Data Analyst Course in Germany

Overview of Python: history, features, and use cases.

Installing Python and setting up the development environment.

Basic syntax: variables, data types, and simple operations.

Hands-on: Writing a simple Python program to understand basic concepts.

Introduction to sequences: lists, tuples, sets, and dictionaries.

File handling: reading and writing files in Python.

Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events.

Hands-on: A program simulating coin tosses or dice rolls to explore simple probability.

Defining and using functions: arguments, return values, and scope.

Object-oriented concepts: classes, objects, inheritance, and polymorphism.

Hands-on: Writing classes to represent basic statistical concepts (e.g., creating a class for a random variable or distributions).

Importing and using Python modules.

Exception handling: try, except, and finally blocks.

Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance).

Introduction to NumPy and its applications.

Creating and manipulating arrays.

Mathematical Concepts: Introduction to descriptive statistics—mean, median, mode, variance, and standard deviation using NumPy.

Probability distributions (e.g., normal distribution) using NumPy’s random module.

Hands-on: Use NumPy to calculate statistical measures from data arrays, simulate random data based on distributions.

Basic Pandas operations: importing/exporting data, Series, and DataFrames.

Cleaning and transforming data.

Mathematical Concepts: Exploring data distributions (frequency distribution, data dispersion).

Hands-on: Using Pandas to compute statistics (mean, standard deviation) from real datasets.

Merging, joining, and reshaping data.

GroupBy, pivot tables, and handling time-series data.

Mathematical Concepts: Correlation and covariance between datasets.

Hands-on: Use Pandas to explore relationships between datasets using correlation and apply statistics to grouped data.

Basic visualization: line plots, bar charts, and scatter plots.

Mathematical Concepts: Visualizing statistical distributions (histograms, box plots) and relationships between variables (scatter plots).

Hands-on: Plotting histograms, scatter plots, and box plots to visualize data distributions and compare variables.

Creating subplots, multiple axes, and advanced customizations in Matplotlib.

Statistical visualizations in Seaborn (e.g., pair plots, heatmaps).

Mathematical Concepts: Visualizing probability distributions, regression analysis, and statistical relationships between variables (pair plots, correlation heatmaps).

Hands-on: Plotting probability density functions and visualizing relationships between multiple variables using Seaborn.

Introduction to web scraping: HTML parsing and scraping static pages with BeautifulSoup.

Using Selenium for scraping dynamic websites.

Hands-on: Scraping data from websites and performing simple data analysis/statistical insights from the scraped data.

What is SQL? Overview and Importance

Relational Database Concepts (Tables, Rows, Columns)

Common Database Management Systems (MySQL, PostgreSQL, Oracle, SQL Server)

SQL Syntax Rules

Connecting to a Database (Using tools like MySQL Workbench or pgAdmin)

The SELECT Statement

Selecting Specific Columns

Using DISTINCT to Remove Duplicates

Simple WHERE Clauses for Filtering

Using ORDER BY for Sorting Data

Basic LIMIT clause (for databases like MySQL/PostgreSQL)

Using Comparison Operators (=, !=, >, <, >=, <=)

Logical Operators (AND, OR, NOT)

Filtering with BETWEEN, IN, and LIKE

Handling NULL values (IS NULL, IS NOT NULL)

Sorting Data with ORDER BY (Ascending & Descending)

Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)

Grouping Data with GROUP BY

Filtering Groups with HAVING

Using GROUP BY with Multiple Columns

Understanding the Difference between WHERE and HAVING

Introduction to Joins and Data Relationships

INNER JOIN for Matching Data

LEFT JOIN for Including Non-matching Records

RIGHT JOIN and FULL OUTER JOIN (if supported by the database)

Using Aliases for Table Names

Joining Multiple Tables

Best Practices for Writing Joins

Overview of Power BI components (Desktop, Service, Mobile).

Copilot Introduction: How to use AI-powered features for data exploration.

Installing Power BI Desktop and setting up the environment.

Navigating the Power BI interface: ribbons, panes, and views.

Hands-on Lab: Importing sample datasets and exploring Copilot.

Connecting to various data sources (Excel, SQL, Web APIs).

Data profiling, data types, and handling missing values.

Advanced transformations: merging, appending, pivoting, unpivoting data.

Using Power Query Editor for complex data shaping.

Hands-on Lab: Transforming raw data into a clean dataset.

Designing star and snowflake schemas.

Creating and managing table relationships.

DAX basics: calculated columns, measures, and quick measures.

Using DAX for basic calculations like SUM, AVERAGE, COUNT.

Hands-on Lab: Building a data model for sales data analysis.

Advanced DAX functions: CALCULATE, FILTER, ALL, RELATED.

Time intelligence functions for date-based calculations (e.g., YTD, QTD).

Performance optimization techniques: DAX query tuning, minimizing model size.

Hands-on Lab: Creating advanced DAX measures for business insights.

Building advanced visuals: combination charts, gauges, maps, and custom visuals.

Creating dynamic reports with slicers, bookmarks, and drill-through.

Designing for user experience: layout, color schemes, and storytelling.

Best practices for dashboard design and performance.

Hands-on Lab: Creating an interactive sales performance dashboard.

Publishing reports and dashboards to Power BI Service.

Configuring dataset refresh schedules and managing gateways.

Setting up Row-Level Security (RLS) for data access control.

Hands-on Lab: Publishing and sharing reports with user access control.

Using Copilot to generate natural language insights and queries.

Automating data exploration and report generation.

Implementing Copilot for predictive analytics and recommendations.

Hands-on Lab: Using Copilot for advanced data exploration.

Utilizing AI visuals like Key Influencers, Decomposition Tree, and Smart Narrative.

Conducting clustering, anomaly detection, and forecasting.

Interactive Q&A with natural language queries.

Hands-on Lab: Applying AI visuals to real-world business data.

Overview of Microsoft Fabric: Synapse, Data Factory, Data Lake, and Power BI.

Using OneLake for centralized data storage.

Building Synapse Dataflows for real-time data ingestion.

Creating data pipelines for ETL processes and integrating with Power BI.

Hands-on Lab: Setting up a Fabric workspace, creating dataflows, and integrating with Power BI.

Overview of Tableau Prep for data cleaning and transformation.

Importing, filtering, and shaping data for analysis.

Data profiling and preparing datasets for analysis.

Connecting to various data sources (spreadsheets, databases, cloud).

Understanding live connections vs extracts.

Managing data joins, unions, and blends.

Building foundational visualizations like bar charts, line charts, and scatter plots.

Sorting, filtering, and grouping data.

Working with visual marks (size, color, labels) for enhanced data presentation.

Creating row-level and aggregate calculations.

Using string functions, logical functions, and conditional calculations.

Practical applications of calculations for deriving insights.

Incorporating reference lines, trend lines, and forecasts.

Using parameters to create dynamic visualizations.

Highlight actions, sets, and advanced tooltips for interactivity.

Understanding Fixed, Include, and Exclude LOD calculations.

Practical scenarios for using LOD expressions in reporting.

Combining LOD expressions with other calculations.

Creating dual-axis charts, waterfall charts, and bullet graphs.

Understanding when and how to use advanced charts for storytelling.

Hands-on practice with custom chart creation.

Building interactive dashboards from multiple sheets.

Using dashboard actions to filter and highlight data dynamically.

Creating stories to present data insights in a narrative format.