Data Analyst - Basics and expertise for career changers

Introduction - Changing world of work through Industry 4.0

In the first module, you will deal with important basic concepts of digitalization such as Industry 4.0 and Work 4.0. You will learn how Industry 4.0 is changing the world of work and what these changes mean for you. The second chapter of this module deals with the topic of Work 4.0 and provides you with valuable skills for digitalization in the commercial sector. In the final chapter, the topic of Work 4.0 is further specified and you will learn valuable soft skills to master the increasingly digital world of work.

Chapter 1: Industry 4.0 - How is Industry 4.0 changing the way we work tomorrow and what does it mean for me?

Industry 4.0 is the generic term for the digitalization of the entire production process. The digital networking of machines and processes is a key factor in securing and further developing value creation in all manufacturing sectors. This introductory chapter focuses on the technological foundations. You will take a look at the development history of the system, identify individual technical approaches and place them in an overall context. Regardless of whether you are a manager or a specialist: This will equip you for the challenges that the entire industry is currently facing.

• Terms, meaning and history:
  • Industry 4.0
  • Internet of Things (IoT)
  • Digital twins
  • Artificial intelligence (AI)
• Understanding and controlling industrial processes
• Industry 4.0 in the application
• IoT in the world of work and everyday life
• Cybernetics and human-machine interaction in Industry 4.0

Chapter 2: Work 4.0 - skills for digitalization in the commercial sector

This chapter helps you to reflect on current and future challenges in the digital world of work so that you can classify specific opportunities and risks. Furthermore, this chapter gives you the skills to independently acquire and expand your expertise in the field of digitalization. This way, you will never lose touch in the rapidly changing world of work 4.0.

• The basics of digitalization
  • Language of the digital world
  • Digital megatrends
• Digitalization in industry, trade, commerce and services
• Challenges and requirements
• Success-relevant skills for the digital future
  • DigComp - the European competence framework for digitalization
  • Competence areas
• What does my current digital skills profile look like?
  • Competence levels - competence levels
  • The Digital Competence Check (DCC) methodology
  • Self-assessment and determination of learning objectives
• Developing and expanding digital skills

Chapter 3: Work 4.0 - Working in new forms of work

In this chapter, you will focus on important issues and soft skills in the world of work 4.0. You will learn how to organize yourself in a fast-paced world and how to work sensibly when topics change frequently. You will also receive tried-and-tested tips on how to create distance from work when working from home to ensure a real end to your working day. This chapter will enable you to expand and manage the potential and strategies of the world of work 4.0 in order to be successful with your personal resources.

• Features of the new digital world of work and its demands on employees' soft skills
• Self-management, self-motivation and self-organization as key soft skills in the digital world of work
• Communication - a core competence in the digital future
• Digital collaboration - crucial to success for employees in the working world of the future
• Curiosity, willingness to learn and innovative spirit as drivers of digital development

Basic knowledge refresher - mathematical and statistical knowledge

In the second module, you will refresh your mathematical and statistical knowledge. This includes the basics of algebra, basic concepts of probability theory, general normal distribution and basic statistical methods

Chapter 1: Basic mathematical and statistical knowledge

Whether you need to calculate trends for your market research presentation or define the measurement limits of permissible tolerances in quality management: You can't do it without math. Anyone who compiles statistics as part of studies or creates programs to encode data needs mathematics as an indispensable tool. A computer can make the work easier and perform complex calculations, but it cannot generate the underlying formulas. Mathematics is therefore a universal basis for all complex professions, whether in the humanities, engineering, computer science or business. In addition to the basics of algebra from simple to complex topics and their transformation, in this chapter you will deal with statistical methods and distributive calculus.

• Basics of algebra:
  • Term transformation
  • Equations
  • Coordinate systems
  • Diagrams
• Basic concepts of probability theory:
  • Random experiment
  • Relative probability
  • Combinatorics
  • Conditional probability
• Laplacian probability, Bernoulli distribution, binomial distribution, factorial:
  • Tree diagrams
  • Path rules
  • N over k
  • 4 fields table
  • pq formula
• General normal distribution
• Calculations for statistics:
  • Mean value
  • Median
  • Modal
  • Standard deviation
  • Variance
  • span

Project management
In the third module, you will familiarize yourself with the basics of project management. This module focuses on project organization, methods, handling and controlling projects, resource management and project evaluation

