The Complete Pandas Bootcamp 2022: Data Science with Python Course is the most comprehensive Pandas Bootcamp on the web. It features 34 hours of video content, 150+ exercises, and two real-world projects to put theoretical knowledge into practice.
The objective of the course is to advance the student’s understanding of data handling and manipulation so that they can advance their Data Science career. The course is divided into 5 sections that focus on the basics of Pandas, Pandas for Finance and Investing, Pandas and Scikit-Learn for Machine Learning, etc. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get The Complete Pandas Bootcamp 2022: Data Science with Python Course for INR 499.
Who all can opt for this course?
- Anyone who wishes to enter the field of data science. The key to everything is the Panda.
- Data Scientists looking to develop their data handling and manipulation abilities.
- Anyone who wants to move their data projects from Excel to more powerful tools (for example, in science/research).
- Finance/Investment professionals who reached the limits of Excel.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 33 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 21,561 students |
| Instructor | Alexander Hagmann https://www.linkedin.com/in/alexanderhagmann |
| Topics Covered | Pandas basics, Machine Learning with Pandas and sci-kit-learn, DataFrame basics |
| Course Level | Beginner |
| Total Student Reviews | 2,965 |
Learning Outcomes
- Brush up your Data Handling & Data Analysis skills
- Study and practice Pandas’ workflows and methodologies by using real-world datasets
- Understand and learn Pandas based on the new version 1.x
- Import, clean, and integrate messy data for machine learning
- Learn how to use Pandas, Scikit-Learn, and Seaborn to master a whole machine-learning project from A to Z
- Utilize Seaborn, Pandas, and Matplotlib to analyze, visualize, and comprehend your data
- Utilize quizzes, 150+ exercises, and comprehensive projects to hone your Pandas skills
- Import financial and stock data from the web and use Pandas to analyze it
- Learn the basics of coding with Pandas and Numpy
- Learn and master the important statistical concepts with scipy
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Getting Started (56 minutes) | Overview / Student FAQ |
| Tips: How to get the most out of this course | ||
| Did you know that…? | ||
| More FAQ / Important Information | ||
| Installation of Anaconda | ||
| Opening a Jupyter Notebook | ||
| How to use Jupyter Notebooks | ||
| How to tackle Pandas Version 1.0 | ||
| 2. | —- PART 1: PANDAS FROM ZERO TO HERO (BUILDING BLOCKS) —- (06 minutes) | Intro to Tabular Data / Pandas |
| Download: Part 1 Course Materials | ||
| 3. | Pandas Basics (DataFrame Basics I) (01 hour 56 minutes) | Create your very first Pandas DataFrame (from csv) |
| Pandas Display Options and the methods head() & tail() | ||
| First Data Inspection | ||
| Built-in Functions, Attributes and Methods with Pandas | ||
| Make it easy: TAB Completion and Tooltip | ||
| First Steps | ||
| Explore your own Dataset: Coding Exercise 1 (Intro) | ||
| Explore your own Dataset: Coding Exercise 1 (Solution) | ||
| Selecting Columns | ||
| Selecting one Column with the “dot notation” | ||
| Zero-based Indexing and Negative Indexing | ||
| Selecting Rows with iloc (position-based indexing) | ||
| Slicing Rows and Columns with iloc (position-based indexing) | ||
| Position-based Indexing Cheat Sheets | ||
| Selecting Rows with loc (label-based indexing) | ||
| Slicing Rows and Columns with loc (label-based indexing) | ||
| Label-based Indexing Cheat Sheets | ||
