With Exercises : Learn to use NumPy, Pandas, Seaborn , Matplotlib for Data Manipulation and Exploration with Python
What you will learn
Use Python for Data Science and Machine Learning
Learn to use Pandas for Data Analysis
Learn to use NumPy for Numerical Data
Learn to use Seaborn for statistical plots
Learn to use Matplotlib for Python Plotting
You will learn how to use Jupyter Notebook for exploratory computations using python.
You will learn basic and advanced features in NumPy (Numerical Python)
You will learn various data analysis tools in Pandas library.
You will learn the essential tools for load, clean, transform, merge, and reshape data.
You will learn how to create informative visualizations with matplotlib, seaborn and Pandas
You will learn how to analyze and manipulate time series data.
You will learn how to handle real world data analysis, including data preparation and exploration.
Description
This course is ideal for you, if you wish is to start your path to becoming a Data Scientist!
Data Scientist is one of the hottest jobs recently the United States and in Europe and it is a rewarding career with a high average salary.
The massive amount of data has revolutionized companies and those who have used these big data has an edge in competition. These companies need data scientist who are proficient at handling, managing, analyzing, and understanding trends in data.
This course is designed for both beginners with some programming experience or experienced developers looking to extend their knowledge in Data Science!
I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single skill in data manipulation using Python libraries for data science.
In this comprehensive course, I will guide you to learn how to use the power of Python to manipulate, explore, and analyze data, and to create beautiful visualizations.
My course is equivalent to Data Science bootcamps that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With over 90 HD video lectures, including all examples presented in this course which are provided in detailed code notebooks for every lecture. This course is one of the most comprehensive course for using Python for data science on Udemy!
I will teach you how to use Python to manipulate and to explore raw datasets, how to use python libraries for data science such as Pandas, NumPy, Matplotlib, and Seaborn, how to use the most common data structures for data science in python, how to create amazing data visualizations, and most importantly how to prepare your datasets for advanced data analysis and machine learning models.
Here a few of the topics that you will be learning in this comprehensive course:
- How to Set Your Python Environment
- How to Work with Jupyter Notebooks
- Learning Data Structures and Sequences for Data Science In Python
- How to Create Functions in Python
- Mastering NumPy Arrays
- Mastering Pandas Dataframe and Series
- Learning Data Cleaning and Preprocessing
- Mastering Data Wrangling
- Learning Hierarchical Indexing
- Learning Combining and Merging Datasets
- Learning Reshaping and Pivoting DataFrames
- Mastering Data Visualizations with Matplotlib, Pandas and Seaborn
- Manipulating Time Series
- Practicing with Real World Data Analysis Example
Enroll in the course and start your path to becoming a data scientist today!
Content
Introduction
Module 2: Setting Python Environment
Module 3: Working with Jupyter Notebooks
Module 4: Data Structures And Sequences In Python
Module 5: Functions in Python
Module 6: NumPy Arrays
Module 7: Pandas Dataframe
Module 8: Data Loading, Storage with Pandas
Module 9: Data Cleaning and Preprocessing
Module 10: Data Wrangling1: Hierarchical Indexing
Module 11: Data Wrangling2: Combining and Merging Datasets
Module 12: Data Wrangling3: Reshaping and Pivoting
Module 13: Data Visualization with Matplotlib and Seaborn
Module 14 : Time Series
Module 15: Real World Data Analysis Example