Chapter 1: Project management basics
Project management is increasingly becoming a success-determining organizational approach for companies in a wide range of industries and sectors. It enables fast and flexible workflows and makes a significant contribution to increasing the effectiveness and efficiency of a modern company. In this chapter, you will learn the theoretical and practical basics of project management in a compact format. You will learn how to use a company's resources dynamically, even across departmental boundaries. The focus is on starting a project, the methodology of project planning and scope and time management. In addition to project implementation, cost management, controlling and team management as well as project completion are also covered.</p

• Project organization
• Methods
• Processing and control
• Resource management
• Project evaluation

Data skills
In the fourth module, you will acquire basic data and AI skills. With a data-driven mindset, you will understand why data-based decisions are essential and how to implement them successfully with data thinking. You will learn what artificial intelligence can achieve and what is needed for successful AI projects in the company. The module is rounded off with best practices for data storytelling. This will enable you to communicate your data knowledge and data-driven results to your target group.</p

Chapter 1: Data Literacy
You will acquire basic skills to process data profitably in your own company. You will expand your knowledge of the most important data technologies such as big data, artificial intelligence and the Internet of Things. By the end of the chapter, you will have a holistic overview of the entire data processing process and will be able to integrate data into your day-to-day work.

1. big data
You will be introduced to the world of big data. A special focus is placed on the flow of a typical data processing procedure from the generation to the analysis of the data. In this context, you will recognize the added value and relevance of high-quality data in the form of structured data. You will receive rules and guidelines on how to establish and ensure the required data quality.</p

• Definition and use cases of big data
• Using data as a decision-making tool
• Data processing process I:
  • Overview of the entire process
  • Focus on structured data
  • Aggregation and data quality
• Data visualization I: General best practices
• Understanding column and pie charts

2. datafication
You will acquire the skills to recognize added value and the possibilities of data. You will learn the relevant points when implementing new data infrastructures and acquire knowledge of data visualization to communicate results as clearly and unambiguously as possible.

• Get to know data storage in the company
• Data processing process II:
  • Focus on different data models
  • How to deal with missing data
• Data visualization II:
  • Understanding histograms
  • Best practices for line charts
  • Location information through maps

3. Artificial intelligence
The terms artificial intelligence, machine learning and deep learning will be demystified for you. You will be able to assess what artificial intelligence can already do and what it cannot yet do. By learning technical terms, you will be able to communicate at eye level with employees in the specialist departments for data analysis.</p

• Definition and application of artificial intelligence
• Focus on machine learning
  • Supervised learning: regression and classification
  • Unsupervised learning: clustering
  • Relevance of data quality
• Data visualization III:
  • Interpreting scatter plots
  • Determine influencing factors from regression lines

4. Internet of Things
You realize that data from sensors also provides important insights. You will also gain an idea of how the networking of devices can lead to the improvement of processes or new value creation opportunities.

• Internet of Things and Industry 4.0: definition, opportunities and use cases
• Merging data from multiple sources
• Basics of A/B testing
• Presentation and interpretation of data:
  • Basics of storytelling
  • Best practices for data storytelling

Chapter 2: AI Literacy
You will receive a practical introduction to the field of artificial intelligence. You will gain the necessary core skills to confidently understand existing and new AI applications based on various scenarios from everyday business life, to successfully transfer them for your company and to interact with them.

• 1. introduction to AI
  You will enter the world of artificial intelligence. You will learn what is behind the term and how to differentiate AI from machine learning and automation. You will learn about the benefits of automation for you and your company and build up basic knowledge with your first interactive tasks.</li
• Definition of artificial intelligence, automation and machine learning
• Review of the history of AI
• Interplay of different technologies

2. Typical applications of AI
You will learn how to apply AI using two typical use cases and how to interact with AI-based systems. This will enable you to generate added value for companies, e.g. by increasing efficiency.</p

• Configuration of a language assistant through the application of NLP (Natural Language Processing)
• Use of AI in recommendation systems with the help of machine learning

3. Advanced applications of AI
You will focus on advanced applications of AI with the help of a practical task on an intelligent industrial robot and take a look into the future of AI with an expert interview.

• Assembling a car with the help of the collaborative robot Cobot
• Expert interview on the AI of the future

Chapter 3: Data Driven Management
In the Data Driven Management chapter, you will receive an overview of the most important data roles and valuable recommendations for action from renowned data experts from industry and business. You will round off the chapter with a toolkit for implementing data strategies and building the necessary structures and core competencies in the company with the help of data thinking.

