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Data Science: Python Programming for Data Science 2022 Complete Bootcamp

Learn and build your Python Programming and Data Science Skills from the ground up with the support of a tutor


SDE Arts | Octavo

Summary

Price
Free
Study method
Online, On Demand What's this?
Duration
6.3 hours
Qualification
No formal qualification
Optional extras

Digital certificate included in price

Additional info
  • Tutor is available to students

1,070 students purchased this course

Add to basket or enquire

Overview

Hello and welcome to the Data Science: Python Programming for Data Science 2022 Complete Bootcamp.

In this course, you will learn how to code in Python from the beginning, then you will master how to work with the most famous libraries and tools of the Python programming language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that the necessary decisions are taken by companies.

What you'll learn

  • Code with Python Programming Language

  • Python Functional Programming

  • Structure Data using collection containers

  • Object-Oriented Design

  • Advanced Python Foundations

  • Handling Data with Python Libraries

  • Numerical Python

  • Extracting and Analyzing data from different resources

  • Data Analysis with Pandas

  • Data Visualization using matplotlib

  • Advanced Visualization with Seaborn

  • Build Python solutions for data science

  • Get Instructor QA Support and help

# Reviews about this course

★★★★★ "This course is very helpful for basic to advance level of learning in python.

I also like this course."

★★★★★ "This was simple and easy to follow and an eye-opener for a beginner to get started with Data analysis and visualization."

★★★★★ "Very good course

The instructor spoke very clear and was very knowledgeable"

★★★★★ "Enrolled this course to sharpen my knowledge in python programming language. Very complete course to begin mastering python."

★★★★★ "easy to understand. well organized course"

★★★★★ "Clear, and easy explanation. Great course for beginners who want to learn python and data science."

★★★★★ "This course is very helpful for me I would like to start research on fault diagnosis"

★★★★★ "Nice course with great practical examples."

★★★★★ "It's totally amazing !! The explanation about the topics is clear and very understandable"

Curriculum

12
sections
80
lectures
6h 19m
total
    • 1: Welcome 01:40
    • 2: Download and Install the tools 01:21
    • 3: Jupyter Overview + Markdown in Jupyter tutorial 06:47
    • 4: Using Jupyter Notebook to code with Python 05:41
    • 5: Using Anaconda Prompt 03:15
    • 6: Quiz 1 01:00
    • 7: Variables and Types Tutorial 10:38
    • 8: Describe what's inside the code 04:44
    • 9: Define Blocks and Avoid IndentationError 04:19
    • 10: String full tutorial 13:44
    • 11: Numbers, Math and f-string tutorial 15:13
    • 12: Handling inputs and outputs 04:29
    • 13: Quiz 2 02:00
    • 14: Structure Data using Lists 21:02
    • 15: Structure data using Tuples 10:32
    • 16: Structure Data using Dictionaries 08:05
    • 17: Structure Data using Sets 07:21
    • 18: Quiz 3 02:00
    • 19: Comparing Values 08:36
    • 20: Output from Logics 06:49
    • 21: Conditional Statements 08:55
    • 22: The while loop 02:56
    • 23: The for loop in Python 07:18
    • 24: Python Library Functions 11:21
    • 25: The Lambda Power 08:51
    • 26: User-Defined Functions 07:30
    • 27: Break statement 04:05
    • 28: Continue statement 04:35
    • 29: For else statement 02:15
    • 30: App to Put all together 01:43
    • 31: Quiz 4 01:00
    • 32: Core Python OOP 12:02
    • 33: Exploring Inheritance 10:10
    • 34: Quiz 5 01:00
    • 35: Comprehensions 05:52
    • 36: Constructed modules and random 06:37
    • 37: Doing mathematics 04:47
    • 38: Doing statistics 04:17
    • 39: Errors Exploration 04:28
    • 40: Exceptions Playground 06:45
    • 41: IO data in memory 07:11
    • 42: Interacting with operating system data 03:04
    • 43: Moving data files between directories 04:42
    • 44: Data will be in the trash bin 04:16
    • 45: Zipping and Unzipping Data 06:49
    • 46: NumPy Level 1 07:09
    • 47: NumPy Level 2 05:03
    • 48: NumPy Level 3 02:25
    • 49: NumPy Level 4 03:44
    • 50: NumPy Level 5 05:38
    • 51: NumPy level 6 03:50
    • 52: NumPy Level 7 03:46
    • 53: NumPy Level 8 03:19
    • 54: NumPy level 9 03:08
    • 55: Pandas data analysis level 1 03:49
    • 56: Pandas data analysis level 2 04:41
    • 57: Pandas data analysis level 3 02:18
    • 58: Pandas data analysis level 4 03:32
    • 59: Pandas data analysis level 5 02:52
    • 60: Pandas data analysis level 6 04:30
    • 61: Matplotlib data visualization level 1 02:57
    • 62: Matplotlib data visualization level 2 01:31
    • 63: Matplotlib data visualization level 3 03:06
    • 64: Matplotlib data visualization level 4 03:14
    • 65: Matplotlib data visualization level 5 01:37
    • 66: Matplotlib data visualization level 6 03:02
    • 67: Matplotlib data visualization level 7 02:32
    • 68: Seaborn statistical graphs level 1 03:41
    • 69: Seaborn statistical graphs level 2 01:56
    • 70: Seaborn statistical graphs level 3 01:17
    • 71: Seaborn statistical graphs level 4 01:39
    • 72: Seaborn statistical graphs level 5 02:31
    • 73: Seaborn statistical graphs level 6 02:30
    • 74: Seaborn statistical graphs level 7 01:49
    • 75: Seaborn statistical graphs level 8 01:00
    • 76: Python Programming 00:40
    • 77: NumPy 00:29
    • 78: Pandas 00:25
    • 79: Matplotlib 00:35
    • 80: Seaborn 00:28

