Python for Data Analytics Beginner to Advanced

Python for Data Analytics – Beginner to Advanced, available at $69.99, has an average rating of 3.54, with 45 lectures, 3 quizzes, based on 286 reviews, and has 22195 subscribers.

You will learn about 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 This course is ideal for individuals who are People who is interested in data related roles, especially data analytics. or People who wants to learn data analysis or People who wants to become a data analyst or People who wants to become a data scientist It is particularly useful for People who is interested in data related roles, especially data analytics. or People who wants to learn data analysis or People who wants to become a data analyst or People who wants to become a data scientist.

Enroll now: Python for Data Analytics – Beginner to Advanced

Summary

Title: Python for Data Analytics – Beginner to Advanced

Price: $69.99

Average Rating: 3.54

Number of Lectures: 45

Number of Quizzes: 3

Number of Published Lectures: 38

Number of Published Quizzes: 2

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 40

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

People who wants to become a data scientist

Target Audiences

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

People who wants to become a data scientist

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 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)

-Data Analysis Projects

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 .

Course Curriculum

Chapter 1: Helpful statistics concepts (optional)

Lecture 1: General concepts in statistics

Lecture 2: Mean-Mode-Median

Lecture 3: Mean-Mode-Median Calculation Exercise

Lecture 4: Probability Introduction

Lecture 5: Inferential Statistics Introduction

Lecture 6: Standard Deviation – Variance Calculation Exercise

Lecture 7: Confidence Interval

Lecture 8: Confidence Interval Practice

Chapter 2: Introduction to Coding

Lecture 1: Installing Python and Code Editor

Lecture 2: Python Files

Chapter 3: Pandas for Data Analysis

Lecture 1: Pandas Part 1: Introduction – Series – Dataframes – Missing Data Handling –

Lecture 2: Pandas Part 2: Data Manipulation – Sorting and Ranking – Merge – Data Cleaning

Lecture 3: Pandas Part 3: Group by – Aggregating Data – Data Visualization – Multi Indexes

Chapter 4: Time Series Analysis

Lecture 1: Data set for pandas for time series analysis

Lecture 2: Pandas for time series analysis

Lecture 3: Seasonality

Lecture 4: Dickey-Fuller test for stationarity

Lecture 5: Autocorrelation

Lecture 6: Decomposition

Chapter 5: Numpy

Lecture 1: Numpy – Introduction to Arrays

Lecture 2: Array Indexing

Lecture 3: Array Slicing and Array Iterating

Chapter 6: Matplotlib

Lecture 1: Matplotlib Introduction

Lecture 2: Matplotlib Coding

Chapter 7: Seaborn

Lecture 1: Visualization of distributions

Lecture 2: Visualization of statistical relationships

Lecture 3: Plotting Categorical Data

Chapter 8: Project 1

Lecture 1: Project Data

Lecture 2: Project 1

Chapter 9: Project 2

Lecture 1: Project Data

Lecture 2: Project 2

Chapter 10: Project 3

Lecture 1: Project Data

Lecture 2: Project 3

Chapter 11: Project 4

Lecture 1: Project Data

Lecture 2: Project 4

Chapter 12: How to build a project by yourself and share it

Lecture 1: Where to find data

Lecture 2: Where to share the projects

Chapter 13: Bonus Section

Lecture 1: bonus lecture

Instructors

Onur Baltac?
Data Scientist

Rating Distribution

1 stars: 18 votes

2 stars: 17 votes

3 stars: 61 votes

4 stars: 99 votes

5 stars: 91 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!