Learn to use NumPy, Pandas, Seaborn , Matplotlib for Data Manipulation and Exploration with Python What you'll 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. Requirements It is advantageous to have basic python knowledge, but it is not required to understand the material in this course. However, people with no previous basic knowledge of python need to focus first on module 2, 3, 4, and 5, that would be enough to comprehend the rest material in this course. 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! Who this course is for I designed this course to be valuable for people who are interested in data science and data analysis with python. If you want to learn data science with python, this course will be a valuable starting point. This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data.