| Indexing and Slicing with reindex() | ||
| Summary, Best Practices and Outlook | ||
| Indexing and Slicing | ||
| Coding Exercise 2 (Intro) | ||
| Coding Exercise 2 (Solution) | ||
| Advanced Indexing and Slicing (optional) | ||
| 4. | Pandas Series and Index Objects (01 hour 50 minutes) | Intro |
| First Steps with Pandas Series | ||
| Analyzing Numerical Series with unique(), nunique() and value_counts() | ||
| Analyzing non-numerical Series with unique(), nunique(), value_counts() | ||
| Creating Pandas Series (Part 1) | ||
| Creating Pandas Series (Part 2) | ||
| Indexing and Slicing Pandas Series | ||
| Sorting of Series and Introduction to the inplace – parameter | ||
| nlargest() and nsmallest() | ||
| idxmin() and idxmax() | ||
| Manipulating Pandas Series | ||
| Pandas Series | ||
| Coding Exercise 3 (Intro) | ||
| Coding Exercise 3 (Solution) | ||
| First Steps with Pandas Index Objects | ||
| Creating Index Objects from Scratch | ||
| Changing Row Index with set_index() and reset_index() | ||
| Changing Column Labels | ||
| Renaming Index & Column Labels with rename() | ||
| Pandas Index objects | ||
| Coding Exercise 4 (Intro) | ||
| Coding Exercise 4 (Solution) | ||
| 5. | DataFrame Basics II (01 hour 17 minutes) | Intro |
| Filtering DataFrames by one Condition | ||
| Filtering DataFrames by many Conditions (AND) | ||
| Filtering DataFrames by many Conditions (OR) | ||
| Advanced Filtering with between(), isin() and ~ | ||
| any() and all() | ||
| Removing Columns | ||
| Removing Rows | ||
| Adding new Columns to a DataFrame | ||
| Creating Columns based on other Columns | ||
| Adding Columns with insert() | ||
| Creating DataFrames from Scratch with pd.DataFrame() | ||
| Adding new Rows (hands-on approach) | ||
| DataFrame Basics II | ||
| Coding Exercise 5 (Intro) | ||
| Coding Exercise 5 (Solution) | ||
| 6. | Manipulating Elements in a DataFrame / Slice +++Important, know the Pitfalls!+++ (48 minutes) | Intro |
| Best Practice (How you should do it) | ||
| Chained Indexing: How you should NOT do it (Part 1) | ||
| Chained Indexing: How you should NOT do it (Part 2) | ||
| View vs. Copy | ||
| Simple Rules what to do when… | ||
| Manipulating DataFrames / Slices | ||
| Coding Exercise 6 (Intro) | ||
| Coding Exercise 6 (Solution) | ||
| 7. | DataFrame Basics III (01 hour 43 minutes) | Intro |
| Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update) | ||
| Ranking DataFrames with rank() | ||
| nunique() and nlargest() / nsmallest() with DataFrames | ||
| Summary Statistics and Accumulations | ||
| The agg() method | ||
| Coding Exercise 7 (Intro) | ||
| Coding Exercise 7 (Solution) | ||
| User-defined Functions with apply(), map() and applymap() | ||
| Hierarchical Indexing (Part 1) | ||
| Hierarchical Indexing (Part 2) | ||
| String Operations (Part 1) | ||
| String Operations (Part 2) | ||
| Coding Exercise 8 (Intro) | ||
| Coding Exercise 8 (Solution) | ||
| 8. | Visualization with Matplotlib (49 minutes) | Intro |
| The plot() method | ||
| Customization of Plots | ||
| Histograms (Part 1) | ||
| Histograms (Part 2) | ||
| Barcharts and Piecharts | ||
| Scatterplots | ||
| Coding Exercise 9 (Intro) | ||
| Coding Exercise 9 (Solution) | ||
| 9. | —- PART 2: FULL DATA WORKFLOW A-Z —- (22 seconds) | Welcome to PART 2: Full Data Workflow A-Z |
| Download: Part 2 Course Materials | ||
| 10. | Importing Data (50 minutes) | Importing csv-files with pd.read_csv |
| Importing messy csv-files with pd.read_csv | ||
| Importing Data from Excel with pd.read_excel() | ||
| Importing messy Data from Excel with pd.read_excel() | ||
