The Data Science Course: Complete Data Science Bootcamp 2024
 | 31hrs 46m total length |
 | 66 sections |
| 520 lectures |
Course Overview
The Data Science Course: Complete Data Science Bootcamp 2024
This course is the ultimate solution for those aspiring to excel in the rapidly expanding field of data science. With the demand for data scientists continuing to surge, this comprehensive program is designed to equip you with all the essential skills needed to become a successful data scientist, even if you’re starting from scratch. You’ll gain expertise Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning.
What you’ll 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 Title
The Data Science Course: Complete Data Science Bootcamp 2024
This course includes:
- 31 hours on-demand video
- 137 coding exercises
- 93 articles
- 541 downloadable resources
- Access on mobile and TV
- Full lifetime access
- Closed captions
- Certificate of completion
Requirements
- 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:
- 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 from the fundamentals and gradually builds up your skills
Course content
66 sections • 520 lectures • 31h 46m total length
1. Part 1: Introduction – 3 lectures 19min
| A practical example: What you will learn in this course | 5:05 |
| What doese the course cover | 3:34 |
| Download All Resources and Important FAQ | 10:42 |
2. The field of data science – The various data science disciplines 5 lectures 31min
| Data science and Business Buzzwords: Why are there so Many? | 5:21 |
| Data Science and Business Buzzwords: Why are there so Many? | 1 question |
| What is the difference between Analysis and Analytics | 3:50 |
| What is the difference between Analysis and Analytics | 1 question |
| Business analytics, data analytics, and data science: An introduction | 8:26 |
| Business Analytics, Data Analytics, and Data Science: An Introduction | 2 questions |
| Continuing with BI, ML, and AI | 9:31 |
| Continuing with BI, ML, and AI | 2 questions |
| A Breakdown of our Data Science Infographic | 4:03 |
| A Breakdown of our Data Science Infographic | 1 question |
3. The field of date science – Connecting the date science disciplines – 1 lecture 7min
| Applying Traditional Data, Big Data, BI, Traditional Data Science and ML | 7:19 |
4. The field of date science – The benefits of each discipline – 1 lecture 5min
| The Reason Behind These Disciplines | 4:44 |
| The Reason Behind These Disciplines | 1 question |
5. The field of data science – Popular data science techniques – 11 lectures 54min
| Techniques for Working with Traditional Data | 8:13 |
| Techniques for Working with Traditional Data | 1 question |
| Real Life Examples of Traditional Data | 1:44 |
| Techniques for Working with Big Data | 4:26 |
| Techniques for Working with Big Data | 1 question |
| Real Life Examples of Big Data | 1:32 |
| Business Intelligence (BI) Techniques | 6:45 |
| Business Intelligence (BI) Techniques | 4 questions |
| Real Life Examples of Business Intelligence (BI) | 1:42 |
| Techniques for Working with Traditional Methods | 9:08 |
| Techniques for Working with Traditional Methods | 4 questions |
| Real Life Examples of Traditional Methods | 2:45 |
| Machine Learning (ML) Techniques | 6:55 |
| Machine Learning (ML) Techniques | 2 questions |
| Types of Machine Learning | 8:13 |
| Types of Machine Learning | 2 questions |
| Real Life Examples of Machine Learning (ML) | 2:11 |
| Real Life Examples of Machine Learning (ML) | 4 questions |
6. The field of data science – Popular data science tools – 1 lecture 6min
| Necessary Programming Languages and Software Used in Data Science | 5:51 |
| Necessary Programming Languages and Software Used in Data Science | 4 questions |
7. The field of data science – Careers in data science – 1 lecture 3min
| Finding the Job – What to Expect and What to Look for | 3:29 |
| Finding the Job – What to Expect and What to Look for | 1 question |
8. The field of data science – Debunking common misconceptions – 1 lecture 4min
| Debunking Common Misconceptions | 4:10 |
| Debunking Common Misconceptions | 1 question |
9. Part 2: Probability – 4 lectures 23min
| The Basic Probability Formula | 7:09 |
| The Basic Probability Formula | 3 questions |
| Computing Expected Values | 5:29 |
| Computing Expected Values | 3 questions |
| Frequency | 5:00 |
| Frequency | 3 questions |
| Events and Their Complements | 5:26 |
| Events and Their Complements | 3 questions |
10. Probability – Combinatorics – 11 lectures 43min
| Fundamentals of Combinatorics | 1:04 |
| Fundamentals of Combinatorics | 1 question |
| Permutations and How to Use Them | 3:21 |
| Permutations and How to Use Them | 2 questions |
| Simple Operations with Factorials | 3:35 |
| Simple Operations with Factorials | 3 questions |
| Solving Variations with Repetition | 2:59 |
| Solving Variations with Repetition | 3 questions |
| Solving Variations without Repetition | 3:48 |
| Solving Variations without Repetition | 3 questions |
| Solving Combinations | 4:51 |
| Solving Combinations | 4 questions |
| Symmetry of Combinations | 3:26 |
| Symmetry of Combinations | 1 question |
| Solving Combinations with Separate Sample Spaces | 2:52 |
| Solving Combinations with Separate Sample Spaces | 1 question |
| Combinatorics in Real-Life: The Lottery | 3:12 |
| Combinatorics in Real-Life: The Lottery | 1 question |
| A Recap of Combinatorics | 2:55 |
| A Practical Example of Combinatorics | 10:53 |
11. Probability – Bayesian inference – 12 lectures 55 min
| Sets and Events | 4:25 |
| Sets and Events | 3 questions |
| Ways Sets Can Interact | 3:45 |
