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Data Analysis for Beginners: Python & Statistics

6h 34m 20s
English
Paid

Data Analysis for Beginners: Python & Statistics is a 56-lesson 6 hours 34 minutes self-paced course by Zero To Mastery. Embark on your journey into the world of data analysis with this comprehensive course on Python and statistics.

Course facts

Lessons
56
Duration
6 hours 34 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

Embark on your journey into the world of data analysis with this comprehensive course on Python and statistics. Designed for beginners, this course offers clear and practical lessons without the need for complex terms, advanced mathematics, or prior experience. Learn how to analyze data with Python from scratch and enhance your analytical skills.

Is This Course Right for You?

This course is perfect for anyone who:

  • Wants to change their profession and enter the field of data analysis.
  • Aims to add data analysis skills to their resume and boost their career prospects.
  • Has a keen interest in data analysis and wishes to explore new opportunities in this dynamic field.

Course Benefits

Upon completing this training, you will gain confidence and acquire a set of practical tools that equip you to apply for entry-level positions in data analysis. Take this course and step confidently into the exciting world of data analysis.

Who teaches Data Analysis for Beginners: Python & Statistics? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Data Analysis for Beginners: Python & Statistics?

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#1: Introduction
All Course Lessons (56)
#Lesson TitleDurationAccess
1
Introduction Demo
05:16
2
Creating a Google Account
05:24
3
Setting Up the Course Materials
03:24
4
Game Plan
01:54
5
Print Function
05:48
6
Python - Print Function
10:28
7
Input Function
04:20
8
Python - Input Function
08:09
9
CHALLENGE - Your Superhero Name
06:51
10
Variable Types
03:02
11
Python - Variable Types
06:27
12
Arithmetic Operators
06:04
13
Python - Arithmetic Operators
06:48
14
Comparison Operators
03:41
15
Python - Comparison Operators
04:57
16
CHALLENGE - Split Bill Calculator
11:29
17
The if-else Condition
06:22
18
Python - if-else Conditions
06:02
19
EXERCISE - Can You Vote?
02:29
20
EXERCISE - Grading Papers
05:22
21
CHALLENGE - Berghain Club Bouncer
11:55
22
CHALLENGE - Your Monthly Savings Plan
19:44
23
Wrap Up - Python Essentials
02:22
24
Game Plan
03:08
25
While Loop
02:27
26
Python - While Loops
06:45
27
EXERCISE - Countdown Times
05:03
28
Python Lists
07:01
29
Python - Lists
10:31
30
EXERCISE - Monthly Expense Report
06:43
31
EXERCISE - Fibonacci Sequence
06:07
32
Randomization
04:36
33
Python - Randomization
05:25
34
EXERCISE - Movie Picker
06:22
35
CHALLENGE - Read my Mind
09:01
36
Dictionaries
04:03
37
Python - Dictionaries
06:31
38
EXERCISE - Magical Pet Sounds
07:36
39
CHALLENGE - Budget Mastermind
16:20
40
For Loops
04:42
41
Python - For Loops
04:19
42
EXERCISE - Sum of Numbers
04:27
43
EXERCISE - Counting the Number of Characters
07:59
44
CHALLENGE - Treasure Hunter
14:09
45
Functions
06:49
46
Python - Functions
06:19
47
EXERCISE - Function That Adds Numbers
02:21
48
EXERCISE - Function That Counts Vowels
04:36
49
EXERCISE - Function That Transforms Fahrenheit to Celsius
06:35
50
CHALLENGE - Recipe Converter
24:41
51
Wrap Up - Python Intermediate Skills
03:24
52
Project Presentation - Virtual Escape Game
05:52
53
Python - Plan the Solution
08:16
54
Python - Check User's Answer Function
08:48
55
Python - Prepare Game
18:12
56
Python - Solving with ChatGPT
06:54
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Frequently asked questions

What prerequisites are needed for this course?
This course is designed for beginners, so there are no prerequisites required. It does not require advanced mathematics or prior experience in programming. The lessons start with foundational topics such as setting up course materials and basic Python functions, making it accessible to anyone interested in learning data analysis from scratch.
What projects or exercises will I work on during the course?
Throughout the course, you will engage in various exercises and challenges designed to reinforce learning. For instance, you will work on projects like a 'Split Bill Calculator', 'Monthly Savings Plan', and 'Budget Mastermind'. Additionally, there is a final project titled 'Virtual Escape Game' where you apply the Python skills you've acquired to plan and check solutions in a game format.
Who is the target audience for this course?
The course is ideal for individuals looking to transition into data analysis careers, those wanting to enhance their resumes with data analysis skills, and anyone with a keen interest in exploring opportunities in data analysis. It caters to beginners without requiring previous experience in the field.
What specific tools or platforms will I learn to use?
In this course, you will primarily focus on learning Python, a key tool used in data analysis. The lessons cover various Python functionalities, such as variable types, arithmetic operators, lists, dictionaries, and functions. These skills are foundational for analyzing data effectively.
What topics or skills are not covered in this course?
The course does not delve into advanced statistical methods or complex data science techniques. It focuses on fundamental Python programming and basic statistical concepts suitable for beginners. Those looking for advanced data science topics may need to seek additional resources after completing this course.
How much time should I expect to commit to this course?
The course consists of a total of 56 lessons. While the exact runtime is not specified, students should plan to dedicate several hours per week to complete the lessons, exercises, and challenges. The pace is flexible, allowing learners to progress according to their own schedules.
How will the skills learned in this course benefit my career?
Upon completion, you will have developed practical Python programming skills and a foundational understanding of data analysis, equipping you for entry-level positions in data analysis. These skills are widely applicable across various industries and can serve as a stepping stone for further learning and career advancement in the field of data science.