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The Data Science Course: Complete Data Science Bootcamp 2024

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About Course

Learn the skills you need to become a data scientist with this comprehensive online course! This free course covers everything from the basics of data science to advanced machine learning techniques, all taught by experienced instructors.

This course is perfect for anyone who wants to learn data science, whether you’re a complete beginner or have some experience. You’ll learn about:

  • Introduction to Data and Data Science
  • Mathematics for Data Science
  • Statistics for Data Science
  • Python Programming
  • Data Visualization with Tableau
  • Advanced Statistics
  • Machine Learning
  • Deep Learning

This course is completely free and comes with:

  • Active Q&A support
  • A community of data science learners
  • A certificate of completion
  • Access to future updates
  • Real-life business cases

This course is available on top platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more. Sign up today and start your journey to becoming a data scientist!

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What Will You 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

Course Content

01 – Part 1 Introduction

  • A Message from the Professor
  • 001 A Practical Example What You Will Learn in This Course.mp4
    00:00
  • 002 What Does the Course Cover.mp4
    00:00
  • 003 Download All Resources and Important FAQ.html
    00:00
  • external-links.txt
    00:00
  • Section Quiz

02 – The Field of Data Science – The Various Data Science Disciplines

03 – The Field of Data Science – Connecting the Data Science Disciplines

04 – The Field of Data Science – The Benefits of Each Discipline

05 – The Field of Data Science – Popular Data Science Techniques

06 – The Field of Data Science – Popular Data Science Tools

07 – The Field of Data Science – Careers in Data Science

08 – The Field of Data Science – Debunking Common Misconceptions

09 – Part 2 Probability

10 – Probability – Combinatorics

11 – Probability – Bayesian Inference

12 – Probability – Distributions

13 – Probability – Probability in Other Fields

14 – Part 3 Statistics

15 – Statistics – Descriptive Statistics

16 – Statistics – Practical Example Descriptive Statistics

17 – Statistics – Inferential Statistics Fundamentals

18 – Statistics – Inferential Statistics Confidence Intervals

19 – Statistics – Practical Example Inferential Statistics

20 – Statistics – Hypothesis Testing

21 – Statistics – Practical Example Hypothesis Testing

22 – Part 4 Introduction to Python

23 – Python – Variables and Data Types

24 – Python – Basic Python Syntax

25 – Python – Other Python Operators

26 – Python – Conditional Statements

27 – Python – Python Functions

28 – Python – Sequences

29 – Python – Iterations

30 – Python – Advanced Python Tools

31 – Part 5 Advanced Statistical Methods in Python

32 – Advanced Statistical Methods – Linear Regression with StatsModels

33 – Advanced Statistical Methods – Multiple Linear Regression with StatsModels

34 – Advanced Statistical Methods – Linear Regression with sklearn

35 – Advanced Statistical Methods – Practical Example Linear Regression

36 – Advanced Statistical Methods – Logistic Regression

37 – Advanced Statistical Methods – Cluster Analysis

38 – Advanced Statistical Methods – K-Means Clustering

39 – Advanced Statistical Methods – Other Types of Clustering

40 – Part 6 Mathematics

41 – Part 7 Deep Learning

42 – Deep Learning – Introduction to Neural Networks

43 – Deep Learning – How to Build a Neural Network from Scratch with NumPy

44 – Deep Learning – TensorFlow 2.0 Introduction

45 – Deep Learning – Digging Deeper into NNs Introducing Deep Neural Networks

46 – Deep Learning – Overfitting

47 – Deep Learning – Initialization

48 – Deep Learning – Digging into Gradient Descent and Learning Rate Schedules

49 – Deep Learning – Preprocessing

50 – Deep Learning – Classifying on the MNIST Dataset

51 – Deep Learning – Business Case Example

52 – Deep Learning – Conclusion

53 – Appendix Deep Learning – TensorFlow 1 Introduction

54 – Appendix Deep Learning – TensorFlow 1 Classifying on the MNIST Dataset

55 – Appendix Deep Learning – TensorFlow 1 Business Case

56 – Software Integration

57 – Case Study – What’s Next in the Course

58 – Case Study – Preprocessing the ‘Absenteeism_data’

59 – Case Study – Applying Machine Learning to Create the ‘absenteeism_module’

60 – Case Study – Loading the ‘absenteeism_module’

61 – Case Study – Analyzing the Predicted Outputs in Tableau

62 – Appendix – Additional Python Tools

63 – Appendix – pandas Fundamentals

64 – Appendix – Working with Text Files in Python

65 – Bonus Lecture

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Student Ratings & Reviews

4.0
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Hasnain Mehmood
3 weeks ago
Kindly add these two Sections "ChatGpt for Data Science" and "Case Study: Train a Naive Classifier with ChatGpt for sentiment analysis"
ZF
4 months ago
please also share the practice material for this course

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