# Complete Data Science Bootcamp

**#56**in category Data Science

Learning Experience | 9 |
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Most effective, time-efficient & structured data science bootcamp training will definitely assist you to move towards hot career path in data science.

## About Data Science Bootcamp Program

A 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. Universities have been slow at creating specialized data science programs. Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fits in the complete picture. The Data Science Bootcamp course will definitely assist you to move towards a hot career path in data science, However data science is a multidisciplinary field. It encompasses a wide range of topics which includes:

- Understanding of the data science field and the type of analysis carried out
- Mathematics, Statistics, and Python.
- Applying advanced statistical techniques in Python.
- Data Visualization, Machine Learning, and Deep Learning.

Each of these topics in the Data Science Bootcamp 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, Udemy has created The Data Science Course 2021. *

Also as being Udemy’s first training program it solves the biggest challenge to entering the data science field. In this data science Bootcamp, you will be able to get all the necessary resources in one place.

## Complete Data Science Bootcamp program includes:

- Intro to Data and Data Science
- Mathematics
- Statistics
- Python
- Tableau
- Advanced Statistics
- Machine Learning

## What you will learn from this course?

- The course provides you 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.
- You will 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 Tensor Flow 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 your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

## Syllabus for the Data Science Bootcamp course

The Data Science Bootcamp course consists of the following:

### Part 1: Introduction

**Show more**

The Field of Data Science – Connecting the Data

Science Disciplines

The Field of Data Science – The Benefits of Each

Discipline

The Field of Data Science – Popular Data Science

Techniques

The Field of Data Science – Popular Data Science To

The Field of Data Science – Careers in Data Science

The Field of Data Science – Debunking Common

Misconceptions

### Part 2: Probability

**Show more**

Probability – Bayesian Inference

Probability – Distributions

Probability – Probability in Other Fields

### Part 3: Statistics

**Show more**

Statistics – Inferential Statistics Fundamentals

Statistics – Inferential Statistics: Confidence

Intervals

Statistics – Practical Example: Inferential Statistics

Statistics – Hypothesis Testing

Statistics – Practical Example: Hypothesis Testing

### Part 4: Introduction to Python

**Show more**

Python – Basic Python Syntax

Python – Other Python Operators

Python – Conditional Statements

Python – Python Functions

Python – Sequences

Python – Iterations

Python – Advanced Python Tools

### Part 5: Advanced Statistical Methods in Python

**Show more**

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

### Part 6: Mathematics

### Part 7: Deep Learning

**Show more**

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 – Initialization

Deep Learning – Digging into Gradient Descent and

Learning Rate Schedules

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 Dataset

Appendix: Deep Learning – TensorFlow 1: Business

Case

Software Integration

### Case Study

**Show more**

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

### Appendix – Additional Python Tools

## 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 with the fundamentals and gradually builds up your skills.

## Prerequisites:

- No prior experience is required. You will start with the very basics.
- You’ll need to install Anaconda. You will be showed how to do that step by step.
- Microsoft Excel 2003, 2010, 2013, 2016, or 365.

*Note: Your review matters*

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

- About our policies and review criteria.
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## Description

## About Data Science Bootcamp Program

A 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. Universities have been slow at creating specialized data science programs. Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fits in the complete picture. The Data Science Bootcamp course will definitely assist you to move towards a hot career path in data science, However data science is a multidisciplinary field. It encompasses a wide range of topics which includes:

- Understanding of the data science field and the type of analysis carried out
- Mathematics, Statistics, and Python.
- Applying advanced statistical techniques in Python.
- Data Visualization, Machine Learning, and Deep Learning.

Each of these topics in the Data Science Bootcamp 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, Udemy has created The Data Science Course 2021. *

Also as being Udemy’s first training program it solves the biggest challenge to entering the data science field. In this data science Bootcamp, you will be able to get all the necessary resources in one place.

## Complete Data Science Bootcamp program includes:

- Intro to Data and Data Science
- Mathematics
- Statistics
- Python
- Tableau
- Advanced Statistics
- Machine Learning

## What you will learn from this course?

- The course provides you 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.
- You will 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 Tensor Flow 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 your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

## Syllabus for the Data Science Bootcamp course

The Data Science Bootcamp course consists of the following:

### Part 1: Introduction

**Show more**

The Field of Data Science – Connecting the Data

Science Disciplines

The Field of Data Science – The Benefits of Each

Discipline

The Field of Data Science – Popular Data Science

Techniques

The Field of Data Science – Popular Data Science To

The Field of Data Science – Careers in Data Science

The Field of Data Science – Debunking Common

Misconceptions

### Part 2: Probability

**Show more**

Probability – Bayesian Inference

Probability – Distributions

Probability – Probability in Other Fields

### Part 3: Statistics

**Show more**

Statistics – Inferential Statistics Fundamentals

Statistics – Inferential Statistics: Confidence

Intervals

Statistics – Practical Example: Inferential Statistics

Statistics – Hypothesis Testing

Statistics – Practical Example: Hypothesis Testing

### Part 4: Introduction to Python

**Show more**

Python – Basic Python Syntax

Python – Other Python Operators

Python – Conditional Statements

Python – Python Functions

Python – Sequences

Python – Iterations

Python – Advanced Python Tools

### Part 5: Advanced Statistical Methods in Python

**Show more**

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

### Part 6: Mathematics

### Part 7: Deep Learning

**Show more**

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 – Initialization

Deep Learning – Digging into Gradient Descent and

Learning Rate Schedules

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 Dataset

Appendix: Deep Learning – TensorFlow 1: Business

Case

Software Integration

### Case Study

**Show more**

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

### Appendix – Additional Python Tools

## 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 with the fundamentals and gradually builds up your skills.

## Prerequisites:

- No prior experience is required. You will start with the very basics.
- You’ll need to install Anaconda. You will be showed how to do that step by step.
- Microsoft Excel 2003, 2010, 2013, 2016, or 365.

*Note: Your review matters*

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

## Specification:

- Udemy
- 365 Careers
- Online Course
- Self-paced
- All levels
- 1-4 Weeks
- Paid Course (Paid certificate)
- English
- Python
- Micorsoft Excel Tableau
- None Pre-requisite
- Big data Data Analysis Data Science Data Science with 'Python' Deep learning Machine learning Practical Statistics Probability TensorFlow

There are no reviews yet.