The Data Science Course: Complete Data Science Bootcamp 2024

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!

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

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002 What Does the Course Cover.mp4

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003 Download All Resources and Important FAQ.html

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external-links.txt

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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

001 Techniques for Working with Traditional Data.mp4

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002 Real Life Examples of Traditional Data.mp4

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003 Techniques for Working with Big Data.mp4

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004 Real Life Examples of Big Data.mp4

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005 Business Intelligence (BI) Techniques.mp4

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006 Real Life Examples of Business Intelligence (BI).mp4

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007 Techniques for Working with Traditional Methods.mp4

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008 Real Life Examples of Traditional Methods.mp4

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009 Machine Learning (ML) Techniques.mp4

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010 Types of Machine Learning.mp4

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011 Real Life Examples of Machine Learning (ML).mp4

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Section Quiz

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

001 Fundamentals of Combinatorics.mp4

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002 Permutations and How to Use Them.mp4

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003 Simple Operations with Factorials.mp4

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004 Solving Variations with Repetition.mp4

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005 Solving Variations without Repetition.mp4

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006 Solving Combinations.mp4

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007 Symmetry of Combinations.mp4

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008 Solving Combinations with Separate Sample Spaces.mp4

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009 Combinatorics in Real-Life The Lottery.mp4

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010 A Recap of Combinatorics.mp4

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011 A Practical Example of Combinatorics.mp4

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Section Quiz

11 – Probability – Bayesian Inference

001 Sets and Events.mp4

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002 Ways Sets Can Interact.mp4

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003 Intersection of Sets.mp4

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004 Union of Sets.mp4

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005 Mutually Exclusive Sets.mp4

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006 Dependence and Independence of Sets.mp4

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007 The Conditional Probability Formula.mp4

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008 The Law of Total Probability.mp4

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009 The Additive Rule.mp4

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010 The Multiplication Law.mp4

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011 Bayes’ Law.mp4

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012 A Practical Example of Bayesian Inference.mp4

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Section Quiz

12 – Probability – Distributions

001 Fundamentals of Probability Distributions.mp4

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002 Types of Probability Distributions.mp4

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003 Characteristics of Discrete Distributions.mp4

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004 Discrete Distributions The Uniform Distribution.mp4

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005 Discrete Distributions The Bernoulli Distribution.mp4

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006 Discrete Distributions The Binomial Distribution.mp4

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007 Discrete Distributions The Poisson Distribution.mp4

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008 Characteristics of Continuous Distributions.mp4

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009 Continuous Distributions The Normal Distribution.mp4

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010 Continuous Distributions The Standard Normal Distribution.mp4

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011 Continuous Distributions The Students’ T Distribution.mp4

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012 Continuous Distributions The Chi-Squared Distribution.mp4

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013 Continuous Distributions The Exponential Distribution.mp4

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014 Continuous Distributions The Logistic Distribution.mp4

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015 A Practical Example of Probability Distributions.mp4

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Section Quiz

13 – Probability – Probability in Other Fields

14 – Part 3 Statistics

15 – Statistics – Descriptive Statistics

001 Types of Data.mp4

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002 Levels of Measurement.mp4

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003 Categorical Variables – Visualization Techniques.mp4

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004 Categorical Variables Exercise.html

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005 Numerical Variables – Frequency Distribution Table.mp4

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006 Numerical Variables Exercise.html

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007 The Histogram.mp4

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008 Histogram Exercise.html

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009 Cross Tables and Scatter Plots.mp4

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010 Cross Tables and Scatter Plots Exercise.html

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011 Mean, median and mode.mp4

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012 Mean, Median and Mode Exercise.html

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013 Skewness.mp4

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014 Skewness Exercise.html

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015 Variance.mp4

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016 Variance Exercise.html

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017 Standard Deviation and Coefficient of Variation.mp4

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018 Standard Deviation and Coefficient of Variation Exercise.html

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019 Covariance.mp4

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020 Covariance Exercise.html

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021 Correlation Coefficient.mp4

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022 Correlation Coefficient Exercise.html

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Section Quiz

16 – Statistics – Practical Example Descriptive Statistics

17 – Statistics – Inferential Statistics Fundamentals

001 Introduction.mp4

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002 What is a Distribution.mp4

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003 The Normal Distribution.mp4

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004 The Standard Normal Distribution.mp4

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005 The Standard Normal Distribution Exercise.html

