The Data Science Course 2021: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
Created by 365 Careers  Creating opportunities for Business & Finance students
Students: 411572, Price: $94.99
Students: 411572, Price: Paid
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programmingoriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.

Understanding of the data science field and the type of analysis carried out

Mathematics

Statistics

Python

Applying advanced statistical techniques in Python

Data Visualization

Machine Learning

Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, timeefficient, and structured data science training available online, we created The Data Science Course 2021.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it?
As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a generalpurpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikitlearn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to nontechnical decision makers.
6. Advanced Statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
***What you get***

A $1250 data science training program

Active Q&A support

All the knowledge to get hired as a data scientist

A community of data science learners

A certificate of completion

Access to future updates

Solve reallife business cases that will get you the job
You will become a data scientist from scratch
We are happy to offer an unconditional 30day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a nobrainer for us, as we are certain you will love it.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and become a part of our data scientist program today.
Data Science AZ™: RealLife Data Science Exercises Included
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Created by Kirill Eremenko  Data Scientist
Students: 191699, Price: $89.99
Students: 191699, Price: Paid
Extremely HandsOn... Incredibly Practical... Unbelievably Real!
This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.
In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities  you name it!
This course will give you a full overview of the Data Science journey. Upon completing this course you will know:
 How to clean and prepare your data for analysis
 How to perform basic visualisation of your data
 How to model your data
 How to curvefit your data
 And finally, how to present your findings and wow the audience
This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:
 SQL
 SSIS
 Tableau
 Gretl
This course has preplanned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.
Or you can do the whole course and set yourself up for an incredible career in Data Science.
The choice is yours. Join the class and start learning today!
See you inside,
Sincerely,
Kirill Eremenko
Machine Learning, Data Science and Deep Learning with Python
Complete handson machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Created by Sundog Education by Frank Kane  Founder, Sundog Education. Machine Learning Pro
Students: 150695, Price: $89.99
Students: 150695, Price: Paid
New! Updated for 2021 with extra content on generative models: variational autoencoders (VAE's) and generative adversarial models (GAN's)
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the toppaying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money  it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry  and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include handson Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course  the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

Creating synthetic images with Variational AutoEncoders (VAE's) and Generative Adversarial Networks (GAN's)

Data Visualization in Python with MatPlotLib and Seaborn

Transfer Learning

Sentiment analysis

Image recognition and classification

Regression analysis

KMeans Clustering

Principal Component Analysis

Train/Test and cross validation

Bayesian Methods

Decision Trees and Random Forests

Multiple Regression

MultiLevel Models

Support Vector Machines

Reinforcement Learning

Collaborative Filtering

KNearest Neighbor

Bias/Variance Tradeoff

Ensemble Learning

Term Frequency / Inverse Document Frequency

Experimental Design and A/B Tests

Feature Engineering

Hyperparameter Tuning
...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.
If you're new to Python, don't worry  the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windowsbased PC's, Linux desktops, and Macs.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by realworld industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

"I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing."  Kanad Basu, PhD
Data Science and Machine Learning Bootcamp with R
Learn how to use the R programming language for data science and machine learning and data visualization!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 71052, Price: $89.99
Students: 71052, Price: Paid
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:
 Programming with R
 Advanced R Features
 Using R Data Frames to solve complex tasks
 Use R to handle Excel Files
 Web scraping with R
 Connect R to SQL
 Use ggplot2 for data visualizations
 Use plotly for interactive visualizations
 Machine Learning with R, including:
 Linear Regression
 K Nearest Neighbors
 K Means Clustering
 Decision Trees
 Random Forests
 Data Mining Twitter
 Neural Nets and Deep Learning
 Support Vectore Machines
 and much, much more!
Enroll in the course and become a data scientist today!
Introduction to Machine Learning for Data Science
A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
Created by David Valentine  The Backyard Data Scientist
Students: 48571, Price: $89.99
Students: 48571, Price: Paid
Course Most Recently Updated Nov/2018!
Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way.
I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered.
Most importantly:
To make this course "real", we've expanded. In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections! We hope you enjoy the new content!
Unlock the secrets of understanding Machine Learning for Data Science!
In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.
Our exotic journey will include the core concepts of:

The train wreck definition of computer science and one that will actually instead make sense.