| Ways Sets Can Interact | 2 questions |
| Intersection of Sets | 2:06 |
| Intersection of Sets | 3 questions |
| Union of Sets | 4:51 |
| Union of Sets | 3 questions |
| Mutually Exclusive Sets | 2:09 |
| Mutually Exclusive Sets | 4 questions |
| Dependence and Independence of Sets | 3:01 |
| Dependence and Independence of Sets | 3 questions |
| The Conditional Probability Formula | 4:16 |
| The Conditional Probability Formula | 3 questions |
| The Law of Total Probability | 3:03 |
| The Additive Rule | 2:21 |
| The Additive Rule | 2 questions |
| The Multiplication Law | 4:05 |
| The Multiplication Law | 2 questions |
| Bayes’ Law | 5:44 |
| Bayes’ Law | 2 questions |
| A Practical Example of Bayesian Inference | 14:52 |
12. Probability – Distributions – 15 lectures 1hr 17min
| Fundamentals of Probability Distributions | 6:29 |
| Fundamentals of Probability Distributions | 3 questions |
| Types of Probability Distributions | 7:32 |
| Types of Probability Distributions | 2 questions |
| Characteristics of Discrete Distributions | 2:00 |
| Characteristics of Discrete Distributions | 2 questions |
| Discrete Distributions: The Uniform Distribution | 2:13 |
| Discrete Distributions: The Uniform Distribution | 2 questions |
| Discrete Distributions: The Bernoulli Distribution | 3:26 |
| Discrete Distributions: The Bernoulli Distribution | 1 question |
| Discrete Distributions: The Binomial Distribution | 7:04 |
| Discrete Distributions: The Binomial Distribution | 1 question |
| Discrete Distributions: The Poisson Distribution | 5:27 |
| Discrete Distributions: The Poisson Distribution | 1 question |
| Characteristics of Continuous Distributions | 7:12 |
| Characteristics of Continuous Distributions | 1 question |
| Continuous Distributions: The Normal Distribution | 4:08 |
| Continuous Distributions: The Normal Distribution | 1 question |
| Continuous Distributions: The Standard Normal Distribution | 4:25 |
| Continuous Distributions: The Standard Normal Distribution | 1 question |
| Continuous Distributions: The Students’ T Distribution | 2:29 |
| Continuous Distributions: The Students’ T Distribution | 1 question |
| Continuous Distributions: The Chi-Squared Distribution | 2:22 |
| Continuous Distributions: The Chi-Squared Distribution | 1 question |
| Continuous Distributions: The Exponential Distribution | 3:15 |
| Continuous Distributions: The Exponential Distribution | 1 question |
| Continuous Distributions: The Logistic Distribution | 4:07 |
| Continuous Distributions: The Logistic Distribution | 1 question |
| A Practical Example of Probability Distributions | 15:03 |
13. Probability – Probability in other fields – 3 lectures 19min
| Probability in Finance | 7:46 |
| Probability in Statistics | 6:18 |
| Probability in Data Science | 4:47 |
14. Part 3: Statistics – 1 lecture 4min
| Population and Sample | 4:02 |
| Population and Sample | 2 questions |
15. Statistics – Descriptive statistics – 22 lectures 48min
| Types of Data | 4:33 |
| Types of Data | 2 questions |
| Levels of Measurement | 3:43 |
| Levels of Measurement | 2 questions |
| Categorical Variables – Visualization Techniques Preview | 4:52 |
| Categorical Variables – Visualization Techniques | 1 question |
| Categorical Variables Exercise | 0:03 |
| Numerical Variables – Frequency Distribution Table | 3:09 |
| Numerical Variables – Frequency Distribution Table | 1 question |
| Numerical Variables Exercise | 0:03 |
| The Histogram | 2:14 |
| The Histogram | 1 question |
| Histogram Exercise | 0:03 |
| Cross Tables and Scatter Plots | 4:44 |
| Cross Tables and Scatter Plots | 1 question |
| Cross Tables and Scatter Plots Exercise | 0:03 |
| Mean, median and mode | 4:20 |
| Mean, Median and Mode Exercise | 0:03 |
| Skewness | 2:37 |
| Skewness | 1 question |
| Skewness Exercise | 0:03 |
| Variance | 5:55 |
| Variance Exercise | 0:15 |
| Standard Deviation and Coefficient of Variation | 4:40 |
| Standard Deviation | 1 question |
| Standard Deviation and Coefficient of Variation Exercise | 0:03 |
| Covariance | 3:23 |
| Covariance | 1 question |
| Covariance Exercise | 0:03 |
| Correlation Coefficient | 3:17 |
| Correlation | 2 questions |
| Correlation Coefficient Exercise | 0:03 |
16. Statistics – Practical example: Descriptive statistics – 2 lectures 16min
| Practical Example: Descriptive Statistics | 16:15 |
| Practical Example: Descriptive Statistics Exercise | 0:03 |
17. Statistics – Inferential statistics fundamentals – 8 lectures 22min
| Introduction | 1:00 |
| What is a Distribution | 4:33 |
| What is a Distribution | 1 question |
| The Normal Distribution | 3:54 |
| The Normal Distribution | 1 question |
| The Standard Normal Distribution | 3:30 |
| The Standard Normal Distribution | 1 question |
| The Standard Normal Distribution Exercise | 0:03 |
| Central Limit Theorem | 4:20 |
| Central Limit Theorem | 1 question |
| Standard error | 1:26 |
| Standard Error | 1 question |
| Estimators and Estimates | 3:07 |
| Estimators and Estimates | 1 question |
18. Statistics – Inferential statistics: Confidence intervals – 15 lectures 44min
| What are Confidence Intervals? | 2:41 |
| What are Confidence Intervals? | 1 question |
| Confidence Intervals; Population Variance Known; Z-score | 8:01 |
| Confidence Intervals; Population Variance Known; Z-score; Exercise | 0:03 |
| Confidence Interval Clarifications | 4:38 |
| Student’s T Distribution | 3:22 |
| Student’s T Distribution | 1 question |
| Confidence Intervals; Population Variance Unknown; T-score | 4:36 |
| Confidence Intervals; Population Variance Unknown; T-score; Exercise | 0:03 |
| Margin of Error | 4:52 |
| Margin of Error | 1 question |
