This course is intended to be an initiation to learn #BigData and #MachineLearning with #Python programming for absolute beginners that have no background in programming.
In this course, we will step by step, using the example of real data, we will go through the main processes related to the topic "Big data and machine learning".
Since the material turned out to be voluminous, I divided the course into five parts.
In this fourth part:
⇉ we will look at the main platforms for visualizing Big Data and consider the main Data Visualization Online-Tools for Big Data. We will briefly look at these platforms and generate several reports in each of the platforms.
This will give you the opportunity to choose the right platform that suits you and your data.
⇉ In practical lesson we exported an excel file with our data to the Kaggle platform and using a Jupyter Notebook we cleared the data and visualized the data using different python libraries.
⚐ You will be guided through the basics of using:
Jupyter Notebook
PowerBI
Tableu
Google DataStudio.
⚐ Topics covered in this course:
Lecture 2. Data Visualization Tools. Power BI, Tableau, Google Data Studio, Jupyter.
What is Business Intelligence?
Data Visualization Tools
Was ist Business Intelligence? Was ist BI?
Jupyter Notebooks as a Custom Calculation Engine
Machine Learning Visualizations made in Python
Lecture 3. Practice. Python Data Visualizations. Prepare Data for Visualizations. Kaggle, Jupyter Notebook (Part 1/3).
Export data from Excel to Python
Uploading data to Visualizations on Kaggle
Introduction to Jupyter Notebooks
Prepare data for Visualisations
Lecture 4. Practice. Python Data Visualizations. Clean data for Visualizations (Part 2/3).
Clean data for Visualizations
Use Pandas in Jupyter Notebook
Data Cleaning With Pandas
Lecture 5. Practice. Python Data Visualizations. Data Visualizations in Jupyter Notebooks (Part 3/3).
Visualization with Seaborn and Matplotlib
Data visualization by Heatmaps and Scatter plots
Python Treemaps with Squarify
Three-Dimensional Plotting in Matplotlib
Lecture 6. Practice. Power BI. Introduction and getting started.
Pros and Cons of Power BI
Import an Excel file into Power BI
How to Get Started
Treemaps in Power BI
Creating Reports in Power BI
Lecture 7. Practice. Tableau. Introduction and getting started.
Pros and Cons of Tableau
Import an Excel file into Tableu
How to Get Started in Tableu
Treemaps in Tableu
Creating Reports in Tableu
Creating Dashboards in Tableu
Lecture 8. Practice. Google Data Studio. Introduction and getting started.
Pros and Cons of Google Data Studio
Import an Excel file into Google Data Studio
How to Get Started in Google Data Studio
Treemaps in Google Data Studio
Creating Reports in Google Data Studio
Creating Dashboards in Google Data Studio
The course is best-suited for learners who are interested in Big Data and Machine Learning (using Python) or for learners who already have Python programming skills but want to practice with a hands-on, real-world data project can also benefit from this course.