while categorical data is represented as column and pie charts.

Chapter 3: Predictions (Statistics)
Objective: Learn statistical concepts and make predictions
Description: Participants learn statistical concepts such as the median and quartiles using product ratings. They identify outliers and make simple predictions using linear and logistic regression.

Chapter 4: Internal Data (SQL)
Objective: Learn SQL for data retrieval
Description: Participants learn to read databases using a human resources database as an example and formulate standard SQL queries.

Chapter 5: External Data (API)
Objective: Access external data sources using APIs
Description: Participants use Python to access information such as web pages and APIs designed by StackFuel on the Internet.

Chapter 6: Advanced Jupyter
Objective: Learn advanced Jupyter functionalities
Description: Participants learn Jupyter functionalities and solve advanced visualization problems such as live updates and interactivity in the context of a stock market scenario.

Chapter 7: Exercise Project
Objective: Apply Python skills to a practical project
Description: Participants analyze a New York taxi data set with over one million trips and use their Python skills as independently as possible to answer certain questions.

Chapter 8: Final Project
Objective: Complete a comprehensive data analysis project
Description: Participants analyze customer churn for a telecommunications company. They work through the entire data pipeline independently and answer typical questions. They then present their project in a 1-on-1 feedback session with the StackFuel mentor team.

FAQ

Why should I choose a career in data, coding or AI?

The demand for data experts is high. Around 4 million data experts will be needed in Europe by 2025. In Germany alone, 149,000 IT jobs are currently vacant. The demand for data and AI experts in particular continues to grow enormously.

But a decision for a data career is so much more than just a safe decision for the future! As a data expert, you deal with powerful, socially relevant topics, are a tech professional, and are communicative and creative at the same time. The profession is varied, can be combined with most other professions and offers an attractive salary. And most importantly, with us it can be learned unerringly!

Am I qualified to work in this profession after further training as a data analyst and data scientist and can I make a lateral move?

Yes, after successful completion of the training, you will receive a certificate of completion from us that you can show in your job applications. Data Analysts and Data Scientists are desperately sought after in many business sectors. Even without relevant work experience, your chances of finding an entry-level job are good. In addition, there are analysts in almost every industry who have different job titles, but the skills you need are the same as those of a data analyst or data scientist.

Can I really organize my learning time freely?

Yes, our online training courses are designed to offer you the greatest possible flexibility. In general, we recommend that you plan six to eight hours per week for learning. When you want to schedule this time is up to you and is not prescribed by us. In our career paths, the Data Analyst and Data Scientist course, we offer live webinars where you can ask our mentors questions, but you don't have to attend if it doesn't fit into your schedule.

testemonial_picture_

The StackFuel Data Lab offers me real added value. Here you can feel the practical relevance particularly well. The tasks were always clearly described and presented. So I always knew what I had to do. The course itself was a great experience!

Alexander Gross
Data Analyst at AIC Portaltechnik

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The greatest added value for me is the practical relevance. Thanks to StackFuel, I can quickly implement what I've learned and adapt it for myself. That is the real learning success behind the online courses.

Lutz Schneider
Strategic IT Buyer at Axel Springer SE

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The content of StackFuel's online course was very practical. There were plenty of good examples and projects. I found that very interesting and educational. Since the course, my day-to-day professional life has changed significantly: I am now a data analytics specialist in my department.

Jaroslaw Wojciech Sulak
Specialist for data analysis at IAV GmbH

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The user-friendly and flexible Python programming course has completely changed my view of complex data structures. Thanks to the sustainable and well thought-out learning concept as well as the seamless application of the learning content in the development environment, I can now implement the new things I've learned in my day-to-day work in test automation in more detail and process data more easily and efficiently than before.

Jenny Lindenau
Technical Manager Test Management at Bank Deutsches Kraftfahrzeuggewerbe GmbH

Let's start with a consultation.

Our consultants will be happy to help you and answer all your questions. Free of charge and without obligation. We look forward to meeting you.

5.355*

(incl. VAT)

0 € with education voucher

English