| Confidence intervals. Two means. Dependent samples | 6:04 |
| Confidence intervals. Two means. Dependent samples Exercise | 0:03 |
| Confidence intervals. Two means. Independent Samples (Part 1) | 4:31 |
| Confidence intervals. Two means. Independent Samples (Part 1). Exercise | 0:03 |
| Confidence intervals. Two means. Independent Samples (Part 2) | 3:57 |
| Confidence intervals. Two means. Independent Samples (Part 2). Exercise | 0:03 |
| Confidence intervals. Two means. Independent Samples (Part 3) | 1:27 |
19. Statistics – Practical example: Inferential statistics – 2 lectures 10min
| Practical Example: Inferential Statistics | 10:05 |
| Practical Example: Inferential Statistics Exercise | 0:03 |
20. Statistics – Hypothesis testing – 15 lectures 48min
| Null vs Alternative Hypothesis Preview | 5:51 |
| Further Reading on Null and Alternative Hypothesis | 1:16 |
| Null vs Alternative Hypothesis | 2 questions |
| Rejection Region and Significance Level | 7:05 |
| Rejection Region and Significance Level | 2 questions |
| Type I Error and Type II Error | 4:14 |
| Type I Error and Type II Error | 4 questions |
| Test for the Mean. Population Variance Known | 6:34 |
| Test for the Mean. Population Variance Known Exercise | 0:03 |
| p-value | 4:13 |
| p-value | 4 questions |
| Test for the Mean. Population Variance Unknown | 4:48 |
| Test for the Mean. Population Variance Unknown Exercise | 0:03 |
| Test for the Mean. Dependent Samples | 5:18 |
| Test for the Mean. Dependent Samples Exercise | 0:03 |
| Test for the mean. Independent Samples (Part 1) | 4:22 |
| Test for the mean. Independent Samples (Part 1). Exercise | 0:03 |
| Test for the mean. Independent Samples (Part 2) | 4:26 |
| Test for the mean. Independent Samples (Part 2) | 1 question |
| Test for the mean. Independent Samples (Part 2). Exercise | 0:03 |
21. Statistics – Practical example: Hypothesis testing – 2 lectures 7min
| Practical Example: Hypothesis Testing | 7:16 |
| Practical Example: Hypothesis Testing Exercise | 0:03 |
22. Part 4: Introduction to Python – 6 lectures 30min
| Introduction to Programming | 5:03 |
| Introduction to Programming | 2 questions |
| Why Python? | 5:11 |
| Why Python? | 2 questions |
| Why Jupyter? | 3:28 |
| Why Jupyter? | 2 questions |
| Installing Python and Jupyter | 6:49 |
| Understanding Jupyter’s Interface – the Notebook Dashboard | 3:15 |
| Prerequisites for Coding in the Jupyter Notebooks | 6:15 |
| Jupyter’s Interface | 3 questions |
23. Python – Variables and data types – 3 lectures 12min
| Variables | 3:37 |
| Python Variables – Exercise #1 | 1 question |
| Python Variables – Exercise #2 | 1 question |
| Python Variables – Exercise #3 | 1 question |
| Python Variables – Exercise #4 | 1 question |
| Variables | 1 question |
| Numbers and Boolean Values in Python | 3:05 |
| Numbers and Boolean Values – Exercise #1 | 1 question |
| Numbers and Boolean Values – Exercise #2 | 1 question |
| Numbers and Boolean Values – Exercise #3 | 1 question |
| Numbers and Boolean Values – Exercise #4 | 1 question |
| Numbers and Boolean Values – Exercise #5 | 1 question |
| Numbers and Boolean Values in Python | 1 question |
| Python Strings | 5:40 |
| Python Strings – Exercise #1 | 1 question |
| Python Strings – Exercise #2 | 1 question |
| Python Strings – Exercise #3 | 1 question |
| Python Strings – Exercise #4 | 1 question |
| Python Strings – Exercise #5 | 1 question |
| Python Strings | 3 questions |
24. Python – Basic Python Syntax – 7 lectures 11min
| Comparison Operators | 2:10 |
| Comparison Operators – Exercise #1 | 1 question |
| Comparison Operators – Exercise #2 | 1 question |
| Comparison Operators – Exercise #3 | 1 question |
| Comparison Operators – Exercise #4 | 1 question |
| Comparison Operators | 2 questions |
| Logical and Identity Operators | 5:35 |
| Logical and Identity Operators – Exercise #1 | 1 question |
| Logical and Identity Operators – Exercise #2 | 1 question |
| Logical and Identity Operators – Exercise #3 | 1 question |
| Logical and Identity Operators – Exercise #4 | 1 question |
| Logical and Identity Operators – Exercise #5 | 1 question |
| Logical and Identity Operators – Exercise #6 | 1 question |
| Logical and Identity Operators | 2 questions |
25. Python – Other Python operators – 2 lectures 8min
| Comparison Operators | 2:10 |
| Comparison Operators – Exercise #1 | 1 question |
| Comparison Operators – Exercise #2 | 1 question |
| Comparison Operators – Exercise #3 | 1 question |
| Comparison Operators – Exercise #4 | 1 question |
| Comparison Operators | 2 questions |
| Logical and Identity Operators | 5:35 |
| Logical and Identity Operators – Exercise #1 | 1 question |
| Logical and Identity Operators – Exercise #2 | 1 question |
| Logical and Identity Operators – Exercise #3 | 1 question |
| Logical and Identity Operators – Exercise #4 | 1 question |
| Logical and Identity Operators – Exercise #5 | 1 question |
| Logical and Identity Operators – Exercise #6 | 1 question |
| Logical and Identity Operators | 2 questions |
26. Python – Conditional Statements – 4 lectures 14min
| The IF Statement | 3:01 |
| The IF Statement – Exercise #1 | 1 question |
| The IF Statement – Exercise #2 | 1 question |
| The IF Statement | 1 question |
| The ELSE Statement | 2:45 |
| The ELSE Statement – Exercise #1 | 1 question |
| The ELIF Statement | 5:34 |
| The ELIF Statement – Exercise #1 | 1 question |
| The ELIF Statement – Exercise #2 | 1 question |
| A Note on Boolean Values | 2:13 |
| A Note on Boolean Values | 1 question |
27. Python – Python Functions – 7 lectures 19min
| Defining a Function in Python | 2:02 |
| How to Create a Function with a Parameter | 3:49 |