An explanation of data that will have you seeing data everywhere that you look!

One of the “greatest lies” ever sold about the future computer science.

A genuine explanation of Big Data, and how to avoid falling into the marketing hype.

What is Artificial intelligence? Can a computer actually think? How do computers do things like navigate like a GPS or play games anyway?

What is Machine Learning? And if a computer can think – can it learn?

What is Data Science, and how it relates to magical unicorns!

How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another.
We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:

How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now.

We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014. Do you have a super computer in your home? You might be surprised to learn the truth.

We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.

Most importantly we’ll show how this is changing our lives. Not just the lives of business leaders, but most importantly…you too!
To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

How do you solve problems with Machine Learning and what are five things you must do to be successful?

How to ask the right question, to be solved by Machine Learning.

Identifying, obtaining and preparing the right data … and dealing with dirty data!

How every mess is “unique” but that tidy data is like families!

How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”

And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science.
Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. We’ll explore:

How to start applying Machine Learning without losing your mind.

What equipment Data Scientists use, (the answer might surprise you!)

The top five tools Used for data science, including some surprising ones.

And for each of the top five tools – we’ll explain what they are, and how to get started using them.

And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.
Bonus Course! To make this “really real”, I’ve included a bonus course!
Most importantly in the bonus course I’ll include information at the end of every section titled “Further Magic to Explore” which will help you to continue your learning experience.
In this bonus course we’ll explore:

Creating a real live Machine Learning Example of Titanic proportions. That’s right – we are going to predict survivability onboard the Titanic!

Use Anaconda Jupyter and python 3.x

A crash course in python  covering all the core concepts of Python you need to make sense of code examples that follow. See the included free cheat sheet!

Hands on running Python! (Interactively, with scripts, and with Jupyter)

Basics of how to use Jupyter Notebooks

Reviewing and reinforcing core concepts of Machine Learning (that we’ll soon apply!)

Foundations of essential Machine Learning and Data Science modules:

NumPy – An Array Implementation

Pandas – The Python Data Analysis Library

Matplotlib – A plotting library which produces quality figures in a variety of formats

SciPy – The fundamental Package for scientific computing in Python

ScikitLearn – Simple and efficient tools data mining, data analysis, and Machine Learning


In the titanic hands on example we’ll follow all the steps of the Machine Learning workflow throughout:

1. Asking the right question.

2. Identifying, obtaining, and preparing the right data

3. Identifying and applying a Machine Learning algorithm

4. Evaluating the performance of the model and adjusting

5. Using and presenting the model


We’ll also see a real world example of problems in Machine learning, including underfit and overfit.
The bonus course finishes with a conclusion and further resources to continue your Machine Learning journey.
So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know  everyday people... like you!
Sign up right now, and we'll see you – on the other side!
Complete Machine Learning & Data Science Bootcamp 2021
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
Created by Andrei Neagoie  Senior Software Developer / Founder of zerotomastery.io
Students: 45901, Price: $89.99
Students: 45901, Price: Paid
This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!
Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, uptodate Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).
This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.
The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!
The topics covered in this course are:
 Data Exploration and Visualizations
 Neural Networks and Deep Learning
 Model Evaluation and Analysis
 Python 3
 Tensorflow 2.0
 Numpy
 ScikitLearn
 Data Science and Machine Learning Projects and Workflows
 Data Visualization in Python with MatPlotLib and Seaborn
 Transfer Learning
 Image recognition and classification
 Train/Test and cross validation
 Supervised Learning: Classification, Regression and Time Series
 Decision Trees and Random Forests
 Ensemble Learning
 Hyperparameter Tuning
 Using Pandas Data Frames to solve complex tasks
 Use Pandas to handle CSV Files
 Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
 Using Kaggle and entering Machine Learning competitions
 How to present your findings and impress your boss
 How to clean and prepare your data for analysis
 K Nearest Neighbours
 Support Vector Machines
 Regression analysis (Linear Regression/Polynomial Regression)
 How Hadoop, Apache Spark, Kafka, and Apache Flink are used
 Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
 Using GPUs with Google Colab
By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.
Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.
Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.
Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.
You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!
Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!
Taught By:
Daniel Bourke:
A selftaught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.
My experience in machine learning comes from working at one of Australia's fastestgrowing artificial intelligence agencies, Max Kelsen.
I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.
Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.
Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.
My longterm goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".
Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5million views.
I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.
My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.
Questions are always welcome.