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006 Central Limit Theorem.mp4

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007 Standard error.mp4

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008 Estimators and Estimates.mp4

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Section Quiz

18 – Statistics – Inferential Statistics Confidence Intervals

001 What are Confidence Intervals.mp4

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002 Confidence Intervals; Population Variance Known; Z-score.mp4

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003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html

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004 Confidence Interval Clarifications.mp4

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005 Student’s T Distribution.mp4

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006 Confidence Intervals; Population Variance Unknown; T-score.mp4

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007 Confidence Intervals; Population Variance Unknown; T-score; Exercise.html

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008 Margin of Error.mp4

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009 Confidence intervals. Two means. Dependent samples.mp4

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010 Confidence intervals. Two means. Dependent samples Exercise.html

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011 Confidence intervals. Two means. Independent Samples (Part 1).mp4

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012 Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html

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013 Confidence intervals. Two means. Independent Samples (Part 2).mp4

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014 Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html

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015 Confidence intervals. Two means. Independent Samples (Part 3).mp4

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Section Quiz

19 – Statistics – Practical Example Inferential Statistics

20 – Statistics – Hypothesis Testing

001 Null vs Alternative Hypothesis.mp4

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002 Further Reading on Null and Alternative Hypothesis.html

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003 Rejection Region and Significance Level.mp4

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004 Type I Error and Type II Error.mp4

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005 Test for the Mean. Population Variance Known.mp4

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006 Test for the Mean. Population Variance Known Exercise.html

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007 p-value.mp4

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008 Test for the Mean. Population Variance Unknown.mp4

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009 Test for the Mean. Population Variance Unknown Exercise.html

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010 Test for the Mean. Dependent Samples.mp4

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011 Test for the Mean. Dependent Samples Exercise.html

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012 Test for the mean. Independent Samples (Part 1).mp4

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013 Test for the mean. Independent Samples (Part 1). Exercise.html

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014 Test for the mean. Independent Samples (Part 2).mp4

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015 Test for the mean. Independent Samples (Part 2). Exercise.html

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Section Quiz

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

001 The Linear Regression Model.mp4

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002 Correlation vs Regression.mp4

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003 Geometrical Representation of the Linear Regression Model_en.mp4

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004 Python Packages Installation.mp4

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005 First Regression in Python.mp4

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006 First Regression in Python Exercise.html

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007 Using Seaborn for Graphs.mp4

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008 How to Interpret the Regression Table.mp4

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009 Decomposition of Variability.mp4

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010 What is the OLS.mp4

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011 R-Squared.mp4

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Section Quiz

33 – Advanced Statistical Methods – Multiple Linear Regression with StatsModels

001 Multiple Linear Regression.mp4

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002 Adjusted R-Squared.mp4

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003 Multiple Linear Regression Exercise.html

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004 Test for Significance of the Model (F-Test).mp4

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005 OLS Assumptions.mp4

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006 A1 Linearity.mp4

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007 A2 No Endogeneity.mp4

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008 A3 Normality and Homoscedasticity.mp4

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009 A4 No Autocorrelation.mp4

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010 A5 No Multicollinearity.mp4

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011 Dealing with Categorical Data – Dummy Variables_en.mp4

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012 Dealing with Categorical Data – Dummy Variables.html

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013 Making Predictions with the Linear Regression_en.mp4

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Section Quiz

34 – Advanced Statistical Methods – Linear Regression with sklearn

001 What is sklearn and How is it Different from Other Packages_en.mp4

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002 How are we Going to Approach this Section.mp4

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003 Simple Linear Regression with sklearn.mp4

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004 Simple Linear Regression with sklearn – A StatsModels-like Summary Table.mp4

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005 A Note on Normalization.html

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006 Simple Linear Regression with sklearn – Exercise.html

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007 Multiple Linear Regression with sklearn.mp4

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008 Calculating the Adjusted R-Squared in sklearn.mp4

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009 Calculating the Adjusted R-Squared in sklearn – Exercise.html

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010 Feature Selection (F-regression).mp4

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011 A Note on Calculation of P-values with sklearn.html

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012 Creating a Summary Table with P-values.mp4

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013 Multiple Linear Regression – Exercise.html

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014 Feature Scaling (Standardization).mp4

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015 Feature Selection through Standardization of Weights.mp4

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016 Predicting with the Standardized Coefficients.mp4

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017 Feature Scaling (Standardization) – Exercise.html