| How to Create a Function with a Parameter – Exercise #1 | 1 question |
| How to Create a Function with a Parameter – Exercise #2 | 1 question |
| Defining a Function in Python – Part II | 2:36 |
| Defining a Function in Python – Exercise #1 | 1 question |
| How to Use a Function within a Function | 1:49 |
| How to Use a Function within a Function – Exercise #1 | 1 question |
| Conditional Statements and Functions | 3:06 |
| Conditional Statements and Functions – Exercise #1 | 1 question |
| Functions Containing a Few Arguments | 1:16 |
| Built-in Functions in Python | 3:56 |
| Built-in Functions in Python – Exercise #1 | 1 question |
| Built-in Functions in Python – Exercise #2 | 1 question |
| Built-in Functions in Python – Exercise #3 | 1 question |
| Built-in Functions in Python – Exercise #4 | 1 question |
| Built-in Functions in Python – Exercise #5 | 1 question |
| Built-in Functions in Python – Exercise #6 | 1 question |
| Built-in Functions in Python – Exercise #7 | 1 question |
| Built-in Functions in Python – Exercise #8 | 1 question |
| Built-in Functions in Python – Exercise #9 | 1 question |
| Python Functions | 2 questions |
28. Python – Sequences – 5 lectures 35min
| Lists | 8:18 |
| Lists – Exercise #1 | 1 question |
| Lists – Exercise #2 | 1 question |
| Lists – Exercise #3 | 1 question |
| Lists – Exercise #4 | 1 question |
| Lists – Exercise #5 | 1 question |
| Lists | 1 question |
| Using Methods | 6:54 |
| Using Methods – Exercise #1 | 1 question |
| Using Methods – Exercise #2 | 1 question |
| Using Methods – Exercise #3 | 1 question |
| Using Methods | 1 question |
| List Slicing | 4:30 |
| List Slicing – Exercise #1 | 1 question |
| List Slicing – Exercise #2 | 1 question |
| List Slicing – Exercise #3 | 1 question |
| List Slicing – Exercise #4 | 1 question |
| List Slicing – Exercise #5 | 1 question |
| List Slicing – Exercise #6 | 1 question |
| List Slicing – Exercise #7 | 1 question |
| Tuples | 6:40 |
| Tuples – Exercise #1 | 1 question |
| Tuples – Exercise #2 | 1 question |
| Tuples – Exercise #3 | 1 question |
| Tuples – Exercise #4 | 1 question |
| Dictionaries | 8:27 |
| Dictionaries – Exercise #1 | 1 question |
| Dictionaries – Exercise #2 | 1 question |
| Dictionaries – Exercise #3 | 1 question |
| Dictionaries – Exercise #4 | 1 question |
| Dictionaries – Exercise #5 | 1 question |
| Dictionaries – Exercise #6 | 1 question |
| Dictionaries | 1 question |
29. Python – Iterations – 6 lectures 33min
| For Loops Preview | 5:40 |
| For Loops – Exercise #1 | 1 question |
| For Loops – Exercise #2 | 1 question |
| For Loops | 1 question |
| While Loops and Incrementing | 5:10 |
| While Loops and Incrementing – Exercise #1 | 1 question |
| Lists with the range() Function | 6:22 |
| Lists with the range() Function – Exercise #1 | 1 question |
| Lists with the range() Function – Exercise #2 | 1 question |
| Lists with the range() Function – Exercise #3 | 1 question |
| Lists with the range() Function | 1 question |
| Conditional Statements and Loops | 6:30 |
| Conditional Statements and Loops – Exercise #1 | 1 question |
| Conditional Statements and Loops – Exercise #2 | 1 question |
| Conditional Statements and Loops – Exercise #3 | 1 question |
| Conditional Statements, Functions, and Loops | 2:27 |
| Conditional Statements, Functions, and Loops – Exercise #1 | 1 question |
| How to Iterate over Dictionaries | 6:21 |
| How to Iterate over Dictionaries – Exercise #1 | 1 question |
| How to Iterate over Dictionaries – Exercise #2 | 1 question |
30. Python – Advanced Python tools – 4 lectures 13min
| Object Oriented Programming | 5:00 |
| Object Oriented Programming | 2 questions |
| Modules and Packages | 1:05 |
| Modules and Packages | 2 questions |
| What is the Standard Library? | 2:47 |
| What is the Standard Library? | 1 question |
| Importing Modules in Python | 4:04 |
| Importing Modules in Python | 2 questions |
31. Part 5: Advanced statistical methods in Python – 1 lecture 1min
| Introduction to Regression Analysis | 1:27 |
| Introduction to Regression Analysis | 1 question |
32. Advanced statistical methods – Linear regression with StatsModels – 11 lectures 41min
| The Linear Regression Model | 5:50 |
| The Linear Regression Model | 2 questions |
| Correlation vs Regression | 1:43 |
| Correlation vs Regression | 1 question |
| Geometrical Representation of the Linear Regression Model | 1:25 |
| Geometrical Representation of the Linear Regression Model | 1 question |
| Python Packages Installation | 4:39 |
| First Regression in Python | 7:11 |
| First Regression in Python Exercise | 0:39 |
| Using Seaborn for Graphs | 1:21 |
| How to Interpret the Regression Table | 5:47 |
| How to Interpret the Regression Table | 3 questions |
| Decomposition of Variability | 3:37 |
| Decomposition of Variability | 1 question |
| What is the OLS? | 3:13 |
| What is the OLS | 1 question |
| R-Squared | 5:30 |
| R-Squared | 2 questions |
33. Advanced statistical methods – Multiple linear regression with StatsModels – 13 lectures 42min
| Multiple Linear Regression | 2:55 |
| Multiple Linear Regression | 1 question |
| Adjusted R-Squared | 6:00 |
| Adjusted R-Squared | 3 questions |
| Multiple Linear Regression Exercise | 0:03 |
| Test for Significance of the Model (F-Test) | 2:01 |
| OLS Assumptions | 2:21 |
| OLS Assumptions | 1 question |
| A1: Linearity | 1:50 |
| A1: Linearity | 2 questions |
| A2: No Endogeneity | 4:09 |
| A2: No Endogeneity | 1 question |
| A3: Normality and Homoscedasticity | 5:47 |
| A4: No Autocorrelation | 3:31 |
| A4: No autocorrelation | 2 questions |
| A5: No Multicollinearity | 3:26 |
