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[FTU] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

[FTU] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp

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Description
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

Bestseller

Created by 365 Careers, 365 Careers Team
Last updated 4/2019
English

This course includes:

• 25 hours on-demand video
• 80 articles
• 126 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• The course provides the entire toolbox you need to become a data scientist
• Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Requirements

• No prior experience is required. We will start from the very basics
• You’ll need to install Anaconda. We will show you how to do that step by step
• Microsoft Excel 2003, 2010, 2013, 2016, or 365

Description

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, 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, time-efficient, and structured data science training available online, we created The Data Science Course 2019.

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 general-purpose 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 scikit-learn, 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 non-technical 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 real-life business cases that will get you the job

You will become a data scientist from scratch

We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer 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.

Who this course is for :

• You should take this course if you want to become a Data Scientist or if you want to learn about the field
• This course is for you if you want a great career
• The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills.

Course content
all 434 lectures 25:30:14

For More Udemy Free Courses >>> https://ftuforum.com/
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.ftuforum.com/




File list
  • [FTU] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
  • 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 43.9 MB
  • 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt 6.4 KB
  • 1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.vtt 5.6 KB
  • 1. Part 1 Introduction/2. What Does the Course Cover.mp4 34.5 MB
  • 1. Part 1 Introduction/2. What Does the Course Cover.srt 5.1 KB
  • 1. Part 1 Introduction/2. What Does the Course Cover.vtt 4.5 KB
  • 1. Part 1 Introduction/3. Download All Resources and Important FAQ.html 20.8 KB
  • 1. Part 1 Introduction/3.1 FAQ_The_Data_Science_Course.pdf.pdf 306.1 KB
  • 1. Part 1 Introduction/3.2 Download All Resources.html 134 B
  • 10. Combinatorics/1. Fundamentals of Combinatorics.mp4 7.5 MB
  • 10. Combinatorics/1. Fundamentals of Combinatorics.srt 1.3 KB
  • 10. Combinatorics/1. Fundamentals of Combinatorics.vtt 1.2 KB
  • 10. Combinatorics/1.1 Course Notes - Combinatorics.pdf.pdf 226.1 KB
  • 10. Combinatorics/10. Solving Variations without Repetition.html 158 B
  • 10. Combinatorics/11. Solving Combinations.mp4 57.4 MB
  • 10. Combinatorics/11. Solving Combinations.srt 5.6 KB
  • 10. Combinatorics/11. Solving Combinations.vtt 5 KB
  • 10. Combinatorics/11.1 Combinations With Repetition.pdf.pdf 207.4 KB
  • 10. Combinatorics/12. Solving Combinations.html 158 B
  • 10. Combinatorics/13. Symmetry of Combinations.mp4 38.7 MB
  • 10. Combinatorics/13. Symmetry of Combinations.srt 4.3 KB
  • 10. Combinatorics/13. Symmetry of Combinations.vtt 3.8 KB
  • 10. Combinatorics/13.1 Symmetry Explained.pdf.pdf 85 KB
  • 10. Combinatorics/14. Symmetry of Combinations.html 158 B
  • 10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 33.1 MB
  • 10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.srt 3.7 KB
  • 10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.vtt 3.3 KB
  • 10. Combinatorics/16. Solving Combinations with Separate Sample Spaces.html 158 B
  • 10. Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 39.4 MB
  • 10. Combinatorics/17. Combinatorics in Real-Life The Lottery.srt 4.1 KB
  • 10. Combinatorics/17. Combinatorics in Real-Life The Lottery.vtt 3.6 KB
  • 10. Combinatorics/18. Combinatorics in Real-Life The Lottery.html 158 B
  • 10. Combinatorics/19. A Recap of Combinatorics.mp4 40.9 MB
  • 10. Combinatorics/19. A Recap of Combinatorics.srt 3.8 KB
