AGENTUR FÜR ARBEIT Data Analytics Program Curriculum

A learning experience that’s as rigorous and in sync with the industry as it is suited to beginners and upskillers alike.

Curriculum overview

Part-time
Full-time
Intro to Data Analytics
1.4 months
Data Immersion
8.3 months
Intro to Data Analytics
0.7 months
Data Immersion
4.1 months
Intro to Data Analytics
Data Immersion

This course will take you through ten tasks leading up to one main project: a descriptive analysis of a video game data set to inform product development and sale strategies.

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1.1 Data Analytics in Practice

Learn what data analysts do and get ready to kick off your own analysis.

1.2 Introduction to Excel

Get to know Excel and learn how to sort, filter, format, organize, and visualize data.

1.3 Understanding Your Data Set

Analyze and describe your data set, then identify sources of bias.

1.4 Cleaning Your Data

Identify errors in your data and learn how to clean your data and minimize issues.

1.5 Grouping & Summarizing Your Data

Create and manipulate pivot tables and learn more advanced Excel skills.

1.6 Introduction to Analytical Methods

Explore different approaches to data analytics and the role of statistics.

1.7 Conducting a Descriptive Analysis

Conduct a descriptive analysis by applying statistical methods in Excel.

1.8 Developing Insights

Learn how to form hypotheses about data sets, and to generate useful insights.

1.9 Visualizing Data Insights

Practice version control with Git.

1.10 Storytelling with Data

Learn to present the results of your analysis in compelling ways.

Immerse yourself into the mindset, processes, and tools that data professionals use every day. You’ll complete a total of six projects (achievements) consisting of several tasks each.

curriculum curriculum-box heading image
Achievement 1
Achievement 2
Achievement 3
Achievement 4
Achievement 5
Achievement 6
Preparing & Analyzing Data

Learn how to interpret business requirements to guide your data analysis and begin developing and designing your data project. Here’s what you’ll learn:

1.1

A Brief History of Data Analytics

1.2

Starting with Requirements

1.3

Designing a Data Research Project

1.4

Sourcing the Right Data

1.5

Data Profiling & Integrity

1.6

Data Quality Measures

1.7

Data Transformation & Integration

1.8

Conducting Statistical Analyses

1.9

Statistical Hypothesis Testing

1.10

Consolidating Analytical Insights

Data Visualization & Storytelling

Explore the different types of data visualization and what they can be used for, as well as some best practices to keep your visualizations accessible and easily interpretable.

2.1

Intro to Data Visualization

2.2

Visual Design Basics & Tableau

2.3

Comparison & Composition Charts

2.4

Temporal Visualizations & Forecasting

2.5

Statistical Visualizations: Histograms & Box Plots

2.6

Statistical Visualizations: Scatterplots & Bubble Charts

2.7

Spatial Analysis

2.8

Textual Analysis

2.9

Storytelling with Data Presentations

2.10

Presenting Findings to Stakeholders

Databases & SQL for Analysts

Develop database-querying skills while mastering SQL, the industry-standard language for performing these tasks in the real world.

3.1

Intro to Relational Databases

3.2

Data Storage & Structure

3.3

SQL for Data Analysts

3.4

Database Querying in SQL

3.5

Filtering Data

3.6

Summarizing & Cleaning Data in SQL

3.7

Joining Tables of Data

3.8

Performing Subqueries

3.9

Common Table Expressions

3.10

Presenting SQL Results

Python Fundamentals for Data Analysts

Get hands-on with Python—the go-to language used by data analysts to conduct advanced analyses. Here’s what you’ll learn:

4.1

Introduction to Programming for Data Analysts

4.2

Jupyter Fundamentals & Python Data Types

4.3

Introduction to Pandas

4.4

Data Wrangling & Subsetting

4.5

Data Consistency Checks

4.6

Combining & Exporting Data

4.7

Deriving New Variables

4.8

Grouping Data & Aggregating Variables

4.9

Intro to Data Visualization with Python

4.10

Coding Etiquette & Excel Reporting

Data Ethics & Applied Analytics

Learn how to identify and address data bias, data privacy, and data security. You’ll also explore big data analysis, machine learning, and data mining.

5.1

Intro to Big Data

5.2

Data Ethics: Data Bias

5.3

Data Ethics: Security & Privacy

5.4

Intro to Data Mining

5.5

Intro to Predictive Analysis

5.6

Time Series Analysis & Forecasting

5.7

Using GitHub as an Analyst

5.8

Preparing a Data Analytics Portfolio

Advanced Analytics & Dashboard Design

Complete an analysis project using data of your choosing, and build on your advanced analytics skills by taking a dive into machine learning and regression analysis.

6.1

Sourcing Open Data

6.2

Exploring Relationships

6.3

Geographical Visualizations with Python

6.4

Supervised Machine Learning: Regression

6.5

Unsupervised Machine Learning: Clustering

6.6

Sourcing & Analyzing Time Series Data

6.7

Creating Data Dashboards

Intro to Data Analytics

This course will take you through ten tasks leading up to one main project: a descriptive analysis of a video game data set to inform product development and sale strategies.

1.1 Data Analytics in Practice

1.2 Introduction to Excel

1.3 Understanding Your Data Set

1.4 Cleaning Your Data

1.5 Grouping & Summarizing Your Data

1.6 Introduction to Analytical Methods

1.7 Conducting a Descriptive Analysis

1.8 Developing Insights

1.9 Visualizing Data Insights

1.10 Storytelling with Data

Data Immersion

Immerse yourself into the mindset, processes, and tools that data professionals use every day. You’ll complete a total of six projects (achievements) consisting of several tasks each.

about ux

Built on proven learning theories and industry expertise

Dive into a comprehensive and varied learning experience designed to take you from beginner to pro data analyst.

Each course is packed with reading materials and supporting videos, audio learning options, and more.

Our instructional designers work hand-in-hand with seasoned experts in the field to keep our curriculum rooted in proven learning theories, and in-sync with the industry.

Graduate portfolios

See some of the incredible work our students complete during the Data Analytics Program.

Create beautiful work with industry-standard tools

Data Analytics Tools

We’ve partnered up with industry-standard tool providers to make sure you have access to the tools you might use on the job. Through free trials and special discounts available to CF students, you’ll be able to try out a variety of tools to see what works best for you. Check out our perks page or Course Prep in your course for more information on tool discounts.

What our graduates have to say

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How to take our Data Analytics Program
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1

Schedule an appointment with the Agentur für Arbeit

If you haven’t been in touch with the job center before, you can find your local center simply by searching online, e.g. “Agentur für Arbeit Berlin” or “Agentur für Arbeit near me”. Once you’ve made an appointment, you’ll be assigned an advisor.

2

Prepare for your appointment at the Agentur für Arbeit

Use our full application guide to prepare for your appointment at the Agentur für Arbeit and convince your advisor to approve your participation in the course. It covers the documents you need for the appointment.

Download our guide:

Request your personal course proposal from CareerFoundry. You might have to provide this document to the Agentur für Arbeit. It only takes a few minutes!

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1

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It takes 10-14 days to complete the enrolment process, so please bear this in mind when choosing your program start date.

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