| A5: No Multicollinearity | 1 question |
| Dealing with Categorical Data – Dummy Variables | 6:43 |
| Dealing with Categorical Data – Dummy Variables | 0:03 |
| Making Predictions with the Linear Regression | 3:29 |
34. Advanced statistical methods – Linear regression with sklearn – 19 lectures 54min
| What is sklearn and How is it Different from Other Packages | 2:14 |
| How are we Going to Approach this Section? | 1:55 |
| Simple Linear Regression with sklearn Preview | 05:38 |
| Simple Linear Regression with sklearn – A StatsModels-like Summary Table Preview | 04:48 |
| A Note on Normalization | 0:09 |
| Simple Linear Regression with sklearn – Exercise | 0:03 |
| Multiple Linear Regression with sklearn | 3:10 |
| Calculating the Adjusted R-Squared in sklearn | 4:45 |
| Calculating the Adjusted R-Squared in sklearn – Exercise | 0:03 |
| Feature Selection (F-regression) | 4:41 |
| A Note on Calculation of P-values with sklearn | 0:13 |
| Creating a Summary Table with P-values | 2:10 |
| Multiple Linear Regression – Exercise | 0:03 |
| Feature Scaling (Standardization) | 5:38 |
| Feature Selection through Standardization of Weights | 5:22 |
| Predicting with the Standardized Coefficients | 3:53 |
| Feature Scaling (Standardization) – Exercise | 0:03 |
| Underfitting and Overfitting | 2:42 |
| Train – Test Split Explained | 6:54 |
35. Advanced statistical methods – Practical example: Linear regression – 9 lectures 38min
| Practical Example: Linear Regression (Part 1) | 11:59 |
| Practical Example: Linear Regression (Part 2) | 6:12 |
| A Note on Multicollinearity | 0:14 |
| Practical Example: Linear Regression (Part 3) | 3:15 |
| Dummies and Variance Inflation Factor – Exercise | 0:03 |
| Practical Example: Linear Regression (Part 4) | 8:09 |
| Dummy Variables – Exercise | 0:14 |
| Practical Example: Linear Regression (Part 5) | 7:34 |
| Linear Regression – Exercise | 0:16 |
36. Advanced statistical methods – Logistic regression – 16 lectures 41min
| Introduction to Logistic Regression | 1:19 |
| A Simple Example in Python | 4:42 |
| Logistic vs Logit Function | 4:00 |
| Building a Logistic Regression | 2:48 |
| Building a Logistic Regression – Exercise | 0:03 |
| An Invaluable Coding Tip | 2:26 |
| Understanding Logistic Regression Tables | 4:06 |
| Understanding Logistic Regression Tables – Exercise | 0:03 |
| What do the Odds Actually Mean | 4:30 |
| Binary Predictors in a Logistic Regression | 4:32 |
| Binary Predictors in a Logistic Regression – Exercise | 0:03 |
| Calculating the Accuracy of the Model | 3:21 |
| Calculating the Accuracy of the Model | 0:03 |
| Underfitting and Overfitting | 3:43 |
| Testing the Model | 5:05 |
| Testing the Model – Exercise | 0:03 |
37. Advanced statistical methods – Cluster analysis – 4 lecture 14min
| Introduction to Cluster Analysis | 3:41 |
| Some Examples of Clusters | 4:31 |
| Difference between Classification and Clustering | 2:32 |
| Math Prerequisites | 3:19 |
38. Advanced statistical methods – K-Means clustering – 15 lectures 49min
| K-Means Clustering | 4:41 |
| A Simple Example of Clustering | 7:48 |
| A Simple Example of Clustering – Exercise | 0:03 |
| Clustering Categorical Data | 2:50 |
| Clustering Categorical Data – Exercise | 0:03 |
| How to Choose the Number of Clusters | 6:11 |
| How to Choose the Number of Clusters – Exercise | 0:03 |
| Pros and Cons of K-Means Clustering | 3:23 |
| To Standardize or not to Standardize | 4:32 |
| Relationship between Clustering and Regression | 1:31 |
| Market Segmentation with Cluster Analysis (Part 1) | 6:03 |
| Market Segmentation with Cluster Analysis (Part 2) | 6:58 |
| How is Clustering Useful? | 4:47 |
| EXERCISE: Species Segmentation with Cluster Analysis (Part 1) | 0:03 |
| EXERCISE: Species Segmentation with Cluster Analysis (Part 2) | 0:03 |
39. Advanced statistical methods – Other types of clustering – 3 lectures 14min
| Types of Clustering | 3:39 |
| Dendrogram | 5:21 |
| Heatmaps Preview | 04:34 |
40. ChatGPT for Data science – 19 lectures 1hr 5min
| Traditional data science methods and the role of ChatGPT | 5:02 |
| How to install ChatGPT | 1:43 |
| How ChatGPT can boost your productivity | 1:57 |
| Data Preprocessing with ChatGPT | 4:38 |
| First attempt at machine learning with ChatGPT | 4:21 |
| Analyzing a client database with ChatGPT in Python | 3:32 |
| Analyzing a client database with ChatGPT in Python – analyzing top products | 3:32 |
| Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM | 3:59 |
| Exploratory data analysis (EDA) with ChatGPT – histogram and scatter plot | 5:06 |
| Exploratory data analysis (EDA) with ChatGPT – correlation matrix, outlier detec | 5:05 |
| Assignment 1 | 0:44 |
| Hypothesis testing with ChatGPT | 3:54 |
| Marvels comic book database: Intro to Regular Expressions (RegEx) | 1:49 |
| Decoding comic book data: Python Regular Expressions and ChatGPT | 4:08 |
| Assignment 2 | 0:46 |
| Algorithm recommendation: Movie Database Analysis with ChatGPT | 2:48 |
| Algorithm recommendation: recommendation engine for movies with ChatGPT | 4:01 |
| Ethical principles in data and AI utilization | 2:51 |
| Using ChatGPT for ethical considerations | 5:01 |
41. Case study: Train a Naive Bayes Classifier with ChatGPT for sentiment analysis – 12 lectures 47min
| Intro to the Case Study | 2:32 |
| The Naive Bayes Algorithm | 4:10 |
| Tokenization and Vectorization | 5:25 |
| Imbalanced Data Sets | 2:18 |
| Overcome Imbalanced Data in Machine Learning | 3:32 |
| Loading the Dataset and Preprocessing | 2:19 |
| Optimizing User Reviews: Data Preprocessing & EDA | 4:10 |
| Reg Ex for Analyzing Text Review Data | 3:29 |
