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Data Science Bootcamp: The Complete Course 2023

Comprehensive training in Data Science covers a range of essential topics including Mathematics, Statistics, Python programming, Advanced Statistics in Python, and Machine & Deep Learning.

Created by- 365 Careers, 365 Careers Team

Certainly! Here are the key points of what you’ll learn in the course:

  1. Gain a comprehensive toolbox to become a data scientist.
  2. Develop in-demand data science skills, including:
    • Statistical analysis
    • Python programming with libraries like NumPy, pandas, matplotlib, and Seaborn
    • Advanced statistical analysis
    • Tableau
    • Machine learning using stats models and scikit-learn
    • Deep learning with TensorFlow
  3. Impress interviewers by demonstrating your understanding of the data science field.
  4. Learn data preprocessing techniques for preparing data for analysis.
  5. Understand the mathematical foundations behind machine learning, which is often overlooked in other courses.
  6. Start coding in Python and learn how to utilize it for statistical analysis.
  7. Perform linear and logistic regressions in Python.
  8. Carry out cluster and factor analysis to uncover patterns in data.
  9. Create machine learning algorithms in Python using libraries like NumPy, statsmodels, and scikit-learn.
  10. Apply your skills to real-life business cases to solve practical problems.
  11. Utilize state-of-the-art deep learning frameworks, such as Google’s TensorFlow.
  12. Develop a practical understanding of business concepts and apply them while coding and solving tasks involving big data.
  13. Explore the power of deep neural networks in solving complex problems.
  14. Improve machine learning algorithms by studying concepts such as underfitting, overfitting, training, validation, n-fold cross-validation, testing, and hyperparameter tuning.
  15. Apply your knowledge and skills to real-life situations and enhance your problem-solving abilities.
Data science

Course content-

Part 1: Introduction
The Field of Data Science – Exploring Different Disciplines within Data Science

The Field of Data Science – Bridging the Gap between 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 Tools
The Field of Data Science – Exploring Career Opportunities in Data Science
The Field of Data Science – Debunking Common Misconceptions
Part 2: Probability
Probability – Combinatorics
Probability – Bayesian Inference
Probability – Distributions
Probability – Probability in Other Fields
Part 3: Statistics
Statistics – Descriptive Statistics
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
Part 4: Introduction to Python
Python – Variables and Data Types
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

Advanced Statistical Methods – Linear Regression Analysis using StatsModels and sklearn

Advanced Statistical Methods – Multiple Linear Regression Analysis using StatsModels

Advanced Statistical Methods – Linear Regression Implementation using sklearn

Advanced Statistical Methods – Practical Example: Applying Linear Regression
Advanced Statistical Methods – Logistic Regression
Advanced Statistical Methods – Cluster Analysis
Advanced Statistical Methods – K-Means Clustering
Advanced Statistical Methods – Exploring Other Types of Clustering
Part 6: Mathematics
Part 7: Deep Learning
Deep Learning – Introduction to Neural Networks
Deep Learning – Creating a Neural Network from Scratch using NumPy
Deep Learning – TensorFlow 2.0: Introduction
Deep Learning – Digging Deeper into NNs: Exploring Deep Neural Networks in Deep Learning
Deep Learning – Overfitting
Deep Learning – Initialization
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: Classification on the MNIST Dataset
Appendix: Deep Learning – TensorFlow 1: Application in a Business Case
Software Integration
Case Study – What’s Next in the Course?
Case Study – Preprocessing the ‘Absenteeism_data’
Case Study: Implementing Machine Learning for the ‘absenteeism_module’
Case Study – Loading the ‘absenteeism_module’
Case Study: Exploring Predicted Outputs through Tableau Analysis
Appendix – Additional Python Tools
Appendix – pandas Fundamentals
Appendix : Text File Manipulation in Python
Bonus Lecture


  1. No prior experience is necessary as the course starts from the fundamentals.
  2. Installation of Anaconda is required, and we will guide you through the step-by-step process.
  3. Microsoft Excel version 2003, 2010, 2013, 2016, or 365 is needed for certain exercises and examples.


The Challenge

Data science is a highly sought-after profession in the digital, programming-oriented, and analytical era of this century. The increasing demand for data scientists in the job market is evident. However, the supply of skilled data scientists remains limited. Acquiring the necessary skills to be hired as a data scientist has proven to be challenging.

The available solutions

Traditional universities have been slow to develop specialized data science programs, and the existing programs are often expensive and time-consuming. Many online courses focus on specific topics, making it difficult to see how these skills fit into the bigger picture of data science.

The Solution

Data science encompasses a wide range of topics, including understanding the field and the types of analysis performed, mathematics, statistics, Python programming, advanced statistical techniques in Python, data visualization, machine learning, and deep learning.

