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.
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.
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
- Strings, Decisions control statements.
- Repetition statements and console input-Output.
- List, Tables, Set, and Dictionary.
- Functions and Recursion, Functional Programming, and Lambda Functions.
- 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.
- 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
- 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.
- 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.
- Set Theory.
- Probability.
- Statistics.
- Probability Distribution.
- Continuous Probability Distribution.
- Sampling and Estimation.
- Hypothesis Testing.
- Non-Parametric Test.
- Real-Time Industry Project 09.
- 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.
- 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.
- 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.
- 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.
- Set Theory.
- Probability.
- Statistics.
- Probability Distribution.
- Continuous Probability Distribution.
- Inferential Statistics.
- Hypothesis Testing.
- Non Parametric Tests.