| Understanding Differences between Multinomial and Bernouilli Naive Bayes | 3:30 |
| Machine Learning with Naïve Bayes (First Attempt) | 5:51 |
| Machine Learning with Naïve Bayes – converting the problem to a binary one | 4:35 |
| Testing the Model on New Data | 4:49 |
42. Part 6: Mathematics – 11 lectures 51min
| What is a Matrix? | 3:37 |
| What is a Matrix? | 6 questions |
| Scalars and Vectors | 2:58 |
| Scalars and Vectors | 5 questions |
| Linear Algebra and Geometry | 3:06 |
| Linear Algebra and Geometry | 3 questions |
| Arrays in Python – A Convenient Way To Represent Matrices | 5:09 |
| What is a Tensor? | 3:00 |
| What is a Tensor? | 2 questions |
| Addition and Subtraction of Matrices | 3:36 |
| Addition and Subtraction of Matrices | 3 questions |
| Errors when Adding Matrices | 2:01 |
| Transpose of a Matrix | 5:13 |
| Dot Product | 3:48 |
| Dot Product of Matrices | 8:23 |
| Why is Linear Algebra Useful? | 10:10 |
43. Part 7: Deep learning – 1 lecture 3min
| What to Expect from this Part? | 3:07 |
44. Deep learning – Introduction to Neural networks – 12 lectures 43min
| Introduction to Neural Networks | 4:09 |
| Introduction to Neural Networks | 1 question |
| Training the Model | 2:54 |
| Training the Model | 3 questions |
| Types of Machine Learning | 3:43 |
| Types of Machine Learning | 4 questions |
| The Linear Model (Linear Algebraic Version) | 3:08 |
| The Linear Model | 2 questions |
| The Linear Model with Multiple Inputs | 2:25 |
| The Linear Model with Multiple Inputs | 2 questions |
| The Linear model with Multiple Inputs and Multiple Outputs | 4:25 |
| The Linear model with Multiple Inputs and Multiple Outputs | 3 questions |
| Graphical Representation of Simple Neural Networks | 1:47 |
| Graphical Representation of Simple Neural Networks | 1 question |
| What is the Objective Function? | 1:27 |
| What is the Objective Function? | 2 questions |
| Common Objective Functions: L2-norm Loss | 2:04 |
| Common Objective Functions: L2-norm Loss | 3 questions |
| Common Objective Functions: Cross-Entropy Loss | 3:55 |
| Common Objective Functions: Cross-Entropy Loss | 4 questions |
| Optimization Algorithm: 1-Parameter Gradient Descent | 6:33 |
| Optimization Algorithm: 1-Parameter Gradient Descent | 4 questions |
| Optimization Algorithm: n-Parameter Gradient Descent | 6:08 |
| Optimization Algorithm: n-Parameter Gradient Descent | 3 questions |
45. Deep learning – How to build a Neural network from scratch with NumPy – 5 lectures 21min
| Basic NN Example (Part 1) | 3:06 |
| Basic NN Example (Part 2) | 4:58 |
| Basic NN Example (Part 3) | 3:25 |
| Basic NN Example (Part 4) | 8:15 |
| Basic NN Example Exercises | 0:51 |
46. Deep learning – TensorFlow 2.0: Introduction – 9 lectures 28min
| How to Install TensorFlow 2.0 | 5:02 |
| TensorFlow Outline and Comparison with Other Libraries | 3:28 |
| TensorFlow 1 vs TensorFlow 2 | 2:32 |
| A Note on TensorFlow 2 Syntax | 0:58 |
| Types of File Formats Supporting TensorFlow | 2:34 |
| Outlining the Model with TensorFlow 2 | 5:48 |
| Interpreting the Result and Extracting the Weights and Bias | 4:09 |
| Customizing a TensorFlow 2 Model | 2:51 |
| Basic NN with TensorFlow: Exercises | 0:47 |
47. Deep learning – Digging deeper into NNs: Introducing deep neural networks – 9 lectures 26min
| What is a Layer? | 1:53 |
| What is a Deep Net? | 2:18 |
| Digging into a Deep Net | 4:58 |
| Non-Linearities and their Purpose | 2:59 |
| Activation Functions | 3:37 |
| Activation Functions: Softmax Activation | 3:24 |
| Backpropagation | 3:12 |
| Backpropagation Picture | 3:02 |
| Backpropagation – A Peek into the Mathematics of Optimization | 0:21 |
48. Deep learning – Overfitting – 6 lectures 20min
| What is Overfitting? | 3:51 |
| Underfitting and Overfitting for Classification | 1:52 |
| What is Validation? | 3:22 |
| Training, Validation, and Test Datasets | 2:30 |
| N-Fold Cross Validation | 3:07 |
| Early Stopping or When to Stop Training | 4:54 |
49. Deep learning – Initialization – 3 lectures 8min
| What is Initialization? | 2:32 |
| Types of Simple Initializations | 2:47 |
| State-of-the-Art Method – (Xavier) Glorot Initialization | 2:45 |
50. Deep learning – Digging into Gradient descent and learning rate schedules – 7 lectures 21min
| Stochastic Gradient Descent | 3:24 |
| Problems with Gradient Descent | 2:02 |
| Momentum | 2:30 |
| Learning Rate Schedules, or How to Choose the Optimal Learning Rate | 4:25 |
| Learning Rate Schedules Visualized | 1:32 |
| Adaptive Learning Rate Schedules (AdaGrad and RMSprop ) | 4:08 |
| Adam (Adaptive Moment Estimation) | 2:39 |
51. Deep learning – Preprocessing – 5 lectures 15min
| Preprocessing Introduction | 2:51 |
| Types of Basic Preprocessing | 1:17 |
| Standardization | 4:31 |
| Preprocessing Categorical Data | 2:15 |
| Binary and One-Hot Encoding | 3:39 |
52. Deep learning – Classifying on the MNIST dataset – 12 lectures 37min
| MNIST: The Dataset | 2:25 |
| MNIST: How to Tackle the MNIST | 2:44 |
| MNIST: Importing the Relevant Packages and Loading the Data | 2:11 |
| MNIST: Preprocess the Data – Create a Validation Set and Scale It | 4:43 |
| MNIST: Preprocess the Data – Scale the Test Data – Exercise | 0:03 |
| MNIST: Preprocess the Data – Shuffle and Batch | 6:30 |
| MNIST: Preprocess the Data – Shuffle and Batch – Exercise | 0:03 |
| MNIST: Outline the Model | 4:54 |
| MNIST: Select the Loss and the Optimizer | 2:05 |
| MNIST: Learning | 5:38 |
| MNIST – Exercises | 1:21 |
| MNIST: Testing the Model | 3:56 |
53. Deep learning – Business case example – 12 lectures 39min
| Business Case: Exploring the Dataset and Identifying Predictors | 7:54 |
| Business Case: Outlining the Solution | 1:31 |
