The Data Science Course: Complete Data Science Bootcamp

Start the journey into the world of data science with “The Data Science Course: Complete Data Science Bootcamp.” On, you may access an extensive and captivating video course that will provide you with the information and abilities required to succeed in the rapidly evolving field of data science.

This bootcamp goes deeply into the fundamental ideas and real-world applications of data science with more than 30 hours of painstakingly produced video content. You will obtain a thorough understanding of data analysis, machine learning, statistical modeling, and data visualization through a structured curriculum and practical examples.

Navigate through the course modules with ease as you learn to harness the power of Python for data manipulation, cleaning, and exploration. Build a strong foundation in statistical analysis as you uncover meaningful insights from complex datasets. Dive into the world of machine learning algorithms, mastering the techniques to create predictive models that drive informed decision-making.

Transition seamlessly between topics, from exploratory data analysis to advanced machine learning, guided by expert instructors who simplify complex concepts into digestible insights. Engage with real-world case studies that demonstrate the application of data science in diverse industries, solidifying your grasp on the subject matter.

What You’ll Learn:

  • Enhance Your Resume: Acquire skills in:
  • Statistical analysis
  • Python programming: NumPy, pandas, matplotlib, Seaborn
  • Advanced statistical analysis
  • Tableau usage
  • Machine Learning: stats models, scikit-learn
  • Deep learning: TensorFlow
  • Comprehensive Understanding: Develop a solid grasp of the data science landscape for confident interactions in interviews and discussions.
  • Data Pre-processing Mastery: Learn the art of preparing data, a fundamental yet often overlooked skill.
  • Mathematics Behind Machine Learning: Gain a deep understanding of the mathematical principles driving Machine Learning.
  • Python Proficiency: Initiate coding in Python and use it for robust statistical analysis.
  • Regression Techniques: Apply linear and logistic regressions using Python.
  • Cluster and Factor Analysis: Explore data patterns through cluster and factor analysis.
  • Machine Learning Algorithms: Develop potent Machine Learning algorithms with Python, utilizing NumPy, statsmodels, and scikit-learn.
  • Real-Life Business Applications: Apply acquired skills to real-world business challenges.
  • Deep Learning Advancements: Proficiency in cutting-edge Deep Learning frameworks, including TensorFlow, for deep neural network exploration.
  • Algorithm Optimization: Enhance Machine Learning algorithms through in-depth topics such as underfitting, overfitting, validation, cross-validation, testing, and hyperparameter optimization.
  • Practical Experience: Immerse in coding and real-world big data scenarios, fostering valuable business intuition.
  • Prepare for an enriching data science journey, equipping you with practical skills and profound knowledge. Excel in the dynamic data science realm and apply your expertise to diverse real-life situations, igniting your passion for this ever-evolving field.

Course content of Complete Data Science Bootcamp

Part 1: Introduction

  • The Field of Data Science – The Various Data Science Disciplines
  • 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 Tools
  • The Field of Data Science – Careers 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 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

Part 6: Mathematics

Part 7: 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 – 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: Classifying on the
  • MNIST Dataset
  • Appendix: Deep Learning – TensorFlow 1: Business Case

More lessons

  • 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
  • Appendix – Additional Python Tools
  • Appendix – pandas Fundamentals
  • Appendix – Working with Text Files in Python
  • Bonus Lecture

Requirements of Complete Data Science Bootcamp

  • 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

Who this course is for:

  • If you would like to learn more about the field of data sciences or if you want to become a data scientist, you should take this course.
  • If you want to have a successful career, take this course.
  • The program is great for novices as well because it starts with the basics and gradually develops your skills.

Is this course suitable for beginners in data science?

Absolutely! This course is designed to cater to learners of all levels, providing a strong foundation while gradually progressing to more advanced topics.

For how long will I be able to access the course materials Complete Data Science Bootcamp?

You will have unrestricted access to the course after you join, allowing you to do it at your own speed.

Are the course videos available for download?

Although you cannot download the course videos, you may always access them by signing into your account on

Free today and unlock a world of possibilities in the realm of data-driven insights and innovation.

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Last Update : 8/2023

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