beginner to advanced – how to become a data scientist

beginner to advanced – how to become a data scientist, available at $49.99, has an average rating of 4.35, with 67 lectures, based on 42 reviews, and has 914 subscribers.

You will learn about You can apply important data science methods on any dataset you want You have acquired a deep understanding in data exploration and preparation techniques You understand numpy and it‘s importance for data science You can apply advanced visualization techniques to present your findings you are prepared to dive deeper into machine learning and neural networks You might open up new career opportunities for you which are not only highly rewarding but also offer more job satisfaction This course is ideal for individuals who are beginners with no prior knowlege or beginners who have acquired some knowledge or students who are interested in a data science career or students who want to acquire a solid foundation to dive into machine learning and neural networks or You want to take advantage of the data driven opportunity ahead It is particularly useful for beginners with no prior knowlege or beginners who have acquired some knowledge or students who are interested in a data science career or students who want to acquire a solid foundation to dive into machine learning and neural networks or You want to take advantage of the data driven opportunity ahead.

Enroll now: beginner to advanced – how to become a data scientist

Summary

Title: beginner to advanced – how to become a data scientist

Price: $49.99

Average Rating: 4.35

Number of Lectures: 67

Number of Published Lectures: 66

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 66

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

You can apply important data science methods on any dataset you want
You have acquired a deep understanding in data exploration and preparation techniques
You understand numpy and it‘s importance for data science
You can apply advanced visualization techniques to present your findings
you are prepared to dive deeper into machine learning and neural networks
You might open up new career opportunities for you which are not only highly rewarding but also offer more job satisfaction

Who Should Attend

beginners with no prior knowlege
beginners who have acquired some knowledge
students who are interested in a data science career
students who want to acquire a solid foundation to dive into machine learning and neural networks
You want to take advantage of the data driven opportunity ahead

Target Audiences

beginners with no prior knowlege
beginners who have acquired some knowledge
students who are interested in a data science career
students who want to acquire a solid foundation to dive into machine learning and neural networks
You want to take advantage of the data driven opportunity ahead

So you want to become a data scientist hm? But you do not know how and where to start?

If your answer to these question is : Yes that’s correct, then you are at the right place!

You could not have chosen a better time to introduce yourself to this topic.Data science is the most interesting topic in the world we live in and beside that also highly rewarding. It will shape our future and therefore it’s better to act now than regret later. Any kind of machine learning (self driving cars, stock market prediction, image recognition, text analyzing or simply getting insights of huge datasets – it’s all part of data science.

The jobs of tomorrow – self employed or employed will encounter exploring, analyzing and visualizing data – it’ s simply the “oil of this century”. And the golden times are yet to come!

“From my personal experienceI can tell you that companies will actively searching for you if you aquire some skills in the data science field. Diving into this topic can not only immensly improve your career opportunities but also your job satisfaction!”

With this in mind it’s totally understandable that smart people like you are searching for a way to enter this topic. Most often the biggest problem is how to find the right way master data science from scratch. And that’s what this course is all about.

My goal is to show you and easy, interesting and efficient way to start data science from scratch. Even if you have barely started with coding and only know the basics of python, this course will help you to learn all the relevant skills for data science!

Together let’s learn, explore and apply the core fundamentals in data science for machine learning / deep learning / neural networks and set up the foundation for you future career..

Can’t wait to start coding with you! Meet me in the first lecture!

Best�

Daniel

Course Curriculum

Chapter 1: Course introduction

Lecture 1: Introduction – Why are you here and what we will accomplish here

Lecture 2: One important thing before you start

Lecture 3: What are the prerequesits for data science and this course

Lecture 4: Check you system

Lecture 5: Download all the source files

Chapter 2: pandas for data science

Lecture 1: 0 All you need to know about Series

Lecture 2: 1 pandas for data scientists

Lecture 3: 2 pandas for data scientists

Lecture 4: 3 pandas for data scientists

Lecture 5: 4 pandas for data scientists

Lecture 6: 5 Broadcasting operations

Lecture 7: 6 Counting

Lecture 8: 7 The issue with missing values – a common problem in machine learning

Lecture 9: 8 Dealing with missing values 2

Lecture 10: 9 The right data in the right format

Lecture 11: 10 Sorting your data properly

Lecture 12: 11 How to slice your data 1

Lecture 13: 12 How to slice your data 2

Lecture 14: 13 How to check for missing values

Lecture 15: 14 A machine learning insight – a full case study

Lecture 16: 15 Master dates

Lecture 17: 16 How to deal with dublicates

Lecture 18: 17 How to play with the Index

Lecture 19: 18 Slicing techniques

Lecture 20: 19 Slicing techniques 2

Lecture 21: 20 More data science techniques in pandas

Lecture 22: 21 Data querying in pandas

Lecture 23: 22 How to work with dates

Lecture 24: 23 How to work with dates 2

Lecture 25: 24 How to work with dates 3

Lecture 26: 25 How to work with dates 4

Lecture 27: 26 Grouping in pandas beginner to advanced

Lecture 28: 27 The Multiindex

Lecture 29: 28 Data science and Finance

Lecture 30: 29 In depth combining dataframes

Lecture 31: 30 Useful ways to deal with strings (regex example)

Lecture 32: 31 Bonus Tips and Tricks

Lecture 33: 32 Bonus Tips and Tricks 2

Lecture 34: 33 Bonus Tips and Tricks 3

Chapter 3: Introduction to numpy – what you need to know

Lecture 1: 34 What are Tensors

Lecture 2: 35 Introduction to numpy 1

Lecture 3: 36 Introduction to numpy 2

Lecture 4: 37 Introduction to numpy 3

Lecture 5: 38 Introduction to numpy 4

Chapter 4: Data Visualization

Lecture 1: 39 Matplotlib – a how to guide

Lecture 2: 40 Matplotlib – advanced

Lecture 3: 41 Matplotlib – advanced

Chapter 5: Master Data Visualization with Seaborn

Lecture 1: 42 Seaborn introduction

Lecture 2: 43 how to master seaborn 1

Lecture 3: 44 how to master seaborn 2

Lecture 4: 45 how to master seaborn 3

Lecture 5: 46 how to master seaborn 4

Lecture 6: 47 how to master seaborn 5

Lecture 7: 48 how to master seaborn 6

Lecture 8: 49 how to master seaborn 7

Lecture 9: 50 how to master seaborn 8

Lecture 10: 51 how to master seaborn 9

Lecture 11: 52 how to master seaborn 10

Lecture 12: 53 how to master seaborn 11

Lecture 13: 54 how to master seaborn 12

Lecture 14: 55 how to master seaborn 13

Lecture 15: 56 how to master seaborn 14

Lecture 16: 57 The end of the road – What to do now?

Lecture 17: More learning resources for your AI learning journey

Lecture 18: Bonus – How to use Transfer learning to predict ice cream

Lecture 19: If you like my teaching style and want to continue learning together

Instructors

Dan We
Coach

Rating Distribution

1 stars: 1 votes
2 stars: 3 votes
3 stars: 7 votes
4 stars: 12 votes
5 stars: 19 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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