  • 10. Combinatorics/19. A Recap of Combinatorics.vtt 3.3 KB
  • 10. Combinatorics/2. Fundamentals of Combinatorics.html 158 B
  • 10. Combinatorics/20. A Practical Example of Combinatorics.mp4 134.1 MB
  • 10. Combinatorics/20. A Practical Example of Combinatorics.srt 14 KB
  • 10. Combinatorics/20. A Practical Example of Combinatorics.vtt 12.4 KB
  • 10. Combinatorics/3. Permutations and How to Use Them.mp4 41.5 MB
  • 10. Combinatorics/3. Permutations and How to Use Them.srt 4.1 KB
  • 10. Combinatorics/3. Permutations and How to Use Them.vtt 3.6 KB
  • 10. Combinatorics/4. Permutations and How to Use Them.html 158 B
  • 10. Combinatorics/5. Simple Operations with Factorials.mp4 36.1 MB
  • 10. Combinatorics/5. Simple Operations with Factorials.srt 3.3 KB
  • 10. Combinatorics/5. Simple Operations with Factorials.vtt 2.9 KB
  • 10. Combinatorics/6. Simple Operations with Factorials.html 158 B
  • 10. Combinatorics/7. Solving Variations with Repetition.mp4 34 MB
  • 10. Combinatorics/7. Solving Variations with Repetition.srt 3.5 KB
  • 10. Combinatorics/7. Solving Variations with Repetition.vtt 3.1 KB
  • 10. Combinatorics/8. Solving Variations with Repetition.html 158 B
  • 10. Combinatorics/9. Solving Variations without Repetition.mp4 43.1 MB
  • 10. Combinatorics/9. Solving Variations without Repetition.srt 4.5 KB
  • 10. Combinatorics/9. Solving Variations without Repetition.vtt 4 KB
  • 11. Bayesian Inference/1. Sets and Events.mp4 25 MB
  • 11. Bayesian Inference/1. Sets and Events.srt 5.1 KB
  • 11. Bayesian Inference/1. Sets and Events.vtt 4.5 KB
  • 11. Bayesian Inference/1.1 Course Notes - Bayesian Inference.pdf.pdf 386 KB
  • 11. Bayesian Inference/10. Mutually Exclusive Sets.html 158 B
  • 11. Bayesian Inference/11. Dependence and Independence of Sets.mp4 34.8 MB
  • 11. Bayesian Inference/11. Dependence and Independence of Sets.srt 3.5 KB
  • 11. Bayesian Inference/11. Dependence and Independence of Sets.vtt 3 KB
  • 11. Bayesian Inference/12. Dependence and Independence of Sets.html 158 B
  • 11. Bayesian Inference/13. The Conditional Probability Formula.mp4 45.9 MB
  • 11. Bayesian Inference/13. The Conditional Probability Formula.srt 4.9 KB
  • 11. Bayesian Inference/13. The Conditional Probability Formula.vtt 4.4 KB
  • 11. Bayesian Inference/14. The Conditional Probability Formula.html 158 B
  • 11. Bayesian Inference/15. The Law of Total Probability.mp4 35.2 MB
  • 11. Bayesian Inference/15. The Law of Total Probability.srt 3.5 KB
  • 11. Bayesian Inference/15. The Law of Total Probability.vtt 3.1 KB
  • 11. Bayesian Inference/16. The Additive Rule.mp4 25.7 MB
  • 11. Bayesian Inference/16. The Additive Rule.srt 2.6 KB
  • 11. Bayesian Inference/16. The Additive Rule.vtt 2.4 KB
  • 11. Bayesian Inference/17. The Additive Rule.html 158 B
  • 11. Bayesian Inference/18. The Multiplication Law.mp4 42.9 MB
  • 11. Bayesian Inference/18. The Multiplication Law.srt 4.6 KB
  • 11. Bayesian Inference/18. The Multiplication Law.vtt 4.1 KB
  • 11. Bayesian Inference/19. The Multiplication Law.html 158 B
  • 11. Bayesian Inference/2. Sets and Events.html 158 B
  • 11. Bayesian Inference/20. Bayes' Law.mp4 59.6 MB
  • 11. Bayesian Inference/20. Bayes' Law.srt 7.2 KB
  • 11. Bayesian Inference/20. Bayes' Law.vtt 6.4 KB
  • 11. Bayesian Inference/21. Bayes' Law.html 158 B
  • 11. Bayesian Inference/3. Ways Sets Can Interact.mp4 45.4 MB
  • 11. Bayesian Inference/3. Ways Sets Can Interact.srt 4.4 KB
  • 11. Bayesian Inference/3. Ways Sets Can Interact.vtt 3.9 KB
  • 11. Bayesian Inference/4. Ways Sets Can Interact.html 158 B
  • 11. Bayesian Inference/5. Intersection of Sets.mp4 27 MB
  • 11. Bayesian Inference/5. Intersection of Sets.srt 2.5 KB
  • 11. Bayesian Inference/5. Intersection of Sets.vtt 2.2 KB
  • 11. Bayesian Inference/6. Intersection of Sets.html 158 B
  • 11. Bayesian Inference/7. Union of Sets.mp4 57.2 MB
  • 11. Bayesian Inference/7. Union of Sets.srt 5.5 KB
  • 11. Bayesian Inference/7. Union of Sets.vtt 5 KB
  • 11. Bayesian Inference/8. Union of Sets.html 158 B
  • 11. Bayesian Inference/9. Mutually Exclusive Sets.mp4 25.4 MB
  • 11. Bayesian Inference/9. Mutually Exclusive Sets.srt 2.5 KB
  • 11. Bayesian Inference/9. Mutually Exclusive Sets.vtt 2.2 KB
  • 12. Probability Distributions/1. Fundamentals of Probability Distributions.mp4 42.4 MB

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