Diploma in Data Analytics and Data Science in Germany

Launch your career in the fast-growing and dynamic field of Data Science in Germany with EIIET which will be delivered in partnership with eduJournal.

Our specialised teams developed data analytics and data science to support international students who are eager to learn about the data science world and train them in data analytics and data science. Our team is committed to implementing innovative solutions that empower students to achieve their career goals and unearth their full potential. Data science and analytics are closely related, but some essential variations exist. While both fields try to work with data to gain insights, data science often uses data to develop models to forecast future consequences. At the same time, data analytics focuses more on analysing past data with a focus on decision-making in the present.

EIIET is also the platform for online programmes, which are interactive online programmes that are approachable anytime, anywhere in the world. Online courses offer unparalleled flexibility that allows students to access learning materials and complete assignments on their schedule, which is particularly beneficial for those with work or personal commitments.

Our specialised teams developed data analytics and data science to support international students who are eager to learn about the data science world and train them in data analytics and data science. Our team is committed to implementing innovative solutions that empower students to achieve their career goals and unearth their full potential. Data science and analytics are closely related, but some essential variations exist. While both fields try to work with data to gain insights, data science often uses data to develop models to forecast future consequences. At the same time, data analytics focuses more on analysing past data with a focus on decision-making in the present.

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Online learning is an attractive replacement for in-person teaching because it can detach dominant barriers to education, making it more equitable and accessible. In this programme, we'll look into the key benefits of making money online so you can decide if it's the best option for you. After completing the programme, students will get a certificate issued by EIIET.

eiIET is also the platform for online programmes, which are interactive online programmes that are approachable anytime, anywhere in the world. Online courses offer unparalleled flexibility that allows students to access learning materials and complete assignments on their schedule, which is particularly beneficial for those with work or personal commitments.

Online learning is an attractive replacement for in-person teaching because it can detach dominant barriers to education, making it more equitable and accessible. In this programme, we'll look into the key benefits of making money online so you can decide if it's the best option for you. After completing the programme, students will get a certificate issued by eiIET.

As a field of competence, data science is much bigger in scope than the task of conducting data analytics and is considered its career path. Those who work in the field of data science are known as data scientists. These professionals develop statistical models, develop algorithms, train machine learning models, and create frameworks to:

Forecast short- and long-term outcomes.
Solve business problems.
Identify opportunities.
Support the business strategy.
Automate tasks and processes.
Power BI platforms.

Course Objectives

This online Data Analytics certification course aims to help you master all the basic and advanced skills. The main goal is to train students to help organisations make better, data-driven decisions. It combines many disciplines, such as mathematics, computer science, software engineering, and statistics. In the world of information technology, data science jobs are presently in demand for several companies and industries. To accomplish a data science career, you should have a deep understanding and broader knowledge of machine learning and AI. It would be best to improve your ability to write in Python, SAS, R, and Scala programming languages. It would help if you also had experience working with big data platforms like Hadoop or Apache Spark.

In addition, data science requires experience in SQL database coding and the ability to work with unstructured data of various types, such as video, audio, pictures, and text. Data Science collects and manages large-scale structured and unstructured data for different academic and business corporations. Meanwhile, data analytics examines datasets to extract value and find answers to specific questions. Let’s explore data science vs. data analytics in more detail.

Focus on your skills, improve your knowledge, and get ready to explore the data science world.

First Phase
Second Phase
Third Phase
Fourth Phase
Fifth Phase
Sixth Phase

Discover from basic to advanced, do more projects, have practical experience, and unearth your potential.

Grow up in your professional career. Focus on your confidence, upgrade your resume, and get experience in real-time projects.

Open up your full capability, along with the tech skills, real-time projects, and other programmes that help you improve your vital soft skills.

Put your feet to the test and explore more internship opportunities to get actual world experience.

Self-confidence is the main factor. Together with the internship, prepare for interviews with our Preparation Resources.

Start your professional career and grow into the world of Data Analytics and Data Science.

Why choose Data Analytics and Science?

Boost your career with our trusted online Data Analytics Programme at EIIET. With our online Data Analytics courses, you will gain industrial knowledge, data transformation, an understanding of algorithms for complex business problems, data analytics project execution strategy, and data visualisation to make your dream job in a core company come true. Data analytics is analysing raw data to conclude that information. It helps a business optimise performance, perform efficiently, maximise profit, or make more strategically guided decisions.

Explore the world of Data Science with the online diploma programme launched by EIIET. Data Science is the implementation of tools, processes, and techniques such as programming, statistics, machine learning, and algorithms for combining, arranging, and investigating large datasets. The datasets are frequently a combination of structured and unstructured data.

Data science often aims to identify patterns and develop actionable insights. Still, it can also produce broad insights by asking questions, finding the right questions to ask, and identifying areas to study. It also helps to define objectives and interpret results based on business domain expertise, teaches how to manage and optimise the organization’s data infrastructure, and utilises relevant programming languages, statistical techniques, and software tools. Creates the curiosity to explore and spot trends and patterns in data. Prepare to communicate and collaborate effectively across an organisation.

Advantages of choosing this programme

Designed for working professionals and educators.
8+ Programming Tools and Languages.
Career Mentorship Sessions (1-on-1).
High-Performance Coaching (1-on-1).
Daily Doubt Resolution Support.
Career Essential Soft Skills Programme.
5+ industry-relevant projects.