| Importing Data from the Web with pd.read_html() | ||
| Coding Exercise 10 | ||
| 11. | Cleaning Data (02 hours 24 minutes) | First Inspection & Handling of inconsistent Data |
| String Operations | ||
| Changing Datatype of Columns with astype() | ||
| Intro NA values / missing values | ||
| Detection of missing Values | ||
| Removing missing values | ||
| Replacing missing values | ||
| Intro Duplicates | ||
| Detection of Duplicates | ||
| Handling / Removing Duplicates | ||
| The ignore_index parameter (NEW in Pandas 1.0) | ||
| Detection of Outliers | ||
| Handling / Removing Outliers | ||
| Categorical Data | ||
| Pandas Version 1.0: New dtypes and pd.NA | ||
| Coding Exercise 11 (Intro) | ||
| Coding Exercise 11 (Solution) | ||
| 12. | Merging, Joining, and Concatenating Data (01 hour 33 minutes) | Intro |
| Adding Rows with append() and pd.concat() (Part 1) | ||
| Adding Rows with pd.concat() (Part 2) | ||
| Arithmetic with Pandas Objects / Data Alignment | ||
| EXCURSUS: Comparing two DataFrames / Identify Differences | ||
| Outer Joins with merge() | ||
| Inner Joins with merge() | ||
| Outer Joins (without Intersection) with merge() | ||
| Left Joins (without Intersection) with merge() | ||
| Right Joins (without Intersection) with merge() | ||
| Left Joins with merge() | ||
| Right Joins with merge() | ||
| Joining on different Column Names / Indexes | ||
| Joining on more than one Column | ||
| pd.merge() and join() | ||
| Coding Exercise 12 | ||
| 13. | GroupBy Operations (01 hour 41 minutes) | Intro |
| Understanding the GroupBy Object | ||
| Splitting with many Keys | ||
| split-apply-combine explained | ||
| split-apply-combine applied | ||
| GroupBy 1 | ||
| Advanced aggregation with agg() | ||
| GroupBy Aggregation with Relabeling (NEW – Pandas Version 0.25) | ||
| Transformation with transform() | ||
| Replacing NA Values by group-specific Values | ||
| Generalizing split-apply-combine with apply() | ||
| Hierarchical Indexing with Groupby | ||
| stack() and unstack() | ||
| GroupBy 2 | ||
| Coding Exercise 13 (Intro) | ||
| Coding Exercise 13 (Solution) | ||
| 14. | Reshaping and Pivoting DataFrames (01 hour 06 minutes) | Intro |
| Transposing Rows and Columns | ||
| Pivoting DataFrames with pivot() | ||
| Limits of pivot() | ||
| pivot_table() | ||
| pd.crosstab() | ||
| melting DataFrames with melt() | ||
| Coding Exercise 14 | ||
| 15. | Data Preparation and Feature Creation (01 hour 34 minutes) | Intro |
| Arithmetic Operations (Part 1) | ||
| Arithmetic Operations (Part 2) | ||
| Transformation/Mapping with map() | ||
| Conditional Transformation | ||
| Discretization and Binning with pd.cut() (Part 1) | ||
| Discretization and Binning with pd.cut() (Part 2) | ||
| Discretization and Binning with pd.qcut() | ||
| Floors and Caps | ||
| Scaling / Standardization | ||
| Creating Dummy Variables | ||
| String Operations | ||
| Coding Exercise 15 | ||
| 16. | Advanced Visualization with Seaborn (40 minutes) | Intro |
| First Steps in Seaborn | ||
| Categorical Plots | ||
| Joint Plots / Regression Plots | ||
| Matrixplots / Heatmaps | ||
| Coding Exercise 16 | ||
| 17. | —- PART 3: COMPREHENSIVE PROJECT CHALLENGES —- (18 seconds) | Intro and Downloads |
| 18. | Data Manipulation and Aggregation Challenge (Olympic Medal Tables) (36 minutes) | Olympic Medal Tables (Instruction & Hints) |
| Olympic Medal Tables (Solution Part 1) | ||
| Olympic Medal Tables (Solution Part 2) | ||
| Olympic Medal Tables (Solution Part 3) | ||
| 19. | Explanatory Data Analysis Challenge (01 hour 09 minutes) | Challenge Introduction and Overview |
| Merging and Concatenating (Solution Part 1) | ||