| Business Case: Balancing the Dataset | 3:39 |
| Business Case: Preprocessing the Data | 11:32 |
| Business Case: Preprocessing the Data – Exercise | 0:12 |
| Business Case: Load the Preprocessed Data | 3:23 |
| Business Case: Load the Preprocessed Data – Exercise | 0:03 |
| Business Case: Learning and Interpreting the Result | 4:15 |
| Business Case: Setting an Early Stopping Mechanism | 5:01 |
| Setting an Early Stopping Mechanism – Exercise | 0:08 |
| Business Case: Testing the Model | 1:23 |
| Business Case: Final Exercise | 0:16 |
54. Deep learning – Conclusion – 6 lectures 17min
| Summary on What You’ve Learned | 3:41 |
| What’s Further out there in terms of Machine Learning | 1:47 |
| DeepMind and Deep Learning | 0:21 |
| An overview of CNNs | 4:55 |
| An Overview of RNNs | 2:50 |
| An Overview of non-NN Approaches | 3:52 |
55. Appendix: Deep learning – TensorFlow 1: Introduction – 10 lectures 29min
| READ ME!!!! | 0:21 |
| How to Install TensorFlow 1 | 2:20 |
| A Note on Installing Packages in Anaconda | 1:14 |
| TensorFlow Intro | 3:46 |
| Actual Introduction to TensorFlow | 1:40 |
| Types of File Formats, supporting Tensors | 2:38 |
| Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases | 6:05 |
| Basic NN Example with TF: Loss Function and Gradient Descent | 3:41 |
| Basic NN Example with TF: Model Output | 6:05 |
| Basic NN Example with TF Exercises | 1:01 |
56: Appendix: Deep learning – TensorFlow 1: Classifying on the MNIST dataset – 11 lectures 40min
| MNIST: What is the MNIST Dataset? | 2:26 |
| MNIST: How to Tackle the MNIST | 2:48 |
| MNIST: Relevant Packages | 1:34 |
| MNIST: Model Outline | 6:51 |
| MNIST: Loss and Optimization Algorithm | 2:39 |
| Calculating the Accuracy of the Model | 4:18 |
| MNIST: Batching and Early Stopping | 2:08 |
| MNIST: Learning | 7:35 |
| MNIST: Results and Testing | 6:11 |
| MNIST: Exercises | 1:29 |
| MNIST: Solutions | 1:31 |
57. Appendix: Deep learning – TensorFlow 1: Business case – 12 lectures 51min
| Business Case: Getting Acquainted with the Dataset | 7:55 |
| Business Case: Outlining the Solution | 1:57 |
| The Importance of Working with a Balanced Dataset | 3:39 |
| Business Case: Preprocessing | 11:35 |
| Business Case: Preprocessing Exercise | 0:13 |
| Creating a Data Provider | 6:37 |
| Business Case: Model Outline | 5:34 |
| Business Case: Optimization | 5:10 |
| Business Case: Interpretation | 2:05 |
| Business Case: Testing the Model | 2:04 |
| Business Case: A Comment on the Homework | 3:51 |
| Business Case: Final Exercise | 0:17 |
58. Software integration – 5 lectures 30min
| What are Data, Servers, Clients, Requests, and Responses | 4:43 |
| What are Data, Servers, Clients, Requests, and Responses | 2 questions |
| What are Data Connectivity, APIs, and Endpoints? | 7:05 |
| What are Data Connectivity, APIs, and Endpoints? | 2 questions |
| Taking a Closer Look at APIs | 8:05 |
| Taking a Closer Look at APIs | 2 questions |
| Communication between Software Products through Text Files | 4:20 |
| Communication between Software Products through Text Files | 1 question |
| Software Integration – Explained | 5:25 |
| Software Integration – Explained | 2 questions |
59. Case study – What’s next in the course? 3 lectures 10min
| Game Plan for this Python, SQL, and Tableau Business Exercise | 4:08 |
| The Business Task | 2:48 |
| Introducing the Data Set | 3:18 |
| Introducing the Data Set | 1 question |
60. Case study – Preprocessing the ‘Absenteeism_data’ – 33 lectures 1hr 30min
| What to Expect from the Following Sections? | 1:28 |
| Importing the Absenteeism Data in Python | 3:23 |
| Checking the Content of the Data Set | 5:53 |
| Introduction to Terms with Multiple Meanings | 3:27 |
| What’s Regression Analysis – a Quick Refresher | 1:50 |
| Using a Statistical Approach towards the Solution to the Exercise | 2:17 |
| Dropping a Column from a DataFrame in Python | 6:27 |
| EXERCISE – Dropping a Column from a DataFrame in Python | 0:26 |
| SOLUTION – Dropping a Column from a DataFrame in Python | 0:01 |
| Analyzing the Reasons for Absence | 5:04 |
| Obtaining Dummies from a Single Feature | 8:37 |
| EXERCISE – Obtaining Dummies from a Single Feature | 0:04 |
| SOLUTION – Obtaining Dummies from a Single Feature | 0:00 |
| Dropping a Dummy Variable from the Data Set | 1:32 |
| More on Dummy Variables: A Statistical Perspective | 1:28 |
| Classifying the Various Reasons for Absence | 8:35 |
| Using .concat() in Python | 4:35 |
| EXERCISE – Using .concat() in Python | 0:04 |
| SOLUTION – Using .concat() in Python | 0:01 |
| Reordering Columns in a Pandas DataFrame in Python | 1:43 |
| EXERCISE – Reordering Columns in a Pandas DataFrame in Python | 0:06 |
| SOLUTION – Reordering Columns in a Pandas DataFrame in Python | 0:12 |
| Creating Checkpoints while Coding in Jupyter | 2:52 |
| EXERCISE – Creating Checkpoints while Coding in Jupyter | 0:04 |
| SOLUTION – Creating Checkpoints while Coding in Jupyter | 0:00 |
| Analyzing the Dates from the Initial Data Set | 7:48 |
| Extracting the Month Value from the “Date” Column | 7:00 |
| Extracting the Day of the Week from the “Date” Column | 3:36 |
| EXERCISE – Removing the “Date” Column | 0:37 |
| Analyzing Several “Straightforward” Columns for this Exercise | 3:17 |
| Working on “Education”, “Children”, and “Pets” | 4:38 |
| Final Remarks of this Section | 1:59 |
| A Note on Exporting Your Data as a *.csv File | 0:26 |
61. Case study – Applying machine learning to create the ‘absenteeism_module’ – 16 lectures 1hr 7min
| Exploring the Problem with a Machine Learning Mindset | 3:20 |
| Creating the Targets for the Logistic Regression | 6:32 |
