Data Science and Data Analysis with Python

Python Data Science and Data Analysis

Data Science 4.6(49)3h 3m16 lectures2 sections

What you'll learn

Understand the basics of data analysis
Set up your Python working environment
Learn the basics of the Python language
Work with the Numpy library
Process data with the Pandas library
Load, explore and filter data
Handle missing and duplicate data
Create charts with Matplotlib
Build a complete data analysis project

About this course

This course teaches data science and data analysis with Python from the ground up. Data science is the field of collecting, cleaning and analyzing data and turning it into useful information for better decisions. The course starts with an introduction to data analysis, then sets up your working environment and covers Python basics. Next you work with the Numpy library across two parts, then move on to Pandas for data processing: loading data, exploring it, filtering it and grouping it, handling missing and duplicate values, and adding and updating data. After that you learn Matplotlib and how to work with charts and visual data. The course closes with a summary of everything covered and a complete hands-on project. It suits anyone who wants to enter the data analysis field, whether you are a beginner or simply interested in programming, and it needs no previous experience because it starts with Python basics. By the end, you will be able to load, clean, analyze and plot data, and carry out a data analysis project on your own.

Expected outcomes

Master the fundamentals of data science
Handle and analyze various types of data
Create visualizations to illustrate and analyze data
Implement data analysis projects using Python

Learner reviews

These reviews are from learners who bought the content and completed at least 50% of it — there are no reviews from anyone who hasn't gone through the material.

4.6

★★★★★

49 reviews

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★★★★★سامر ن.·منذ 3 شهرشاهد 51٪
كرس اكتر من رائع , شامل اهم برامج تحليل البيانات في سوق العمل , بطريقه مبسطه و سهله.
★★★★★فؤاد ع.·منذ 3 شهرشاهد 80٪
c'est partfait , une bonne formation
★★★★★محمد ف.·منذ 4 شهرشاهد 100٪
amazing course...

تم عرض كل التقييمات (3)

Course content

Python Data Science

15 lectures

Introduction to Data AnalysisIntroduces data analysis and why to learn Python for it, compares it with Excel and R, then shows how to download Python and the PyCharm editor.6:02
Setting Up the Work EnvironmentExplains variables and data types in Python, especially strings, including spacing, quotation handling, and the functions that modify text.18:28
Basics of Python LanguageExplains Python lists, how to store different data types in them, access elements by index and slice them, and add or remove items.12:43
Working with Numpy Library (Part One)Introduces the NumPy library, how to import it, create one-dimensional arrays, and perform arithmetic operations on them.15:30
Working with Numpy Library (Part Two)Continues NumPy with two-dimensional arrays: selecting rows and columns, changing values, generating numbers over a range, and reshaping arrays.18:26
Data Processing Using Pandas LibraryIntroduces the Pandas library and exploratory data analysis, and shows how to create a Series and build a DataFrame from dictionaries and lists.23:29
Loading and Exploring DataShows how to upload a data file to Anaconda, open it in Jupyter Notebook, read it with Pandas, view its first and last rows, and check its rows and columns.7:24
Data FilteringExplains sorting data in ascending and descending order and filtering it by conditions using query in Pandas inside Jupyter Notebook.12:24
Data CollectionExplains grouping data with Group By in Pandas and computing the mean, maximum, minimum and sum for each category, by one or several columns.6:50
Handling Missing and Duplicate DataExplains detecting and removing missing and duplicate data, and replacing missing values with the mean, maximum or minimum in Pandas.9:39
Explaining Matplotlib and Working with GraphicsExplains turning data into visualizations with Matplotlib and seaborn, such as the histogram, box plot and bar plot, and how to read each one.11:41
Explaining Visual Data and GraphicsContinues data visualization with other plot types such as the count plot, comparing categories and averaging by weekday, and explains how to read a box plot.14:14
Adding and Updating DataExplains adding a new column to a DataFrame and updating existing values, such as increasing everyone's age or salary by a fixed amount.5:32
Summary of Everything Studied Before Starting the ProjectReviews through summary slides everything covered so far, including Anaconda, NumPy, Pandas, Matplotlib and Seaborn, as a cheat sheet before starting the project.7:45
Practical Application of What Has Been Studied (Creating a Complete Project)Applies what was learned in a housing data analysis project: dropping unneeded columns, examining how price relates to area and room count, and drawing a heat map.13:45