| Data Cleaning 1 (Solution Part 2) | ||
| Data Cleaning 2 (Solution Part 3) | ||
| The most successful Countries (Solution Part 4) | ||
| Impact of GDP, Population and Politics (Solution Part 5) | ||
| Statistical Analysis and Hypothesis Testing (Solution Part 6) | ||
| Aggregating and Ranking (Solution Part 7) | ||
| Summer Games vs. Winter Games – does Location matter? (Solution Part 8) | ||
| Men vs. Women – do Culture & Religion matter? (Solution Part 9) | ||
| National Sports and Traditions (Solution Part 10) | ||
| 20. | —- PART 4: PANDAS FOR FINANCE, INVESTING & TIME SERIES —- (25 seconds) | Welcome to PART 4: Finance and Investments with Pandas |
| Download: Part 4 Course Materials | ||
| 21. | Time Series Basics (01 hour 26 minutes) | Importing Time Series Data from csv-files |
| Converting strings to datetime objects with pd.to_datetime() | ||
| Initial Analysis / Visualization of Time Series | ||
| Indexing and Slicing Time Series | ||
| Creating a customized DatetimeIndex with pd.date_range() | ||
| More on pd.date_range() | ||
| Downsampling Time Series with resample() (Part 1) | ||
| Downsampling Time Series with resample (Part 2) | ||
| The PeriodIndex object | ||
| Advanced Indexing with reindex() | ||
| 22. | Pandas for Finance and Investing (01 hour 16 minutes) | Intro |
| Getting Ready (Installing required package) | ||
| Importing Stock Price Data from Yahoo Finance (it still works!) | ||
| Initial Inspection and Visualization | ||
| Normalizing Time Series to a Base Value (100) | ||
| The shift() method | ||
| The methods diff() and pct_change() | ||
| Measuring Stock Performance with MEAN Returns and STD of Returns | ||
| Financial Time Series – Return and Risk | ||
| Financial Time Series – Covariance and Correlation | ||
| Helpful DatetimeIndex Attributes and Methods | ||
| Filling NA Values with bfill, ffill and interpolation | ||
| Coding Exercise 17 | ||
| 23. | —- PART 5: MACHINE LEARNING WITH PANDAS AND SCIKIT-LEARN —- (13 seconds) | Overview & Downloads |
| 24. | Introduction to Regression and Classification (38 minutes) | Machine Learning – an Overview |
| Linear Regression with scikit-learn – a simple Introduction | ||
| Making Predictions with Linear Regression | ||
| Overfitting | ||
| Underfitting | ||
| Logistic Regression with scikit-learn – a simple Introduction (Part 1) | ||
| Logistic Regression with scikit-learn – a simple Introduction (Part 2) | ||
| 25. | BONUS: Machine Learning Project A-Z (Regression) (01 hour 00 minutes) | Project Intro |
| Importing the Dataset and first Inspection | ||
| Cleaning the Data and Creating more Features | ||
| Explanatory Data Analysis (Part 1) | ||
| Explanatory Data Analysis (Part 2) | ||
| Feature Engineering (Part 1) | ||
| Feature Engineering (Part 2) | ||
| Splitting the Data into Training Set and Test Set | ||
| Training the Machine Learning Model | ||
| Testing/Evaluating the Model with the Test Set | ||
| Feature Importance | ||
| 26. | +++ WHAT´S NEW IN PANDAS VERSION 1.0? – A HANDS-ON GUIDE +++ (47 minutes) | Intro and Overview |
| How to update Pandas to Version 1.0 | ||
| Downloads for this Section | ||
| Important Recap: Pandas Display Options (Changed in Version 0.25) | ||
| Info() method – new and extended output | ||
| NEW Extension dtypes (“nullable” dtypes): Why do we need them? | ||
| Creating the NEW extension dtypes with convert_dtypes() | ||
| NEW pd.NA value for missing values | ||
| The NEW “nullable” Int64Dtype | ||
| The NEW StringDtype | ||
| The NEW “nullable” BooleanDtype | ||
| Addition of the ignore_index parameter | ||
| Removal of prior Version Deprecations | ||