Andrei Neagoie:
Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.
Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.
Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.
Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.
See you inside the course!
Data Science Bootcamp with 5 Data Science Projects
Data Science and Machine Learning Masterclass with Python with 5 Data Science Real World Projects
Created by Data Is Good Academy  An Google, Facebook, Kaggle Grandmasters team
Students: 35451, Price: $19.99
Students: 35451, Price: Paid
Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights.
The Data Science field is growing so rapidly, and revolutionizing so many industries.
Data Science has incalculable benefits in business, research, and our everyday lives.
Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways.
Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.
It encompasses a wide range of topics:
1. Python.
2. Statistics.
3. Machine Learning.
4. Mathematics.
5. Data Visualization.
6. Data Cleaning.
7. Hypothesis Testing.
8. Query Analysis.
Each of these topics are build on the other. You need to acquire all the skills in the right order.
You are at the right place!!!
Welcome to this online resource to learn Data Science Skills.
The Complete Data Science Bootcamp course will really help you to boost your career.
This Data Science Course begins with the most basic level and goes up to the most advanced techniques step by step.
even if you don't know anything in advance, this course will make complete sense to you.
In this Data Science Course you will learn about the following:
1. The fundamentals of python programming language: variables, data types, loops and conditionals.
2. Python data structures: lists, tuples, dictionaries, sets, stacks, queues.
3. Objectoriented programming in python.
4. Regular Expressions.
5. Numpy library.
6. Pandas library.
7. Grouping and filtering operations for data analysis.
8. Basic and Advanced visualizations.
9. Descriptive statistics.
10. Inferential statistics.
11. Hypothesis Testing.
12. Exploring Dabl and Sweetviz library.
13. Linear Regression theory and practical.
14. Logistic Regression theory and practical.
15. Clustering analysis.
There are lots and lots of exercises for you to practice In this Python Data Science Course and also a 5 Bonus Data Science Project "Player’s Performance Reviewer", "Startups Case Study and Analysis", "Movie Recommender Engine", "Global Cost of Living Analysis" and "Customer Segmentation Engine".
In this Player’s Performance Reviewer project, you will analyze the performance metrics of players based on their ground positions, skills, nationality, clubs, age, height, weight, and understanding the major factors driving the performance of these players.
In this Startups Case Study and Analysis project, you will analyze the Indian Startups, and Understand the Startup Ecosystems in India to answer some Interesting Questions. Try to find out the Major Investors and Startups.
In this Movie Recommender Engine project, you will get to learn How to analyze a Movie Database to find some useful insights and Recommend Movies.
In this Global Cost of Living Analysis project, you will learn how to perform Geospatial Analysis and understand some major factors determining the quality of life in different cities of the world. And also learn to perform Comparative analysis.
In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.
You will make use of all the topics read in this Python Data Science Course 2021.
You will also have access to all the resources used in this Python Data Science Course 2021.
Enroll now and become a Data Science professional!!!
Computer Science 101: Master the Theory Behind Programming
Computer Science 101: Learn Computer Science to become a better Programmer and Software Engineer.
Created by Kurt Anderson  MultiMedia Designer, Computer Scientist, YouTube Guru
Students: 17739, Price: $49.99
Students: 17739, Price: Paid
Master the Theory to Becoming a Good Programmer!
If you're looking to learn the theory that makes great programmers, you've come to the right place! This course is perfect for anyone interested in learning the fundamentals to Computer Science Theory.
No Previous Experience Necessary!
Computer science and technology are often thought of as things only for "analytical minds". I believe however that technology and it's theory are for everyone. So I designed this course to teach each topic in a variety of easy to digest ways. Through these multiple reinforcing steps, I believe anyone can follow along and succeed!
Why is the Theory of Programming Important?
Understanding Computer Science theory is what sets apart Great programmers from average ones. Programming theory is something that transcends a single programming language. It gives you skills and techniques you can apply to any programming language you touch. Learning the theory behind programming is just as important, if not more important than learning a singular programming language like Java or C++.
Programming is all about problem solving. Analyzing a problem, and being able to figure out a way that a computer can help with that problem. Computer Science is the practice of this analysis process. It goes over the techniques and knowledge necessary to design efficient and sustainable code.
So if you want to begin setting yourself apart from the average programmers, this is the course for you!
Enroll Now and you'll Learn:

