پیشنهاد فرادرس

دوره کامل علم داده 2018

دسته بندی ها: علم داده (Data Science) ، آموزش های یودمی (Udemy)

دانشمند داده یکی از بهترین حرفه های مناسب برای رشد در این قرن است. بنابراین، جای تعجب نیست که تقاضا برای دانشمندان داده در بازار کار افزایش یافته است. علم داده یک فیلد چندرشتته ای است که طیف وسیعی از موضوعات مانند آمار، ریاضیات، پایتون، اعمال تکنیک های پیشرفته آماری در پایتون، مصورسازی داده، یادگیری عمیق و یادگیری ماشینی شامل می شود. این دوره هر آنچه که برای تبدیل شدن به دانشمند داده نیاز داشته باشید را به شما آموزش می دهد.

سرفصل:

  • معرفی داده و علم داده
  • ریاضیات
  • آمار
  • پایتون
  • Tableau
  • آمار پیشرفته
  • یادگیری ماشینی
  • فرق بین تحلیل و آنالیز
  • تحلیل کسب و کار، تحلیل داده، و علم داده
  • هوش مصنوعی، یادگیری ماشینی
  • اعمال داده سنتی، کلان داده، BI، علم داده سنتی و یادگیری ماشینی
  • داده سنتی
  • تکنیک های هوش تجاری
  • تکنیک های یادگیری ماشینی
  • مشاغل در علم داده
  • و غیره
The Data Science Course 2018: Complete Data Science Bootcamp Publisher:Udemy Author:365 Careers Duration:16:43:38

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
The Problem
Data scientist is one of the best suited professions to thrive in this century. Digital. Programming-oriented. 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 2018.
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 in the field of data science but what do they all mean? 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.
Why learn it?
   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 case 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 is the target audience?
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 fundementals and gradually builds up your skills

Part 1: Introduction
2 Lectures
08:39
A Practical Example: What You Will Learn in This Course
Preview
05:05
What Does the Course Cover
Preview
03:34

The Field of Data Science - The Various Data Science Disciplines
5 Lectures
31:11
Data Science and Business Buzzwords: Why are there so many?
Preview
05:21
Data Science and Business Buzzwords: Why are there so many?
1 question
What is the difference between Analysis and Analytics
03:50
What is the difference between Analysis and Analytics
1 question
Business Analytics, Data Analytics, and Data Science: An Introduction
Preview
08:26
Business Analytics, Data Analytics, and Data Science: An Introduction
3 questions
Continuing with BI, ML, and AI
09:31
Continuing with BI, ML, and AI
2 questions
A Breakdown of our Data Science Infographic
04:03
A Breakdown of our Data Science Infographic
1 question

The Field of Data Science - Connecting the Data Science Disciplines
1 Lecture
07:19
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
07:19
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
1 question

The Field of Data Science - The Benefits of Each Discipline
1 Lecture
04:44
The Reason behind these Disciplines
04:44
The Reason behind these Disciplines
1 question

The Field of Data Science - Popular Data Science Techniques
11 Lectures
53:34
Techniques for Working with Traditional Data
08:13
Techniques for Working with Traditional Data
1 question
Real Life Examples of Traditional Data
01:44
Techniques for Working with Big Data
04:26
Techniques for Working with Big Data
1 question
Real Life Examples of Big Data
01:32
Business Intelligence (BI) Techniques
06:45
Business Intelligence (BI) Techniques
4 questions
Real Life Examples of Business Intelligence (BI)
01:42
Techniques for Working with Traditional Methods
09:08
Techniques for Working with Traditional Methods
4 questions
Real Life Examples of Traditional Methods
02:45
Machine Learning (ML) Techniques
06:55
Machine Learning (ML) Techniques
2 questions
Types of Machine Learning
08:13
Types of Machine Learning
2 questions
Real Life Examples of Machine Learning (ML)
02:11
Real Life Examples of Machine Learning (ML)
5 questions

The Field of Data Science - Popular Data Science Tools
1 Lecture
05:51
Necessary Programming Languages and Software Used in Data Science
05:51
Necessary Programming Languages and Software Used in Data Science
4 questions

The Field of Data Science - Careers in Data Science
1 Lecture
03:29
Finding the Job - What to Expect and What to Look for
03:29
Finding the Job - What to Expect and What to Look for
1 question

The Field of Data Science - Debunking Common Misconceptions
1 Lecture
04:10
Debunking Common Misconceptions
04:10
Debunking Common Misconceptions
1 question

Part 2: Statistics
1 Lecture
04:02
Population and Sample
04:02
Population and Sample
2 questions

Statistics - Descriptive Statistics
22 Lectures
48:11
Types of Data
04:33
Types of Data
2 questions
Levels of Measurement
03:43
Levels of Measurement
2 questions
Categorical Variables - Visualization Techniques
04:52
Categorical Variables Exercise
00:03
Numerical Variables - Frequency Distribution Table
03:09
Numerical Variables Exercise
00:03
The Histogram
02:14
Histogram Exercise
00:03
Cross Table and Scatter Plot
04:44
Cross Tables and Scatter Plots Exercise
00:03
Mean, median and mode
04:20
Mean, Median and Mode Exercise
00:03
Skewness
02:37
Skewness Exercise
00:03
Variance
05:55
Variance Exercise
00:15
Standard Deviation and Coefficient of Variation
04:40
Standard Deviation and Coefficient of Variation Exercise
00:03
Covariance
03:23
Covariance Exercise
00:03
Correlation Coefficient
03:17
Correlation Coefficient Exercise
00:03
35 More Sections

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