<p
1. Data Strategy
   You will gain an overview of the benefits for a company that uses data to make decisions
.
• Data-based decision making
• Communication and data literacy
• Adding value with data
2. Data Thinking
   You will receive an introduction to the topic of data thinking and the associated process steps
.
• Understanding companies
• Understanding users
• Understanding data
3. Data management
   You will learn which changes in the company enable a successful data strategy
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• Data strategy and change management
• Data processing and data quality
• Data competence and job roles
• Organization within the company

Chapter 4: Data storytelling
In six steps, you will learn the basics of data visualization and the most important techniques for effectively communicating data-driven results and abstract statistical concepts in order to successfully implement a presentation that is appropriate for the target group.

<p
1. Introduction
   You will learn why data storytelling is important and where and how it is used. We explain why it is important to have good presentation skills and give you an initial overview of data storytelling as a data visualization method.</li
2. Determine the context
   We will show you the most effective methods for creating presentations as well as valuable aspects that influence your presentation. Using a practical handout, we explain relevant techniques for creating good data visualizations. We go into more detail about the framework conditions, the audience, the goal and the tone of the presentation.</li
3. Tell your data story
   You will learn how to derive and formulate stories in a data-driven context. You will understand why stories are important and learn how to differentiate between the most important story writing techniques using an example. Finally, you will familiarize yourself with the storyboard as an overarching technique.</li
4. Choosing the right illustration
   You will learn about the advantages and disadvantages of the most important diagram types. You will be provided with a guide for selecting suitable visualizations.</li
5. Unclutter your illustrations
   Here you will explore the relevance and techniques of "decluttering" in order to effectively design visualizations based on psychological mechanisms. You will be provided with best practices for directing attention with illustrations.</li
6. Final project
   In the final project, you will use the skills you have learned to build a storyboard. You will create a suitable storyboard in a business-relevant use case and present it in a finished presentation. We will give you final feedback on your project in a "directors cut" (annotated sample solution).</li

Tools for data analysis and data visualization
In this module, you will learn the basics of relational databases and database models, carry out simple data queries and deepen your SQL knowledge in a project work with a final presentation. In the further course of this module, you will learn the basics of data visualization with Google Data Studio. This includes the theory of data visualization, the theory of models, getting started with Google Data Studio and the implementation of complex data visualization projects.</p

Chapter 1: SQL Basics for Software Developers
SQL is the standard for all database systems and is therefore one of the basic skills required in many areas, including software development. In this chapter, you will gain a theoretical and practical insight into the syntax of SQL and its possible applications. With this course, you will acquire basic knowledge of relational database systems as well as application-ready skills in the use of the database language SQL. In addition to carrying out practical tasks in a practice database, you will also realize a small database project of your own. This will teach you about SQL topics in a practical way, from creating a data structure, executing DML statements and simple queries to some advanced topics such as stored procedures.

> Introduction:
- Basics of database architectures
- Introduction to MySQL and MariaDB
- Methods for database design
> Relational data models:
- Creating a database
- Creating and managing tables
- Use of suitable data types
- Inserting, updating and deleting data (DML)
> Simple database queries:
- Operators in WHERE clauses
- ORDER BY and GROUP BY
- Application of stand functions
- Data queries across multiple tables using joins
> Advanced topics:
- Primary and secondary keys
- Foreign keys
- Stored procedures and stored functions indexes
> Project work with final presentation

Chapter 2: Data visualization with Google Data Studio
Data visualization is the key to analyzing and evaluating complex data. In today's business world, more and more data is being generated and compiled in less and less time in order to derive benefits, generate a basis for decision-making or create competitive advantages. The different formats and storage forms of this data are often the first hurdles. Once you have succeeded in making this data available in the same formats, you are faced with the challenge of the relationships and scalability of such data. Who should use it and what insights can be gained from the data sets? How does the type of presentation influence the interpretation of the data? You need more than a simple standard Excel chart. In this course, you will learn the fundamental theoretical principles of data visualization and gain initial practice with powerful tools to master complex data visualization projects.</p

> Theory of data visualization
- Correlation, relation, dimension, dynamics, trend
- Differentiation
- Visualization forms 2D and 3D
- Complex comparisons in different dimensions
> Theory of the models
- Target groups
- Psychology and perception
- Expectation, interpretation, responsibility
> Google Data Studio
> Complex data visualization projects
- Formulating objectives, questions and target groups
- Analyze and provide data pools
- Preparing and presenting data
- Evaluation phases

Data analyst with communication and presentation techniques
In the sixth module, you will learn how to independently clean, process and visualize data and make business-relevant predictions. You will acquire highly sought-after skills in the programming language Python and in the field of machine learning