Course media

Description

Data science enables companies to measure, track, and record performance metrics for facilitating and enhancing decision making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability.

And the employment of data science and its tools depends on the purpose you want from them.

For example, using data science in health care is very different from using data science in finance and accounting, and so on. And I’ll show you the core libraries for data handling, analysis and visualization which you can use in different areas.

One of the most powerful programming languages ​​that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data.

By the end of this course, you'll learn:

  • Python tools for Data Analysis

  • Python Basics

  • Python Fundamentals

  • Python Object-Oriented

  • Advanced Python Foundations

  • Data Handling with Python

  • Numerical Python(NumPy)

  • Data Analysis with Pandas

  • Data Visualization with Matplotlib

  • Advanced Graphs with Seaborn

  • Instructor QA Support and Help

HD Video Training + Working Files + Resources + QA Support.

In this course, you will learn how to code in Python from the beginning and then you will master how to deal with the most famous libraries and tools of the Python language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that, the necessary decisions are taken by companies.

I am Ahmed Ibrahim, a software engineer and Instructor and I have taught more than 200,000 engineers and developers around the world in topics related to programming languages ​​and their applications, and in this course, we will dive deeply into the core Python fundamentals, Advanced Foundations, Data handling libraries, Numerical Python, Pandas, Matplotlib and finally Seaborn.

I hope that you will join us in this course to master the Python language for data analysis and Visualization like professionals in this field.

We have a lot to cover in this course.

Let’s get started!

Who is this course for?

  • Python beginners and newbies
  • Data Scientist who knows other language tools
  • New Python Data Analysts
  • Data Science Beginners
  • New developers and Programmers
  • Programmers and developers who know other programming language but are new to python
  • Anyone who wants to use Python for data analysis and visualization in a short time!

Requirements

  • No Python prior experience is required to take this Training
  • Computer and Internet access

Career path

  • Python Data Scientist salaries currently range between $104,000 to $156,000
  • Python Developer Avg. salary: $107,003 / Annual
  • Software Engineer Avg. salary: $111,206 / Annual
  • Software Developer Avg. salary: $100,000 / Annual
  • Python Data Analyst Avg. salary: $115,156 per year

Questions and answers


No questions or answers found containing ''.


Juliet Ogbaji O. asked:

Can an Android phone be used in place of a Conputer fur this course?

Answer:

To follow this course without any obstacles you need a computer or laptop with any operating system, and the jupyter notebook installed. Best regards Ahmed Ibrahim

This was helpful. Thank you for your feedback.
Sunny asked:

How useful will this course be fore me as an Accountant and Internal Auditor.

Answer:

Hi Sunny, In this course you'll learn how to use Python for Data handling, Analytics and Visualization in a more general purpose programming environment by using Jupyter Notebook for Python rather than in Excel or any other spreadsheet app. Actually, Many companies are now transitioning from Excel and other spreadsheet apps to Python, because Python is considered a more efficient data analysis tool for complex calculations and large volumes of data. Best wishes, Ahmed

This was helpful. Thank you for your feedback.
Bharti. Does this course cover my req. below. asked:

My requirements do not fit in this box. Can I send them by email to confirm if this course fits what I need to learn. I am a Physics student.

Answer:

Certainly this training course is for you because it does not require any prior experience. Everything will be clear and understandable to you. And if you have any questions, do not hesitate to ask questions and I will answer you as soon as possible. Best wishes, Ahmed

This was helpful. Thank you for your feedback.

Optional extras

Digital certificate

Reed courses certificate of completion is included in course price

Additional information:

Will be downloadable when all lectures have been completed

Reviews

4.7
Course rating
94%
Service
94%
Content
98%
Value

FAQs

What does study method mean?

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

What are CPD hours/points?

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

What is a ‘regulated qualification’?

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

What is an ‘endorsed’ course?

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body’s logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.