| 27. | —- APPENDIX: PYTHON BASICS, NUMPY & STATISTICS —- (07 seconds) | Welcome to the Appendix |
| 28. | Python Basics (02 hours 49 minutes) | Downloads |
| Intro | ||
| First Steps | ||
| Variables | ||
| Data Types: Integers and Floats | ||
| Data Types: Strings | ||
| Data Types: Lists (Part 1) | ||
| Data Types: Lists (Part 2) | ||
| Data Types: Tuples | ||
| Data Types: Sets | ||
| Operators & Booleans | ||
| Conditional Statements (if, elif, else, while) | ||
| For Loops | ||
| Key words break, pass, continue | ||
| Generating Random Numbers | ||
| User Defined Functions (Part 1) | ||
| User Defined Functions (Part 2) | ||
| User Defined Functions (Part 3) | ||
| Visualization with Matplotlib | ||
| Python Basics | ||
| Python Basics Quiz: Solution | ||
| 29. | The Numpy Package (01 hour 39 minutes) | Downloads |
| Introduction to Numpy Arrays | ||
| Numpy Arrays: Vectorization | ||
| Numpy Arrays: Indexing and Slicing | ||
| Numpy Arrays: Shape and Dimensions | ||
| Numpy Arrays: Indexing and Slicing of multi-dimensional Arrays | ||
| Numpy Arrays: Boolean Indexing | ||
| Generating Random Numbers | ||
| Performance Issues | ||
| Case Study: Numpy vs. Python Standard Library | ||
| Summary Statistics | ||
| Visualization and (Linear) Regression | ||
| Numpy | ||
| Numpy Quiz: Solution | ||
| 30. | Statistical Concepts (03 hours 02 minutes) | Statistics – Overview, Terms and Vocabulary |
| Downloads for this Section | ||
| Population vs. Sample | ||
| Visualizing Frequency Distributions with plt.hist() | ||
| Relative and Cumulative Frequencies with plt.hist() | ||
| Measures of Central Tendency (Theory) | ||
| Coding Measures of Central Tendency – Mean and Median | ||
| Coding Measures of Central Tendency – Geometric Mean | ||
| Variability around the Central Tendency / Dispersion (Theory) | ||
| Minimum, Maximum and Range with Python/Numpy | ||
| Percentiles with Python/Numpy | ||
| Variance and Standard Deviation with Python/Numpy | ||
| Skew and Kurtosis (Theory) | ||
| How to calculate Skew and Kurtosis with scipy.stats | ||
| How to generate Random Numbers with Numpy | ||
| Reproducibility with np.random.seed() | ||
| Probability Distributions – Overview | ||
| Discrete Uniform Distributions | ||
| Continuous Uniform Distributions | ||
| The Normal Distribution (Theory) | ||
| Creating a normally distributed Random Variable | ||
| Normal Distribution – Probability Density Function (pdf) with scipy.stats | ||
| Normal Distribution – Cumulative Distribution Function (cdf) with scipy.stats | ||
| The Standard Normal Distribution and Z-Values | ||
| Properties of the Standard Normal Distribution (Theory) | ||
| Probabilities and Z-Values with scipy.stats | ||
| Confidence Intervals with scipy.stats | ||
| Covariance and Correlation Coefficient (Theory) | ||
| Cleaning and preparing the Data – Movies Database (Part 1) | ||
| Cleaning and preparing the Data – Movies Database (Part 2) | ||
| How to calculate Covariance and Correlation in Python | ||
| Correlation and Scatterplots – visual Interpretation | ||
| What is Linear Regression? (Theory) | ||
| A simple Linear Regression Model with numpy & Scipy | ||
| How to interpret Intercept and Slope Coefficient | ||
| Case Study (Part 1): The Market Model (Single Factor Model) | ||
| Case Study (Part 2): The Market Model (Single Factor Model) | ||
| 31. | What´s next? (outlook and additional resources) (03 minutes) | Bonus Lecture |
Resources Required
- A computer that has either Windows, Mac, or Linux, and is capable of storing and running Anaconda. The course will walk students through installing the necessary free software.
