The Ultimate Python Bootcamp for Data Science & Machine Learning course is a hands-on training-based program for Data Analysts who want to learn Python, focusing especially on Pandas.
The course not only covers the basics of Pandas but also teaches advanced concepts like working with hierarchical indexing, pivoting, time series analysis, etc. The course is available for INR 799 on Udemy.
Who all can opt for this course?
- Python beginners who are curious to learn about Data Science and Data Analysis
- Aspiring data scientists who want to expand their skill set by learning Python
- Students and professionals
- AI and ML aspirants who want to upgrade their knowledge in Data Preprocessing before applying machine learning algorithms to their projects
- Data Analyst job seekers
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 799 |
| Duration | 15.5 hours |
| Rating | 4.0/5 |
| Student Enrollment | 180,538 students |
| Instructor | Pruthviraja L https://www.linkedin.com/in/pruthvirajal |
| Topics Covered | Pandas Data Structures, Data Indexing, Data Cleaning & Handling, Data Aggregation, etc. |
| Course Level | Intermediate |
| Total Student Reviews | 931 |
Learning Outcomes
- Create a strong foundation in Data Analysis using Python
- Work with Pandas Data Structures: Series, DataFrame, and Index Objects
- Discover hundreds of methods and characteristics across numerous Panda objects
- Learn how to analyze huge and disorganized data sets
- Prepare real-world data fields for AI & ML
- Manipulate data quickly and effectively
- Learn the Pandas basics required to become a Data Analyst
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Getting Started (50 minutes) | Course Introduction |
| How To Get Most Out Of This Course | ||
| Better To Know These Things | ||
| How To Install Python IPython And Jupyter Notebook | ||
| How To Install Anaconda For macOS And Linux Users | ||
| How To Work With The Jupyter Notebook Part-1 | ||
| How To Work With The Jupyter Notebook Part-2 | ||
| 2. | Pandas Building Blocks (19 minutes) | How To Work With The Tabular Data |
| How To Read The Documentation In Pandas | ||
| 3. | Pandas_Data Structures (01 hour 09 minutes) | Theory On Pandas Data Structures |
| How To Construct The Pandas Series | ||
| How To Construct The DataFrame Objects | ||
| How To Construct The Pandas Index Objects | ||
| Practice Part 01 | ||
| Practice Part 01 Solution | ||
| 4. | Data Indexing And Selection (58 minutes) | Theory On Data Indexing And Selection |
| Data Selection In Series Part 1 | ||
| Data Selection In Series Part 2 | ||
| Indexers Loc And Iloc In Series | ||
| Data Selection In DataFrame Part 1 | ||
| Data Selection In DataFrame Part 2 | ||
| Accessing Values Using Loc Iloc And Ix In DataFrame Objects | ||
| Practice Part 02 | ||
| Practice Part 02 Solution | ||
| 5. | Essential Functionalities (02 hours 03 minutes) | Theory On Essential Functionalities |
| How To Reindex Pandas Objects | ||
| How To Drop Entries From An Axis | ||
| Arithmetic And Data Alignment | ||
| Arithmetic Methods With Fill Values | ||
| Broadcasting In Pandas | ||
| Apply And Applymap In Pandas | ||
| How To Sort And Rank In Pandas | ||
| How To Work With The Duplicated Indices | ||
| Summarising And Computing Descriptive Statistics | ||
| Unique Values Value Counts And Membership | ||
| Practice_Part_03 | ||
| Practice_Part_03 Solution | ||
| 6. | Data Handling (01 hour 24 minutes) | Theory On Data Handling |
| How To Read The Csv Files Part – 1 | ||
| How To Read The Csv Files Part – 2 | ||
| How To Read Text Files In Pieces | ||
| How To Export Data In Text Format | ||
| How To Use Python’s Csv Module | ||
| Practice_Part_04 | ||
| Practice_Part_04 Solution | ||
| 7. | Data Cleaning And Preparation (02 hours 54 minutes) | Theory On Data Preprocessing |
| How To Handle Missing Values | ||
| How To Filter The Missing Values | ||
| How To Filter The Missing Values Part 2 | ||
| How To Remove Duplicate Rows And Values | ||
| How To Replace The Non Null Values | ||
| How To Rename The Axis Labels | ||
| How To Descretize And Bin The Data Part – 1 | ||
| How To Filter And Detect The Outliers | ||
| How To Reorder And Select Randomly | ||
| Converting The Categorical Variables Into Dummy Variables | ||
| How To Use ‘map’ Method | ||
| How To Manipulate With Strings | ||
| Using Regular Expressions | ||
| Working With The Vectorized String Functions | ||
| Practice_Part_05 | ||
| Practice_Part_05 Solution | ||
| 8. | Data Wrangling (01 hour 45 minutes) | Theory On Data Wrangling |