| Selecting the Inputs for the Logistic Regression | 2:41 |
| Standardizing the Data | 3:26 |
| Splitting the Data for Training and Testing | 6:12 |
| Fitting the Model and Assessing its Accuracy | 5:39 |
| Creating a Summary Table with the Coefficients and Intercept | 5:16 |
| Interpreting the Coefficients for Our Problem | 6:14 |
| Standardizing only the Numerical Variables (Creating a Custom Scaler) | 4:12 |
| Interpreting the Coefficients of the Logistic Regression | 5:10 |
| Backward Elimination or How to Simplify Your Model | 4:02 |
| Testing the Model We Created | 4:43 |
| Saving the Model and Preparing it for Deployment | 4:06 |
| ARTICLE – A Note on ‘pickling’ | 1:15 |
| EXERCISE – Saving the Model (and Scaler) | 0:13 |
| Preparing the Deployment of the Model through a Module | 4:04 |
62. Case study – Loading the ‘absenteeism_module’ – 4 lectures 11min
| Are You Sure You’re All Set? | 0:14 |
| Deploying the ‘absenteeism_module’ – Part I | 3:50 |
| Deploying the ‘absenteeism_module’ – Part II | 6:23 |
| Exporting the Obtained Data Set as a *.csv | 0:31 |
63. Case study – Analyzing the Predicted outputs in Tableau – 6 lectures 23min
| EXERCISE – Age vs Probability | 0:14 |
| Analyzing Age vs Probability in Tableau | 8:49 |
| EXERCISE – Reasons vs Probability | 0:15 |
| Analyzing Reasons vs Probability in Tableau | 7:49 |
| EXERCISE – Transportation Expense vs Probability | 0:22 |
| Analyzing Transportation Expense vs Probability in Tableau | 6:00 |
64. Appendix – Additional Python Tools – 6 lectures 40min
| Using the .format() Method | 9:02 |
| Using .format() – Exercise #1 | 1 question |
| Using .format() – Exercise #2 | 1 question |
| Using .format() – Exercise #3 | 1 question |
| Using .format() – Exercise #4 | 1 question |
| Using .format() – Exercise #5 | 1 question |
| Iterating Over Range Objects | 4:17 |
| Introduction to Nested For Loops | 5:59 |
| Triple Nested For Loops | 5:37 |
| Triple Nested For Loops – Exercise #1 | 1 question |
| Triple Nested For Loops – Exercise #2 | 1 question |
| Triple Nested For Loops – Exercise #3 | 1 question |
| Triple Nested For Loops – Exercise #4 | 1 question |
| Triple Nested For Loops – Exercise #5 | 1 question |
| Triple Nested For Loops – Exercise #6 | 1 question |
| Triple Nested For Loops – Exercise #7 | 1 question |
| List Comprehensions | 8:30 |
| List Comprehensions – Exercise #1 | 1 question |
| List Comprehensions – Exercise #2 | 1 question |
| List Comprehensions – Exercise #3 | 1 question |
| List Comprehensions – Exercise #4 | 1 question |
| List Comprehensions – Exercise #5 | 1 question |
| Anonymous (Lambda) Functions | 7:00 |
| Anonymous Functions – Exercise #1 | 1 question |
| Anonymous Functions – Exercise #2 | 1 question |
| Anonymous Functions – Exercise #3 | 1 question |
| Anonymous Functions – Exercise #4 | 1 question |
65. Appendix – Pandas fundamentals – 13 lectures 1hr
| Introduction to pandas Series | 8:33 |
| A Note on Completing the Upcoming Coding Exercises | 1:22 |
| Introduction to pandas Series – Exercise #1 | 1 question |
| Introduction to pandas Series – Exercise #2 | 1 question |
| Introduction to pandas Series – Exercise #3 | 1 question |
| Introduction to pandas Series – Exercise #4 | 1 question |
| Introduction to pandas Series – Exercise #5 | 1 question |
| Introduction to pandas Series – Exercise #6 | 1 question |
| Introduction to pandas Series – Exercise #7 | 1 question |
| Introduction to pandas Series – Exercise #8 | 1 question |
| Introduction to pandas Series – Exercise #9 | 1 question |
| Introduction to pandas Series – Exercise #10 | 1 question |
| Working with Methods in Python – Part I | 4:49 |
| Working with Methods in Python – Part II | 2:32 |
| Working with Methods in Python – Exercise #1 | 1 question |
| Working with Methods in Python – Exercise #2 | 1 question |
| Parameters and Arguments in pandas | 4:09 |
| Parameters and Arguments in pandas – Exercise #1 | 1 question |
| Parameters and Arguments in pandas – Exercise #2 | 1 question |
| Using .unique() and .nunique() | 3:49 |
| Using .sort_values() | 3:58 |
| Introduction to pandas DataFrames – Part I | 4:41 |
| Introduction to pandas DataFrames – Exercise #1 | 1 question |
| Introduction to pandas DataFrames – Exercise #2 | 1 question |
| Introduction to pandas DataFrames – Part II | 5:05 |
| Introduction to pandas DataFrames – Exercise #3 | 1 question |
| Introduction to pandas DataFrames – Exercise #4 | 1 question |
| Introduction to pandas DataFrames – Exercise #5 | 1 question |
| pandas DataFrames – Common Attributes | 4:15 |
| Data Selection in pandas DataFrames | 6:55 |
| pandas DataFrames – Indexing with .iloc[] | 5:56 |
| pandas DataFrames – Indexing with .loc[] | 3:51 |
66. Bonus lecture – 1 lecture 1min
| Bonus Lecture: Next Steps | 1:07 |
Instructor: 365 Careers
365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,900,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.
Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA
2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy
3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla’s Strategy and Marketing
4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook
5) Blockchain for Business
All of our courses are:
– Pre-scripted
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By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.
If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur
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Course Feature
Course Feature
Course Provider: Udemy
UEN: N/A
Course Reference Number: N/A
Mode Of Training: Online
| FULL COURSE FEE | $139.98 |
|---|---|
| Duration | 31hrs 46m |
Available in: English