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018 Underfitting and Overfitting.mp4

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019 Train – Test Split Explained.mp4

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Section Quiz

35 – Advanced Statistical Methods – Practical Example Linear Regression

001 Practical Example Linear Regression (Part 1).mp4

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002 Practical Example Linear Regression (Part 2).mp4

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003 A Note on Multicollinearity.html

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004 Practical Example Linear Regression (Part 3).mp4

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005 Dummies and Variance Inflation Factor – Exercise.html

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006 Practical Example Linear Regression (Part 4).mp4

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007 Dummy Variables – Exercise.html

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008 Practical Example Linear Regression (Part 5).mp4

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009 Linear Regression – Exercise.html

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external-links.txt

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Section Quiz

36 – Advanced Statistical Methods – Logistic Regression

001 Introduction to Logistic Regression.mp4

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002 A Simple Example in Python.mp4

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003 Logistic vs Logit Function.mp4

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004 Building a Logistic Regression.mp4

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005 Building a Logistic Regression – Exercise.html

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006 An Invaluable Coding Tip.mp4

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007 Understanding Logistic Regression Tables.mp4

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008 Understanding Logistic Regression Tables – Exercise.html

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009 What do the Odds Actually Mean.mp4

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010 Binary Predictors in a Logistic Regression.mp4

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011 Binary Predictors in a Logistic Regression – Exercise.html

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012 Calculating the Accuracy of the Model.mp4

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013 Calculating the Accuracy of the Model.html

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014 Underfitting and Overfitting.mp4

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015 Testing the Model.mp4

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016 Testing the Model – Exercise.html

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Section Quiz

37 – Advanced Statistical Methods – Cluster Analysis

38 – Advanced Statistical Methods – K-Means Clustering

001 K-Means Clustering.mp4

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002 A Simple Example of Clustering.mp4

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003 A Simple Example of Clustering – Exercise.html

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004 Clustering Categorical Data.mp4

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005 Clustering Categorical Data – Exercise.html

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006 How to Choose the Number of Clusters.mp4

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007 How to Choose the Number of Clusters – Exercise.html

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008 Pros and Cons of K-Means Clustering.mp4

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009 To Standardize or not to Standardize.mp4

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010 Relationship between Clustering and Regression.mp4

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011 Market Segmentation with Cluster Analysis (Part 1).mp4

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012 Market Segmentation with Cluster Analysis (Part 2).mp4

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013 How is Clustering Useful.mp4

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014 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html

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015 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html

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Section Quiz

39 – Advanced Statistical Methods – Other Types of Clustering

40 – Part 6 Mathematics

001 What is a Matrix.mp4

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002 Scalars and Vectors.mp4

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003 Linear Algebra and Geometry.mp4

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004 Arrays in Python – A Convenient Way To Represent Matrices.mp4

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005 What is a Tensor.mp4

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006 Addition and Subtraction of Matrices.mp4

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007 Errors when Adding Matrices.mp4

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008 Transpose of a Matrix.mp4

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009 Dot Product.mp4

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010 Dot Product of Matrices.mp4

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011 Why is Linear Algebra Useful.mp4

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Section Quiz

41 – Part 7 Deep Learning

42 – Deep Learning – Introduction to Neural Networks

001 Introduction to Neural Networks.mp4

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002 Training the Model.mp4

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003 Types of Machine Learning.mp4

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004 The Linear Model (Linear Algebraic Version).mp4

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005 The Linear Model with Multiple Inputs.mp4

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006 The Linear model with Multiple Inputs and Multiple Outputs.mp4

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007 Graphical Representation of Simple Neural Networks.mp4

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008 What is the Objective Function.mp4

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009 Common Objective Functions L2-norm Loss.mp4

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010 Common Objective Functions Cross-Entropy Loss.mp4

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011 Optimization Algorithm 1-Parameter Gradient Descent.mp4

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012 Optimization Algorithm n-Parameter Gradient Descent.mp4

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Section Quiz

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

44 – Deep Learning – TensorFlow 2.0 Introduction

001 How to Install TensorFlow 2.0.mp4

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002 TensorFlow Outline and Comparison with Other Libraries.mp4

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003 TensorFlow 1 vs TensorFlow 2.mp4

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004 A Note on TensorFlow 2 Syntax.mp4

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005 Types of File Formats Supporting TensorFlow.mp4

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006 Outlining the Model with TensorFlow 2.mp4