| Hierarchical Indexing | ||
| Hierarchical Indexing Reordering And Sorting | ||
| Summary Statistics By Level | ||
| Hierarchical Indexing With DataFrame Columns | ||
| How To Merge The Pandas Objects | ||
| Merging On Row Index | ||
| How To Concatenate Along An Axis | ||
| How To Combine With Overlap | ||
| How To Reshape And Pivot Data In Pandas | ||
| Practice_Part_06 | ||
| Practice_Part_06 Solution | ||
| 9. | Data Grouping And Aggregation (01 hour 03 minutes) | Thoery On Data Groupby And Aggregation |
| Groupby Operation | ||
| How To Iterate Over Groupby Object | ||
| How To Select Columns In Groupby Method | ||
| Grouping Using Dictionaries And Series | ||
| Grouping Using Functions And Index Level | ||
| Data Aggregation | ||
| Practice_Part_07 | ||
| Practice_Part_07 Solution | ||
| 10. | Time Series Analysis (01 hour 40 minutes) | Theory On Time Series Analysis |
| Introduction To Time Series Data Types | ||
| How To Convert Between String And Datetime | ||
| Time Series Basics With Pandas Objects | ||
| Date Ranges Frequencies And Shifting | ||
| Date Ranges Frequencies And Shifting Part – 2 | ||
| Time Zone Handling | ||
| Periods And Period Arithmetic’s | ||
| Practice_Part_08 | ||
| Practice_Part_08 Solution | ||
| 11. | How To Analyse With The Part of Real Life Projects (01 hour 36 minutes) | A Brief Introduction To The Pandas Projects |
| Project_1 Description | ||
| Project_1 Solution Part – 1 | ||
| Project_1 Solution Part – 2 | ||
| Project_2 Description | ||
| Project_2 Solution | ||
| Project_3 Description | ||
| Project_3 Solution Part – 1 | ||
| Project_3 Solution Part – 2 | ||
| Project Assignment |
Resources Required
- Basics of Python programming language
- Experience with Microsoft Excel or equivalent spreadsheet programs at the beginner or intermediate level is preferred but not required.
- Basic knowledge of data type fundamentals (strings, integers, floating points, Booleans, etc) is recommended but not required.
- Knowledge of other programming languages or even just the basics will be helpful.
Featured Review
Kumesh Rana (5/5): Very Good Course for Beginners for those who want to learn Python for Career.
Pros
- Sagar kalbhor (5/5) : I am a mechanical engineer but wanted to do career in data science And that course was simply great.
- LUQMAN MOHAMED ABDI (5/5) : It was such great learning experience and of the best bootcamps for data science and machine learning
- Maryam Soleimani-Alyar (4/5) : Thanks for this great course and I hope that I can finish this course successfully.
- Dhananjai singh (5/5) : Good course for beginners to learn all the basics…Please make one full course in detail on Machine learning models in details…
Cons
- Pooja (1/5): it was a really bad experience waste of time, the audio was bad, the explanations was bad, lectures were boring
- Vikas Sharma (1/5): Instructor audio is so horrible I barely understand what he is trying to say.
- Quentin MOUTY (1/5): The sound is terrible, I can not take that courses like that.
- Graham L LaFebre (1/5): he’s using a laptop microphone with heavy noise reduction that makes it so i can’t understand him at all, this is sub-YouTube tutorial level stuff
About the Author
The instructor of this course is Pruthviraja L. who is a professional educator, software trainer, and author. With a 4.1 instructor rating and 1,756 reviews on Udemy, he offers 3 courses and has taught 238,137 students so far.
- Pruthviraja L. has been training and instructing in technical institutes for over seven years.
- He holds an undergraduate degree in electrical and electronics engineering from University in Belgaum, Karnataka.
- He also holds a certification as a data analyst.
- He received certificates from a number of eLearning institutions, including Tableau, IBM’s Coursera, Intellipaat in Bengaluru, LinkedIn, and Udemy.
- His research articles have been successfully published and presented in numerous National & International journals and conferences.
Comparison Table
| Parameters | Ultimate Python Bootcamp For Data Science & Machine Learning | Data Analytics Real-World Projects in Python | Google Data Studio A-Z: Looker Studio for Data Visualization |
|---|---|---|---|
| Offers | INR 799 | NR 455 ( | INR 455 ( |
| Duration | 15.5 hours | 7 hours | 4.5 hours |
| Rating | 4.0/5 | 4.3 /5 | 4.5 /5 |
| Student Enrollments | 180,538 | 81,514 | 170,954 |
| Instructors | Pruthviraja L | Shan Singh | Start-Tech Academy |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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