Binary Number System

N Notation

Big O Notation

How to Analyze a Program

Arrays and their Advantages

Nodes and their Importance

Linked Lists and their Advantages and Implementations

Stacks implemented with Arrays and Linked Lists

Queues Implemented with Arrays and Linked Lists

Various Sorting Algorithms and Their Comparisions

Trees and Binary Search Trees

And Much Much More!
My Guarantee
I am so confident you will enjoy this course, I offer a 100% 30day moneyback guarantee through Udemy. If you are not happy with your purchase, I have no problem with giving your money back!
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Kurt
Data Science 2021 : Complete Data Science & Machine Learning
Machine Learning AZ, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science
Created by Jitesh Khurkhuriya  Data Scientist and Digital Transformation Consultant
Students: 11464, Price: $89.99
Students: 11464, Price: Paid
Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning AZ, Deep learning and more?
Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.
We are going to execute following reallife projects,

Kaggle Bike Demand Prediction from Kaggle competition

Automation of the Loan Approval process

The famous IRIS Classification

Adult Income Predictions from US Census Dataset

Bank Telemarketing Predictions

Breast Cancer Predictions

Predict Diabetes using Prima Indians Diabetes Dataset
Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.
As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?
Data Science and Machine Learning require indepth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,

Understanding of the overall landscape of Data Science and Machine Learning

Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects

Python Programming skills which is the most popular language for Data Science and Machine Learning

Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science

Statistics and Statistical Analysis for Data Science

Data Visualization for Data Science

Data processing and manipulation before applying Machine Learning

Machine Learning

Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning

Feature Selection and Dimensionality Reduction for Machine Learning models

Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning

Cluster Analysis for unsupervised Machine Learning

Deep Learning using most popular tools and technologies of today.
This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning AZ Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.
Also, without understanding the Mathematics and Statistics it's impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work.
Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said,
"If you can not explain it simply, you have not understood it enough."
As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth.
As you will see from the preview lectures, some of the most complex topics are explained in a simple language.
Some of the key skills you will learn,

Python Programming
Python has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras.

Advance Mathematics for Machine Learning
Mathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives.

Advance Statistics for Data Science
It is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning.

Data Visualization
As they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it.

Data Processing
Data Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data.

Machine Learning
The heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models.

Feature Selection and Dimensionality Reduction
In case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA.

Deep Learning
You can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world.

Kaggle Project
As an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you.
Your takeaway from this course,

Complete handson experience with huge number of Data Science and Machine Learning projects and exercises

Learn the advance techniques used in the Data Science and Machine Learning

Certificate of Completion for the most in demand skill of Data Science and Machine Learning

All the queries answered in shortest possible time.