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007 Interpreting the Result and Extracting the Weights and Bias.mp4

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008 Customizing a TensorFlow 2 Model.mp4

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009 Basic NN with TensorFlow Exercises.html

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Section Quiz

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

001 What is a Layer.mp4

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002 What is a Deep Net.mp4

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003 Digging into a Deep Net.mp4

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004 Non-Linearities and their Purpose.mp4

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005 Activation Functions.mp4

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006 Activation Functions Softmax Activation.mp4

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007 Backpropagation.mp4

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008 Backpropagation Picture.mp4

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009 Backpropagation – A Peek into the Mathematics of Optimization.html

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Section Quiz

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

001 MNIST The Dataset.mp4

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002 MNIST How to Tackle the MNIST.mp4

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003 MNIST Importing the Relevant Packages and Loading the Data.mp4

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004 MNIST Preprocess the Data – Create a Validation Set and Scale It.mp4

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005 MNIST Preprocess the Data – Scale the Test Data – Exercise.html

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006 MNIST Preprocess the Data – Shuffle and Batch.mp4

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007 MNIST Preprocess the Data – Shuffle and Batch – Exercise.html

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008 MNIST Outline the Model.mp4

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009 MNIST Select the Loss and the Optimizer.mp4

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010 MNIST Learning.mp4

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011 MNIST – Exercises.html

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012 MNIST Testing the Model.mp4

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Section Quiz

51 – Deep Learning – Business Case Example

001 Business Case Exploring the Dataset and Identifying Predictors.mp4

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002 Business Case Outlining the Solution.mp4

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003 Business Case Balancing the Dataset.mp4

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004 Business Case Preprocessing the Data.mp4

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005 Business Case Preprocessing the Data – Exercise.html

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006 Business Case Load the Preprocessed Data.mp4

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007 Business Case Load the Preprocessed Data – Exercise.html

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008 Business Case Learning and Interpreting the Result.mp4

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009 Business Case Setting an Early Stopping Mechanism.mp4

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010 Setting an Early Stopping Mechanism – Exercise.html

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011 Business Case Testing the Model.mp4

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012 Business Case Final Exercise.html

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Section Quiz

52 – Deep Learning – Conclusion

53 – Appendix Deep Learning – TensorFlow 1 Introduction

001 READ ME!!!!.html

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002 How to Install TensorFlow 1.mp4

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003 A Note on Installing Packages in Anaconda.html

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004 TensorFlow Intro.mp4

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005 Actual Introduction to TensorFlow.mp4

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006 Types of File Formats, supporting Tensors.mp4

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007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4

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008 Basic NN Example with TF Loss Function and Gradient Descent.mp4

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009 Basic NN Example with TF Model Output.mp4

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010 Basic NN Example with TF Exercises.html

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Section Quiz

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

001 MNIST What is the MNIST Dataset.mp4

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002 MNIST How to Tackle the MNIST.mp4

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003 MNIST Relevant Packages.mp4

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004 MNIST Model Outline.mp4

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005 MNIST Loss and Optimization Algorithm.mp4

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006 Calculating the Accuracy of the Model.mp4

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007 MNIST Batching and Early Stopping.mp4

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008 MNIST Learning.mp4

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009 MNIST Results and Testing.mp4

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010 MNIST Exercises.html

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011 MNIST Solutions.html

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Section Quiz

55 – Appendix Deep Learning – TensorFlow 1 Business Case

001 Business Case Getting Acquainted with the Dataset.mp4

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002 Business Case Outlining the Solution.mp4

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003 The Importance of Working with a Balanced Dataset.mp4

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004 Business Case Preprocessing.mp4

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005 Business Case Preprocessing Exercise.html

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006 Creating a Data Provider.mp4

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007 Business Case Model Outline.mp4

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008 Business Case Optimization.mp4

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009 Business Case Interpretation.mp4

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010 Business Case Testing the Model.mp4

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011 Business Case A Comment on the Homework.mp4

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012 Business Case Final Exercise.html

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Section Quiz

56 – Software Integration

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

58 – Case Study – Preprocessing the ‘Absenteeism_data’

001 What to Expect from the Following Sections.html

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002 Importing the Absenteeism Data in Python.mp4

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003 Checking the Content of the Data Set.mp4

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004 Introduction to Terms with Multiple Meanings.mp4

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005 What’s Regression Analysis – a Quick Refresher.html