- A stable and strong internet connection capable of streaming videos.
- Ideally, some fundamental knowledge of spreadsheets and programming (not mandatory, the course guides you through the basics).
Featured Review
Bhanurdra Narayan Mohapatra (5/5): Thanks Alex for such a great course. This is the best course for anyone like to learn Pandas. I can sense that you have brought the do’s and don’t’s from your real life work experience. The course is curated such a way anyone can learn. Anyone wants to learn Machine Learning, this is the starting point. Only thing I would like to some Videos regarding handling of Json, databases, except that it is perfect. Thanks Alex for such great course !!
Pros
- Husain Merchant (5/5): Alexander is an excellent teacher, and his explanations can always be understood.
- Itzel Beltrán Mata (5/5): All the classes are perfect and the teacher is the best.
- Michelle Price (4/5): Excellent approach to explaining in detail each element of the lines of code.
- Ks (5/5): This is one of the best structured and presented courses I ever took in my life.
Cons
- Kevin M. (2.5/5): Your voice is one of the problems but currently on Part 1 you are not explaining why you use some these pandas function but are just showcasing what they do.
- Felice S. (2/5): Video and audio quality should be drastically improved.
- Juan Miguel R. (1.5/5): Nothing personal, but it was too slow in my opinion. A lot of time used to learning something new in every lesson.
- Giorgos M. (1/5): The structure of the content is really bad. Not recommended.
About the Author
Alexander Hagmann is the instructor of the course. He is a Data Scientist, Finance Professional, and Entrepreneur. He offers a total of 15 courses and holds a 4.6 instructor rating and 9,878 reviews on Udemy. He has taught 85,415 students so far.
- Alexander has more than 10 years of experience in the finance and investment industry as a data scientist and finance professional.
- Additionally, he is a top-rated Udemy instructor for data analysis and manipulation using pandas.
- Alexander began his career in the conventional finance industry before advancing gradually into data-driven and AI-driven finance roles.
- He presently develops solutions for Robo Investing and Algorithmic Trading while working on cutting-edge Fintech initiatives.
- His graduates hold positions in some of the biggest and most well-known IT and banking firms worldwide.
- One thing all of Alexander’s courses have in common is their practical, real-world-proven content and principles.
- The emphasis is clearly on understanding concepts and developing abilities rather than memorization.
- Alexander has a Master’s in Finance and has successfully completed all three CFA Exams (he is currently no active member of the CFA Institute).
Comparison Table
| Parameters | The Complete Pandas Bootcamp 2022: Data Science with Python | Python for Financial Analysis and Algorithmic Trading | Manage Finance Data with Python & Pandas: Unique Masterclass |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 34 hours | 16.5 hours | 27 hours |
| Rating | 4.6 /5 | 4.5 /5 | 4.8 /5 |
| Student Enrollments | 21,561 | 113,992 | 8,470 |
| Instructors | Alexander Hagmann | Jose Portilla | Alexander Hagmann |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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