Explore the world of Data Science in Germany with the online diploma programme launched by EIIET. Data Science is the implementation of tools, processes, and techniques such as programming, statistics, machine learning, and algorithms for combining, arranging, and investigating large datasets. The datasets are frequently a combination of structured and unstructured data.

The goal of data science is often to identify patterns and develop actionable insights, but it can also be to produce broad insights by asking questions, finding the right questions to ask, and identifying areas to study.

Course Syllabus

Python Programming

- Introduction to Python, Python Basics.
- Strings, Decisions control statements.
- Repetition statements and console input-Output.
- List, Tables, Set, and Dictionary.
- Functions and Recursion, Functional Programming, and Lambda Functions.

Advance Python Programming

- File Input-Output Modules.
- Classes and Objects.
- Exception Handling, Iterators and Generators.
- Data Analysis with Pandas.
- Numeric and Scientific computing using NumPy.
- Matplotlib and Seaborn.
- SciPy.
- Exploratory Data Analysis. - Real-Time Industry Project 01, 02.

SQL (Structured Query Language) for Data Science

- Introduction to SQL.
- Database Creation and Manipulation.
- Database Selection.
- Quering Multiple Tables.
- Data Exploration.
- Index, Views and transactions.
- Quering with Conditions.
- Stored Procedures.
- Integrating SQL with Excel.
- Integrating SQL with Python.
- Real-Time Industry Project 03, 04

Business Intelligence (BI) and Visualisation using Tableau

- Introduction to Tableau.
- Shelves, Cards, and Analytics Pane.
- Datatypes, Sorting, and Grouping.
- Filtering and table calculation.
- Complete Dashboard Building and Best Practices.
- Distributing and Publishing, Joins and Blending.
- Relationships (2020.2 and Later Version).
- Data Preparation, Parameters and Set Actions.
- Advance Calculations and Analytics.
- Nested LODs and Analytics Functions.
- Dynamic Design, Extensions and Tooltip Visualisation.
- Real-Time Industry Project 05, 06.

Data Analysis and Visualisation using MS Excel

- Course Introduction and Fundamentals.
- Basic Excel Functions and Modifications of Worksheets.
- Data Formatting, Working with Shapes and Images, and Creating Your First Chart.
- Excel Templates, Excel Options, Printing Your Excel Worksheets, and Introduction to Tables.
- Conditional Functions and Other Functions. Pivot Table and LookUp Functions.
- New Functions, Data Tab, and Introduction to Power Query.
- Advance Pivot Table Functions and Power Pivot Tools.
- Large Datasets, File Protection, Named Ranges, and More in the Data Tab.
- Lists Functions and Excel Automation using Macros.
- VBA Basics.
- Email Automation and Industry Application.

Introduction to Machine Learning Algorithms

- Introduction to Machine Learning.
- Set Theory.
- Probability.
- Statistics.
- Probability Distribution.
- Continuous Probability Distribution.
- Sampling and Estimation.
- Hypothesis Testing.
- Non-Parametric Test.
- Real-Time Industry Project 09.

Introduction to Machine Learning Algorithms & their Implementations by using Python Programming

- Introduction to Data Science and Programming Languages (Tools) for Data Science.
- Basics of Programming.
- Essential Python Libraries.
- Introduction to Machine Learning Cross-Validation and Bias Variance Trade-Off.
- Evaluation Matrics.
- Importing data and hands-on imported data.
- Univariate and Multivariate Linear Regression.
- Principal Component Analysis.
- Logistics Regression and k-nearest neighbour.
- Decision Tree and Random Forest.
- K-Mean and Hierarchical Clustering.
- Neural Networks.
- Real-Time Industry Project 10.

Machine Learning Fundamentals

- Basics of Probability and Statistics.
- Basics of Machine Learning and Artificial Intelligence.
- Supervised Learning - Prediction.
- Supervised Learning - Classification.
- Random Forest and Model Evaluation.
- Supervised Learning - Classification (SVM).
- Unsupervised Learning - K-Means.
- Unsupervised Learning - Hierarchical.
- Unsupervised Learning - PCA.
- Rea-lime Industry Project 11.

Advance Deep Learning using Python

- Artificial Neural Network.
- Activation Functions in Neural Networks.
- Deep Learning.
- Evaluation of Models.
- Optimizers.
- Convolutional Neural Networks (CNN) - Part 01.
- Convolutional Neural Networks (CNN) - Part 02.
- Recurrent Neural Network (RNN) - Part 01.
- Recurrent Neural Network (RNN) - Part 02.
- Basics of Neural Language Processing (NLP). - End to End ML Projects.

Effective Dashboards using Power Bl

- Introduction to Power Bl.
- Transform Data (Query Editor) - Part 01.
- Transform Data (Query Editor) - Part 02.
- Relational Data Model - Part 01.
- Relational Data Model - Part 02.
- Basics of DAX - Part 01.
- Basics of DAX - Part 02.
- Advance DAX Functions.
- Creating Interactive Reports - Part 01.
- Creating Interactive Reports - Part 02.
- Creating Interactive Reports - Part 03.
- Real-Time Industry Project 12.
- Real-Time Industry Project 13.

Statistics and Probability for Data Science

- Introduction to Machine Learning.
- Set Theory.
- Probability.
- Statistics.
- Probability Distribution.
- Continuous Probability Distribution.
- Inferential Statistics.
- Hypothesis Testing.
- Non Parametric Tests.