# The Data Science Course : Complete Data Science Bootcamp

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

## 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 TensorFlow Develop 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 y0ur 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 professionals 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 programms. (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 sience 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 2021.**

We believe this is the first training program that solves the biggest challenges 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 prograamms (not to mention the amount of time you will save).

**The Skills**

**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 detail 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 it’s 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 visualization. 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 scientistis don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right descisions to make. These executives may not be well versed in data science, so the data scientist must be able to present and visualize 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 like Tableau to communicate complex results to non-technical descision makers.

**6. Advanced Statistics**

Regressions, clustering and factor analysis are all disciplines that wew invented before machine learning. However, now these statistical methods are all performed through machine learning to provide presictions 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 learnning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learnining 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.

**You will become a data scientist from scratch.**

## 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

- Introduction

- The Various Data Science Disciplines

- Connecting the Data Science Disciplines

- The Benefits of Each Discipline

- Popular Data Science Techniques

- Popular Data Science Tools

- Careers in Data Science

- Debunking Common Misconceptions

- Probability

- Combinatorics

- Bayesian Inference

- Distributions

- Probability in Other Field

- Statistics

- Descriptive Statistics

- Practical Example: Descriptive Statistics

- Statistics - Inferential Statistics Fundamentals

- Statistics - Inferential Statistics: Confidence Intervals

- Statistics - Practical Example: Inferential Statistics

- Statistics - Hypothesis Testing

- Statistics - Practical Example : Hypothesis Testing

- Introduction to Python

- Python - Variables and Data Types

- Python - Basic Syntax

- Python - Other Python Operators

- Python - Conditional Statements

- Python - Python Functions

- Python - Python Sequences

- Python - Iterations

- Python - Advanced Python Tools

- Advanced Statistical Methods in Python

- Advanced Statistical Methods - Linear Regression with StatsModels

- Advanced Statistical Methods - Multiple Linear Regression with StatsModels

- Advanced Statistical Methods - Linear Regression with sklearn

- Advanced Statistical Methods - Practical Example : Linear Regression

- Advanced Statistical Methods - Logistic Regression

- Advanced Statistical Methods - Cluster Analysis

- Advanced Statistical Methods - K-Means Clustering

- Advanced Statistical Methods - Other Types of Clustering

- Mathematics

- Deep Learning

- Deep Learning - Introduction to Neural Networks

- Deep Learning - How to Build a Neural Network from Scratch with Numpy

- Deep Learning - TensorFlow 2.0 Introduction

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

- Deep Learning - Overfitting

- Deep Learning - Initializing

- Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

- Deep Learning - Preprocessing

- Deep Learning - Classifying on The MNIST Dataset

- Deep Learning - Business Case Example

- Deep Learning - Conclusion

- Appendix: Deep Learning - TensorFlow 1: Introduction

- Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST

- Appendix: Deep Learning - TensorFlow 1: Business Case

- Software Integration

- Case Study - What's Next in the Course?

- Case Study - Preprocessing the 'Absenteeism_data'

- Case Study - Applying Machine Learning to Create the 'Absenteeism_Module'

- Case Study - Loading the 'Absenteeism_Module'

- Case Study - Analyzing the Predicted Outputs in Tableau

- Case Study - Analyzing the Predicted Outputs in Tableau

- Exercise File

## Course Meta Data

Created By 365 Careers, 365 Careers Team || Last Updated 1/2021 || Resource