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006 Using a Statistical Approach towards the Solution to the Exercise.mp4

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007 Dropping a Column from a DataFrame in Python.mp4

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008 EXERCISE – Dropping a Column from a DataFrame in Python.html

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009 SOLUTION – Dropping a Column from a DataFrame in Python.html

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010 Analyzing the Reasons for Absence.mp4

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011 Obtaining Dummies from a Single Feature.mp4

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012 EXERCISE – Obtaining Dummies from a Single Feature.html

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013 SOLUTION – Obtaining Dummies from a Single Feature.html

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014 Dropping a Dummy Variable from the Data Set.html

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015 More on Dummy Variables A Statistical Perspective.mp4

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016 Classifying the Various Reasons for Absence.mp4

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017 Using .concat() in Python.mp4

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018 EXERCISE – Using .concat() in Python.html

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019 SOLUTION – Using .concat() in Python.html

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020 Reordering Columns in a Pandas DataFrame in Python.mp4

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021 EXERCISE – Reordering Columns in a Pandas DataFrame in Python.html

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022 SOLUTION – Reordering Columns in a Pandas DataFrame in Python.html

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023 Creating Checkpoints while Coding in Jupyter.mp4

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024 EXERCISE – Creating Checkpoints while Coding in Jupyter.html

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025 SOLUTION – Creating Checkpoints while Coding in Jupyter.html

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026 Analyzing the Dates from the Initial Data Set.mp4

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027 Extracting the Month Value from the Date Column.mp4

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028 Extracting the Day of the Week from the Date Column.mp4

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029 EXERCISE – Removing the Date Column.html

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030 Analyzing Several Straightforward Columns for this Exercise.mp4

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031 Working on Education, Children, and Pets.mp4

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032 Final Remarks of this Section.mp4

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033 A Note on Exporting Your Data as a .csv File.html

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Section Quiz

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

001 Exploring the Problem with a Machine Learning Mindset_en.mp4

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002 Creating the Targets for the Logistic Regression_en.mp4

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003 Selecting the Inputs for the Logistic Regression_en.mp4

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004 Standardizing the Data.mp4

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005 Splitting the Data for Training and Testing.mp4

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006 Fitting the Model and Assessing its Accuracy.mp4

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007 Creating a Summary Table with the Coefficients and Intercept_en.mp4

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008 Interpreting the Coefficients for Our Problem.mp4

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009 Standardizing only the Numerical Variables (Creating a Custom Scaler)_en.mp4

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010 Interpreting the Coefficients of the Logistic Regression_en.mp4

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011 Backward Elimination or How to Simplify Your Model_en.mp4

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012 Testing the Model We Created_en.mp4

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013 Saving the Model and Preparing it for Deployment_en.mp4

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014 ARTICLE – A Note on ‘pickling’.html

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015 EXERCISE – Saving the Model (and Scaler).html

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016 Preparing the Deployment of the Model through a Module_en.mp4

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external-links.txt

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Section Quiz

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

001 Introduction to pandas Series.mp4

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002 Working with Methods in Python – Part I.mp4

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003 Working with Methods in Python – Part II.mp4

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004 Parameters and Arguments in pandas.mp4

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005 Using .unique() and .nunique().mp4

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006 Using .sort_values().mp4

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007 Introduction to pandas DataFrames – Part I.mp4

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008 Introduction to pandas DataFrames – Part II.mp4

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009 pandas DataFrames – Common Attributes.mp4

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010 Data Selection in pandas DataFrames.mp4

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011 pandas DataFrames – Indexing with .iloc().mp4

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012 pandas DataFrames – Indexing with .loc().mp4

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Section Quiz

64 – Appendix – Working with Text Files in Python

001 An Introduction to Working with Files in Python.mp4

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002 File vs File Object, Reading vs Parsing Data.mp4

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003 Structured, Semi-Structured and Unstructured Data.mp4

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004 Text Files and Data Connectivity.mp4

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005 Importing Data in Python – Principles.mp4

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006 Plain Text Files, Flat Files and More.mp4

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007 Text Files of Fixed Width.mp4

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008 Common Naming Conventions.mp4

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009 Importing Text Files – open().mp4

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010 Importing Text Files – with open().mp4

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011 Importing .csv Files – Part I.mp4

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012 Importing .csv Files – Part II.mp4

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013 Importing .csv Files – Part III.mp4

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014 Importing Data with index_col.mp4