To address these challenges and provide the most effective, time-efficient, and structured data science training available online, we have developed The Data Science Course 2023. This comprehensive program is designed to solve the key challenge of having all the necessary resources in one place.

Our focus is on teaching topics that seamlessly build upon and complement each other. By following this course, you will gain a thorough understanding of data science and acquire the skills needed to become a data scientist, all at a fraction of the cost and time investment required by traditional programs.

The Skills:

  1. Introduction to Data and Data Science:

Explore the buzzwords in the field of data science, including big data, business intelligence, business analytics, machine learning, and artificial intelligence. Gain a comprehensive understanding of these concepts and their relevance in the realm of data science.

This introduction will equip you with the knowledge to approach problem-solving in data science effectively.

  1. Mathematics:

Understand the fundamental tools of data science by studying calculus and linear algebra. These subfields are crucial for programming in data science and are essential for comprehending advanced machine learning algorithms.

  1. Statistics:

Develop a scientific mindset and learn to frame problems as hypotheses through statistics. Acquire techniques for testing these hypotheses, enabling you to think like a scientist in your data analysis. This course not only provides the necessary statistical tools but also trains you to apply them effectively.

  1. Python:

Master Python, a powerful and versatile programming language widely used in data science. Python’s capabilities span web applications, computer games, and data science. Gain proficiency in libraries that facilitate data manipulation, transformation, visualization, and its application in machine and deep learning.

  1. Tableau:

Learn Tableau, a leading visualization software in business intelligence and data science. Enhance your ability to present and communicate data insights to non-technical decision-makers, enabling you to convey the data’s story effectively.

  1. Advanced Statistics:

Explore regression, clustering, and factor analysis techniques in depth. These statistical methods, now integrated into machine learning, offer unparalleled accuracy in predictive modeling. Expand your expertise in these methods through the advanced statistics section of the course.

  1. Machine Learning:

Dive into machine learning techniques and deep learning methods using TensorFlow. Master the skills necessary to apply machine and deep learning in your work, differentiating yourself as a data scientist rather than a data analyst.

What You Get:

  • A $1250 data science training program
  • Active Q&A support
  • Comprehensive knowledge to secure a data scientist role
  • Access to a community of data science learners
  • Certificate of completion
  • Future updates to the course content
  • Real-life business case studies to enhance your job prospects
  • Start your journey as a data scientist from scratch

We offer an unconditional 30-day money-back guarantee, ensuring your satisfaction with the course.

Don’t miss out on this opportunity—click the “Buy Now” button and join our data scientist program today.

You can also check other course as well: 2023 ML Mastery: Complete Machine Learning with R Studio

Target Audience:

  • Individuals aspiring to become Data Scientists or seeking to gain knowledge about the field.
  • Those who are looking for a rewarding career opportunity.
  • Beginners who want to start their data science journey from the fundamentals and progressively enhance their skills.

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Frequently Asked Questions (FAQs)

Here are some frequently asked questions (FAQs) about the comprehensive training in Data Science:

Q: What topics are covered in the course?

A: The course covers a wide range of topics including mathematics, statistics, Python programming, advanced statistical analysis in Python, data visualization, machine learning using stats models and scikit-learn, and deep learning with TensorFlow. It also includes an introduction to data science, probability, and Tableau.

Q: What skills will I acquire from this course?

A: By completing this course, you will gain a comprehensive toolbox of data science skills. These skills include statistical analysis, Python programming with libraries like NumPy, pandas, matplotlib, and Seaborn, advanced statistical analysis, Tableau, machine learning using stats models and scikit-learn, and deep learning with TensorFlow. You will also develop a business intuition while coding and solving tasks with big data.

Q: Do I need any prior experience or knowledge to take this course?

A: No prior experience is necessary as the course starts from the fundamentals. However, basic knowledge of Python programming and familiarity with concepts like variables, data types, and conditional statements will be helpful.

Q: Are there any software requirements for the course?

A: To follow along with the course, you will need to have Anaconda installed. The course also includes exercises and examples that require Microsoft Excel version 2003, 2010, 2013, 2016, or 365.

Q: What is the teaching approach of the course?

A: The course is designed to provide a structured and comprehensive learning experience. It follows a specific order of topics to ensure a seamless progression of knowledge. The course includes video lectures, practical examples, exercises, and real-life business case studies to enhance your problem-solving abilities.

Q: Is there any support available during the course?

A: Yes, the course provides active Q&A support, allowing you to ask questions and get assistance from the instructors. Additionally, you will have access to a community of data science learners, which can further enhance your learning experience.

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