The Data Science Course 2019: Complete Data Science Bootcamp

The Data Science Course 2019: Complete Data Science Bootcamp

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What you will 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

Curriculum

Section 1: Part 1: Introduction

Section 2: The Field of Data Science - The Various Data Science Disciplines

Section 3: The Field of Data Science - Connecting the Data Science Disciplines

Section 4: The Field of Data Science - The Benefits of Each Discipline

Section 5: The Field of Data Science - Popular Data Science Techniques

Section 6: The Field of Data Science - Popular Data Science Tools

Section 7: The Field of Data Science - Careers in Data Science

Section 8: The Field of Data Science - Debunking Common Misconceptions

Section 9: Part 3: Statistics

Section 10: Statistics - Descriptive Statistics

Section 11: Statistics - Practical Example: Descriptive Statistics

Section 12: Statistics - Inferential Statistics Fundamentals

Section 13: Statistics - Inferential Statistics: Confidence Intervals

Section 14: Statistics - Practical Example: Inferential Statistics

Section 15: Statistics - Hypothesis Testing

Section 16: Statistics - Practical Example: Hypothesis Testing

Section 17: Part 4: Introduction to Python

Section 18: Python - Variables and Data Types

Section 19: Python - Basic Python Syntax

Section 20: Python - Other Python Operators

Section 21: Python - Python Functions

Section 22: Python - Sequences

Section 23: Python - Iterations

Section 24: Python - Advanced Python Tools

Section 25: Part 5: Advanced Statistical Methods in Python

Section 26: Advanced Statistical Methods - Linear regression with StatsModels

Section 27: Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Section 28: Advanced Statistical Methods - Cluster Analysis

Section 29: Advanced Statistical Methods - K-Means Clustering

Section 30: Advanced Statistical Methods - Other Types of Clustering

Section 31: Part 6: Mathematics

Section 32: Part 7: Deep Learning

Section 33: Deep Learning - Introduction to Neural Networks

Section 34: Deep Learning - How to Build a Neural Network from Scratch with NumPy

Section 35: Deep Learning - TensorFlow: Introduction

Section 36: Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks

Section 37: Deep Learning - Overfitting

Section 38: Deep Learning - Initialization

Section 39: Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

Section 40: Deep Learning - Preprocessing

Section 41: Deep Learning - Classifying on the MNIST Dataset

Section 42: Deep Learning - Business Case Example

Section 43: Deep Learning - Conclusion

Course Description

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

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