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015 Importing Data with .loadtxt() and .genfromtxt().mp4

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016 Importing Data – Partial Cleaning While Importing Data.mp4

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017 Importing Data with NumPy – Exercise.html

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018 Importing Data from .json Files.mp4

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019 An Introduction to Working with Excel Files in Python.mp4

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020 Working with Excel (.xlsx) Data.mp4

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021 Importing Data in Python – an Important Exercise.mp4

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022 Importing Data with the .squeeze() Method.mp4

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023 Importing Files in Jupyter.mp4

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024 Saving Your Data with pandas.mp4

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025 Saving Your Data with NumPy – Part I – .npy.mp4

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026 Saving Your Data with NumPy – Part II – .npz.mp4

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027 Saving Your Data with NumPy – Part III – .csv.mp4

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028 Saving Data with Numpy – Exercise.html

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029 Working with Text Files in Python – Conclusion.mp4

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external-links.txt

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Section Quiz

65 – Bonus Lecture

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Student Ratings & Reviews

4.0

Total 2 Ratings

1 Rating

0 Rating

1 Rating

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Hasnain Mehmood

3 months ago

Kindly add these two Sections "ChatGpt for Data Science" and "Case Study: Train a Naive Classifier with ChatGpt for sentiment analysis"

zoha faiz

6 months ago

please also share the practice material for this course

About Course

What Will You Learn?

Course Content

01 – Part 1 Introduction

A Message from the Professor

001 A Practical Example What You Will Learn in This Course.mp4

002 What Does the Course Cover.mp4

003 Download All Resources and Important FAQ.html

external-links.txt

Section Quiz

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

001 Data Science and Business Buzzwords Why are there so Many.mp4

002 What is the difference between Analysis and Analytics.mp4

003 Business Analytics, Data Analytics, and Data Science An Introduction.mp4

004 Continuing with BI, ML, and AI.mp4

005 A Breakdown of our Data Science Infographic.mp4

Section Quiz

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

001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML_en.mp4

Section Quiz

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

001 The Reason Behind These Disciplines.mp4

Section Quiz

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

001 Techniques for Working with Traditional Data.mp4

002 Real Life Examples of Traditional Data.mp4

003 Techniques for Working with Big Data.mp4

004 Real Life Examples of Big Data.mp4

005 Business Intelligence (BI) Techniques.mp4

006 Real Life Examples of Business Intelligence (BI).mp4

007 Techniques for Working with Traditional Methods.mp4

008 Real Life Examples of Traditional Methods.mp4

009 Machine Learning (ML) Techniques.mp4

010 Types of Machine Learning.mp4

011 Real Life Examples of Machine Learning (ML).mp4

Section Quiz

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

001 Necessary Programming Languages and Software Used in Data Science_en.mp4

Section Quiz

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

001 Finding the Job – What to Expect and What to Look for.mp4

Section Quiz

08 – The Field of Data Science – Debunking Common Misconceptions

001 Debunking Common Misconceptions.mp4

Section Quiz

09 – Part 2 Probability

001 The Basic Probability Formula.mp4

002 Computing Expected Values.mp4

003 Frequency.mp4

004 Events and Their Complements.mp4

Section Quiz

10 – Probability – Combinatorics

001 Fundamentals of Combinatorics.mp4

002 Permutations and How to Use Them.mp4

003 Simple Operations with Factorials.mp4

004 Solving Variations with Repetition.mp4

005 Solving Variations without Repetition.mp4

006 Solving Combinations.mp4

007 Symmetry of Combinations.mp4

008 Solving Combinations with Separate Sample Spaces.mp4

009 Combinatorics in Real-Life The Lottery.mp4

010 A Recap of Combinatorics.mp4

011 A Practical Example of Combinatorics.mp4

Section Quiz

11 – Probability – Bayesian Inference

001 Sets and Events.mp4

002 Ways Sets Can Interact.mp4

003 Intersection of Sets.mp4

004 Union of Sets.mp4

005 Mutually Exclusive Sets.mp4

006 Dependence and Independence of Sets.mp4

007 The Conditional Probability Formula.mp4

008 The Law of Total Probability.mp4

009 The Additive Rule.mp4

010 The Multiplication Law.mp4

011 Bayes’ Law.mp4

012 A Practical Example of Bayesian Inference.mp4

Section Quiz

12 – Probability – Distributions

001 Fundamentals of Probability Distributions.mp4