Chapter 1: Python Beginners Guide
You will familiarize yourself with the interactive learning environment - StackFuel's Data Lab - and the Python programming language

1. Python Basics
   This is your first time in the Data Lab and you will familiarize yourself with the basics of programming. You will learn to store numbers and texts as variables in Python and to bundle them as groups in lists. The correct way to read error messages rounds off your basic Python knowledge.</li
2. Programming basics
   In the second part, you will build on your programming basics. This chapter focuses on the use of functions and methods as well as sequence controls using conditions.</li
3. Loops and Functions
   The last part of the basics chapter is dedicated to flow control using loops. You will expand your range of functions by importing additional Python packages and gain an insight into versioning code with Git. By the end of the chapter, you will be familiar with the most important programming concepts that are important for working as a data analyst.</li

Chapter 2: Data Analytics with Python
You will learn how to access, filter and merge new data sources. You will practise making company data accessible to target groups with appealing visualizations and independently carrying out classic data processing procedures (importing, filtering, cleaning, processing and visualizing data).

1. Data pipelines (pandas)
   This section teaches the efficient use of Pandas - the standard tool of a data analyst in Python. You will learn how to use it to read, clean and aggregate data in CSV files.</li
2. Data Exploration (Matplotlib)
   You practice visualizing different levels of data using marketing data. Numerical data is visualized as histograms and scatter plots, while categorical data is visualized as bar charts and pie charts.</li
3. Predictions (Statistics)
   You will learn statistical terms such as median and quartiles based on product evaluations. You will identify outliers and create simple predictions using linear and logistic regression.</li
4. Internal Data (SQL)
   You will learn to read databases using the example of a personnel database and to formulate standard SQL queries.</li
5. External Data (API)
   You use Python to access information such as web pages and APIs designed by StackFuel on the Internet.</li
6. Advanced Jupyter
   You will learn Jupyter functionalities and solve advanced visualization problems such as live updates and interactivity in the context of a stock market scenario.
7. Exercise Project
   You will analyze a New York cab data set with over one million trips and use your Python skills as independently as possible to answer given questions.
8. Final Project
   You will analyze the customer churn of a telecommunications company. You will run through the entire data pipeline independently and answer typical questions. You will present your final project in a 1-on-1 feedback session with the StackFuel mentoring team
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Chapter 3: Machine Learning Basics
You will create data science workflows with sklearn, evaluate your model performance using suitable metrics and become aware of the problem of overfitting.

1. Supervised learning (regression)
   Using linear regression, you will learn how to use the Python package sklearn. You will also deal with the assumptions of the regression model and the evaluation of the forecasts generated. In this course, the bias-variance trade-off, concepts of regularization and various measures of model quality are also clarified.</li
2. Supervised Learning (Classification)
   You will be introduced to classification algorithms using the k-Nearest Neighbors algorithm and learn how to evaluate the algorithm and assess the classification performance. You will optimize the parameters of your model, taking into account the division of the data into training and evaluation sets.</li
3. Unsupervised Learning (Clustering)
   You will learn about the k-Means algorithm as an example of an unsupervised learning algorithm. You will critically examine the assumptions and performance metrics of the algorithm and take a brief look at an alternative to k-Means clustering.</li
4. Unsupervised Learning (Dimensionality Reduction)
   You will learn how to use Principal Component Analysis (PCA) to reduce the dimension of the data and use PCA to generate uncorrelated features from the original data. In this context, the topic of feature engineering is examined in more detail and new features are created from the old ones.</li
5. Outlier Detection
   You will learn about different approaches to identifying outliers and understand how to deal with these unusual data points. You will use robust measures and models to minimize the influence of outliers
.

Communication and presentation
In the additional module, you will learn many helpful methods and tricks for communicating and presenting content to your target group. The focus is on teaching you new skills in visualizing, presenting and moderating. We will also show you how to follow up moderations and presentations in the best possible way.

Chapter 1: Skillful moderation - creative presentation
Moderating and presenting are everyday professional tasks that can often be more complicated than expected. What works in one situation can go wrong in the next. That's why you'll learn how to professionally prepare and deliver presentations that are tailored to your target group and situation in this additional chapter.

Module overview

• Visualize:
  - Purpose, means, craft
  - Planning, storyboard, implementation
• Presenting:
  - Topic, goal, target group, framework and means
  - Procedure and organization, clothing, voice and posture
  - Dealing with disruptions
• Moderating:
  - Topic and objective, target group and setting
  - Procedure and organization
  - Topic and participant orientation
• Follow-up of moderations and presentations