All future updates based on updates to libraries, packages

Continuous enhancements and addition of future Machine Learning course material

All the knowledge of Data Science and Machine Learning at fraction of cost
This Data Science and Machine Learning course comes with the Udemy's 30DayMoneyBack Guarantee with no questions asked.
So what you are waiting for? Hit the "Buy Now" button and get started on your Data Science and Machine Learning journey without spending much time.
I am so eager to see you inside the course.
Disclaimer: All the images used in this course are either created or purchased/downloaded under the license from the provider, mostly from Shutterstock or Pixabay.
Intro to Data Science: Your StepbyStep Guide To Starting
Learn the critical elements of Data Science, from visualization to databases to Python and more, in just 6 weeks!
Created by Kirill Eremenko  Data Scientist
Students: 11210, Price: $89.99
Students: 11210, Price: Paid
The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself.
The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks.
With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You’ll discover:
* The structured path for rapidly acquiring Data Science expertise
* How to build your ability in statistics to help interpret and analyse data more effectively
* How to perform visualizations using one of the industry's most popular tools
* How to apply machine learning algorithms with Python to solve real world problems
* Why the cloud is important for Data Scientists and how to use it
Along with much more. You'll pick up all the core concepts that veteran Data Scientists understand intimately. Use common industrywide tools like SQL, Tableau and Python to tackle problems. And get guidance on how to launch your own Data Science projects.
In fact, it might seem like too much at first. And there is a lot of content, exercises, study and challenges to get through. But with the right attitude, becoming a Data Scientist this quickly IS possible!
Once you've finished Introduction to Data Science AZ, you’ll be ready for an incredible career in a field that's expanding faster than almost anything else in the world.
Complete this course, master the principles, and join the ranks of Data Scientists all around the world.
Careers in Data Science AZ™
How to Become a Top Level Data Scientist  Learn What to Expect, How to be Prepared, How to Stand Out and More...
Created by Kirill Eremenko  Data Scientist
Students: 10749, Price: $89.99
Students: 10749, Price: Paid
Becoming a Data Scientist might be on your mind right now.
Named the "Sexiest Job of the 21st Century", this career seems like a great idea not only due to its high demand, but lack of supply of skilled proffesionals.
But the million dollar question is: What makes the difference between Top Level Data Scientist and just another one from the bunch?
Here is where this course jumps in...
With over 8 years combined experience in the field, we've decided to step back and put all of the lessons we've learned through our careers into one simple course.
If you want to get valuable insights, advice, hacks & tips, recommendations, lessons from failures and successes from our careers and learn how to apply it to your own and take your Data Science career to the next level, then this course is just for you.
Linear Algebra for Beginners: Open Doors to Great Careers
Learn the core topics of Linear Algebra to open doors to Computer Science, Data Science, Actuarial Science, and more!
Created by Richard Han  PhD in Mathematics
Students: 4554, Price: $19.99
Students: 4554, Price: Paid

The prerequisite to the course Linear Algebra for Beginners: Open Doors to Great Careers 2.

Would you like to learn a mathematics subject that is crucial for many highdemand lucrative career fields such as:
 Computer Science
 Data Science
 Actuarial Science
 Financial Mathematics
 Cryptography
 Engineering
 Computer Graphics
 Economics
If you're looking to gain a solid foundation in Linear Algebra, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you. If you're a working professional needing a refresher on linear algebra or a complete beginner who needs to learn Linear Algebra for the first time, this online course is for you.
Why you should take this online course: You need to refresh your knowledge of linear algebra for your career to earn a higher salary. You need to learn linear algebra because it is a required mathematical subject for your chosen career field such as computer science or electrical engineering. You intend to pursue a masters degree or PhD, and linear algebra is a required or recommended subject.
Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have extensive teaching experience: 6 years as a teaching assistant at University of California, Riverside, over two years as a faculty member at Western Governors University, #1 in secondary education by the National Council on Teacher Quality, and as a faculty member at Trident University International.
In this course, I cover the core concepts such as:
 Gaussian elimination
 Vectors
 Matrix Algebra
 Determinants
 Vector Spaces
 Subspaces
After taking this course, you will feel CAREFREE AND CONFIDENT. I will break it all down into bitesized nobrainer chunks. I explain each definition and go through each example STEP BY STEP so that you understand each topic clearly. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.
Practice problems are provided for you, and detailed solutions are also provided to check your understanding.
30 day full refund if not satisfied.
Grab a cup of coffee and start listening to the first lecture. I, and your peers, are here to help. We're waiting for your insights and questions! Enroll now!
Linear Algebra for Beginners: Open Doors to Great Careers 2
Learn the core topics of Linear Algebra to open doors to Computer Science, Data Science, Actuarial Science, and more!
Created by Richard Han  PhD in Mathematics
Students: 2049, Price: $19.99
Students: 2049, Price: Paid

The sequel to the course Linear Algebra for Beginners: Open Doors to Great Careers.

