Data Analysis with Python Training in Copenhagen, Denmark

This hands-on course introduces participants to data analysis using Python, covering data manipulation, visualization, exploratory data analysis, and working with real datasets. Participants will learn core libraries such as Pandas, NumPy, Matplotlib, and Seaborn, and apply them to draw insights and prepare data for further modeling.

PYTHON FUNDAMENTALS AND DATA HANDLING

Introduction to Python for Data Analysis

  Setting up the environment: Jupyter, VS Code, Anaconda

  Python syntax, variables, functions, lists, loops

  Working with Python packages and virtual environments

Introduction to NumPy and Pandas

  NumPy arrays, operations, and use in analysis

  Pandas Series and DataFrames: structure and operations

  Reading data from CSV, Excel, and JSON files

DATA CLEANING AND EXPLORATION

Data Cleaning and Preparation

  Handling missing values and duplicates

  Data type conversion and column manipulation

  Filtering, sorting, and deriving new columns

Exploratory Data Analysis (EDA)

  Descriptive statistics: mean, median, std, percentiles

  Grouping, aggregating, and pivoting data

  Detecting outliers and trends

DATA VISUALIZATION AND INSIGHTS

Data Visualization with Matplotlib and Seaborn

  Line plots, bar charts, histograms, boxplots

  Plotting relationships and distributions

  Styling and customizing plots for reports

Wrap-Up and Next Steps

  Overview of where to go next: ML, dashboards, or SQL

  Best practices for self-learning and project development

  Resources and certification tips