Overview
Learn how to analyze data, Learn how to do a data analysis project, Learn how to visualize data, Learn (or repeat) the basics of statistics and python, Learn the analysis of time series data
People who is interested in data related roles, especially data analytics., People who wants to learn data analysis, People who wants to become a data analyst
No requirements. This course includes Python & Statistics fundamentals.
This is a data analysis course which we use Python and its libraries in order to clean, analyze and visualize our data. This course is for anyone who is interested in data analytics. You don't need to have any knowledge about python or statistics since we will be repeating these two at the beginning of the course. We will cover python libraries which is designed for data manipulation, data analysis, data visualization. Topics we are going to be covering:
-Fundamentals of Python
-Fundamentals of Statistics
-Pandas ( a Python Library designed for data cleaning, data analysis and data manipulation)
-Time Series Analysis
-Matplotlib (a Python Library designed for data visualization)
-Seaborn (a Python Library designed for data visualization)
will be covered in the course. After this course, you can create and share data analysis projects, start learning about machine learning in order to becoming a data scientist or you can learn a business intelligence tool like Microsoft Power BI or Tableau in order to start your career in business analytics. General concepts and codes and their returns will be covered in this course. In all course process and finishing it i would love to answering your questions about data analysis, data science and other concepts. Feel free to contact to me via courses Q&A Section .
Onur Baltacı
I have BSc in Economics . I am teaching Data Science on Udemy for more than a year and I try to simplify concepts in order to create efficient courses. More than 35,000 students from +160 countries are enrolled into my courses and I have +4 rating overall. I create courses about statistics, data science, economics and programming languages.