Would you like to learn a mathematics subject that is crucial for many highdemand lucrative career fields such as:
 Computer Science
 Data Science
 Actuarial Science
 Financial Mathematics
 Cryptography
 Engineering
 Computer Graphics
 Economics
If you're looking to gain a solid foundation in Linear Algebra, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you. If you're a working professional needing a refresher on linear algebra or a complete beginner who needs to learn Linear Algebra for the first time, this online course is for you.
Why you should take this online course: You need to refresh your knowledge of linear algebra for your career to earn a higher salary. You need to learn linear algebra because it is a required mathematical subject for your chosen career field such as computer science or electrical engineering. You intend to pursue a masters degree or PhD, and linear algebra is a required or recommended subject.
Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have extensive teaching experience: 6 years as a teaching assistant at University of California, Riverside, over two years as a faculty member at Western Governors University, #1 in secondary education by the National Council on Teacher Quality, and as a faculty member at Trident University International.
In this course, I cover the core concepts such as:
 Inner Product Spaces
 Linear Transformations
 Eigenvalues and Eigenvectors
 Symmetric Matrices and Orthogonal Diagonalization
 Quadratic Forms
 Singular Value Decomposition
After taking this course, you will feel CAREFREE AND CONFIDENT. I will break it all down into bitesized nobrainer chunks. I explain each definition and go through each example STEP BY STEP so that you understand each topic clearly. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.
Practice problems are provided for you, and detailed solutions are also provided to check your understanding.
30 day full refund if not satisfied.
Grab a cup of coffee and start listening to the first lecture. I, and your peers, are here to help. We're waiting for your insights and questions! Enroll now!
Discrete Mathematics: Open Doors to Great Careers
Learn the core topics of Discrete Math to open doors to Computer Science, Data Science, Actuarial Science, and more!
Created by Richard Han  PhD in Mathematics
Students: 1803, Price: $19.99
Students: 1803, Price: Paid

The prerequisite to the course Discrete Mathematics: Open Doors to Great Careers 2.

Would you like to learn a mathematics subject that is crucial for many highdemand lucrative career fields such as:
 Computer Science
 Data Science
 Actuarial Science
 Financial Mathematics
 Cryptography
 Engineering
 Computer Graphics
 Economics
If you're looking to gain a solid foundation in Discrete Mathematics, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you. If you're a working professional needing a refresher on discrete mathematics or a complete beginner who needs to learn Discrete Mathematics for the first time, this online course is for you.
Why you should take this online course: You need to refresh your knowledge of discrete mathematics for your career to earn a higher salary. You need to learn discrete mathematics because it is a required mathematical subject for your chosen career field such as computer science or electrical engineering. You intend to pursue a masters degree or PhD, and discrete mathematics is a required or recommended subject.
Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have extensive teaching experience: 6 years as a teaching assistant at University of California, Riverside, four years as a faculty member at Western Governors University, #1 in secondary education by the National Council on Teacher Quality, and as a faculty member at Trident University International.
In this course, I cover core topics such as:
 Propositional Logic
 Predicate Logic
 Proofs
 Mathematical Induction
After taking this course, you will feel CAREFREE AND CONFIDENT. I will break it all down into bitesized nobrainer chunks. I explain each definition and go through each example STEP BY STEP so that you understand each topic clearly. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.
Practice problems are provided for you, and detailed solutions are also provided to check your understanding.
30 day full refund if not satisfied.
Grab a cup of coffee and start listening to the first lecture. I, and your peers, are here to help. We're waiting for your insights and questions! Enroll now!
Introduction to the Actuarial Exams
By MJ the Fellow Actuary
Created by Michael Jordan  Actuary (FASSA/CERA)
Students: 87, Price: $19.99
Students: 87, Price: Paid

What is the Purpose of Actuarial Science?

What are the Dimensions of Risk?

What is the complete syllabus of Actuarial Science?

What Actuarial Exams do I need to write?

What Study Techniques can I use?
This course aims to answer the questions above and be an introduction to anyone writing the actuarial exams.
I'll also be adding lists of recommended textbooks for each subject and other resources in time.