Learn Python for Data Analytics
This course includes:
- 2.5 hours of on-demand video
- 10 articles
- 4 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
4.2
(92 ratings)
Created by Onur Baltacı
What you’ll learn
- Learn how to analyze data
- Learn how to build a Data Analytics Project
- Learn how to visualize data
- Repeat the basics of statistics and python
Course content
12 sections • 43 lectures • 2h 18m total lengthCollapse all sections
Before starting the course1 lecture • 1min
- Before Starting00:20
Statistics fundamentals (Optional)10 lectures • 26min
- About this section of the course00:16
- General ConceptsPreview01:43
- Descriptive statistics introduction & Frequency02:46
- Mean – Mode – Median02:47
- Probability Introduction02:29
- Probability Distributions03:07
- Conditional Probability & Tree Diagram05:05
- Inferential Statistics introduction03:28
- Confidence Interval03:07
- Simple Linear Regression01:22
- Statistics Quiz3 questions
Python environment will be used in the course1 lecture • 1min
- Google Colab & Jupyter Notebook00:07
Python fundamentals9 lectures • 49min
- Print & Comments02:20
- Variables part 106:04
- Variables part 203:58
- Data Types part 1Preview09:05
- Data Types part 205:06
- Operators08:40
- If Statements05:05
- Loops04:27
- Functions04:03
- Python Quiz4 questions
Pandas7 lectures • 25min
- Pandas part 107:19
- Pandas part 208:25
- Data set for pandas coding 1 & 200:03
- Pandas Coding 101:49
- Pandas Coding 201:38
- Data set for pandas for time series analysis00:03
- Pandas for time series analysis05:13
- Pandas Quiz5 questions
Numpy3 lectures • 16min
- Numpy – Introduction to Arrays05:28
- Array Indexing03:41
- Array Slicing and Array Iterating07:03
Matplotlib2 lectures • 3min
- Matplotlib Introduction00:52
- Matplotlib Coding01:38
- Matplotlib Quiz3 questions
Seaborn3 lectures • 3min
- Visualization of distributions01:19
- Visualization of statistical relationships00:34
- Plotting Categorical Data01:00
- Seaborn Quiz2 questions
Project 12 lectures • 10min
- You can download the data set from here00:03
- Project 109:27
Project 22 lectures • 6min
- You can download the data set from here00:03
- Project 206:12
How to build a project by yourself and share it2 lectures • 1min
- Where to find data00:26
- Where to share the projects00:28
Bonus Section1 lecture • 1min
- bonus lecture00:11
Requirements
- No programming experience is needed. We will repeat statistics and python fundamentals together.
Description
This is a data analysis course in 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, and 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)
-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 become 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 processes and finishing it I would love to answer your questions about data analysis, data science, and other concepts. Feel free to contact me via the course Q&A Section.
Who this course is for:
- This course is for people who are interested in data-related roles, especially data analytics.
