Python & Introduction to Data Science Course is a training program that begins with the fundamentals of Python, the most significant programming language in the current Data Science world. It then walks students through important Python libraries like Numpy, Pandas, and Matplotlib.
The instructor uses practical and effective case studies and examples to help students understand the course better. Updates are made frequently to the course’s content and examples. The courses are usually available at INR 3,299 on Udemy but you can click now to get 87% off and get Python & Introduction to Data Science Course for INR 499.
Who all can opt for this course?
- Researchers who seek to consolidate the fundamentals in the fields of data analysis, machine learning, and data mining
- Programming aspirants looking to get started learning Python
- Python language learners who have prior programming knowledge in other languages
- Any college student who wants to work in the field of data science
- Anyone who wishes to enter this new field for employment or professional development
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 09 Hours |
| Rating | 4.5/5 |
| Student Enrollment | 45,752 students |
| Instructor | AI 4 MY https://www.linkedin.com/in/ai4my |
| Topics Covered | Python, NumPy, Pandas |
| Course Level | Beginner |
| Total Student Reviews | 1,099 |
Learning Outcomes
- Basic Notebook commands
- Python variables and conversions
- Python classes, dictionaries, lists, sets, and variables
- Definition of a function
- Date management
- File reading and writing
- Numpy’s mathematical functions
- Functions to generate random data
- Indexing strategies
- Pandas pivot tables
- RAM memory optimization for large amounts of data
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (02 minutes) | 01 Python & Introduction to Data Science |
| 2. | Python (03 hours 48 minutes) | 2.01 Configuration of the development environment |
| 2.02 How to install Python libraries | ||
| 2.03 Basic Notebook Controls | ||
| 2.04 Introduction to Python | ||
| 2.05 Operations in Python | ||
| 2.06 Variables and conversions in Python | ||
| 2.07 Strings and functions of modifications | ||
| 2.08 Python’s Lists | ||
| 2.09 Functions with lists | ||
| 2.10 Dictionaries in Python | ||
| 2.11 Functions with dictionaries | ||
| 2.12 Set in Python | ||
| 2.13 Assignment mechanism in Python | ||
| 2.14 Conditional instructions in Python | ||
| 2.15 Python iteration instructions | ||
| 2.16 Creating functions in Python | ||
| 2.17 Scripts and modules in Python | ||
| 2.18 Error handling in Python | ||
| 2.19 Reading and writing files in Python | ||
| 2.20 Classes in Python | ||
| 2.21 Inheritance of classes in Python | ||
| 2.22 Time management functions | ||
| 2.23 Practical exercises with Python (1) | ||
| 2.24 Practical exercises with Python (2) | ||
| 2.25 Practical exercises with Python (3) | ||
| 2.26 Practical exercises with Python (4) | ||
| 2.27 Practical exercises with Python (5) | ||
| 2.28 Practical exercises with Python (6) | ||
| 3. | Numpy (01 hour 59 minutes) | 3.01 Introduction to Numpy |
| 3.02 Arrays in Numpy | ||
| 3.03 Indexing of matrices in Numpy | ||
| 3.04 Copy, arange and random in Numpy | ||
| 3.05 Data type and conversion to Numpy | ||
| 3.06 Mathematical Functions in Numpy | ||
| 3.07 Order functions in Numpy | ||
| 3.08 Data management functions in Numpy | ||
| 3.09 Functions to create arrays in Numpy (1) | ||
| 3.10 Functions to create arrays in Numpy (2) | ||
| 3.11 Logical operations in Numpy | ||
| 3.12 Random in Numpy | ||
| 3.13 Reading files in Numpy | ||
| 3.14 Writing files in Numpy | ||
| 3.15 Practical exercises with Numpy (1) | ||
| 3.16 Practical exercises with Numpy (2) | ||
| 3.17 Practical exercises with Numpy (3) | ||
| 3.18 Practical exercises with Numpy (4) | ||
| 3.19 Practical exercises with Numpy (5) | ||
| 3.20 Practical exercises with Numpy (6) | ||
| 3.21 Practical exercises with Numpy (7) | ||
| 4. | Pandas (03 hours 18 minutes) | 4.01 Introduction to Pandas |
| 4.02 DataFrame and Series in Pandas | ||
| 4.03 Indexing methods in Pandas | ||
| 4.04 Groupby in Pandas | ||
| 4.05 Mathematical Operations in Pandas | ||
| 4.06 Indexing and editing of a Series data | ||
| 4.07 Indexing, editing and deletion of a DataFrame | ||
| 4.08 Merge in DataFrame | ||
| 4.09 Display options in Pandas | ||
| 4.10 Pivot chart in Pandas | ||
| 4.11 Managing dates in Pandas (1) | ||
| 4.12 Managing dates in Pandas (2) | ||
| 4.13 Processing data in Pandas (1) | ||
| 4.14 Processing of data in Pandas (2) | ||
| 4.15 Methods for editing strings in Pandas | ||
| 4.16 Advanced indexing methods in Pandas | ||
| 4.17 Create graphs in Pandas | ||
| 4.18 Memory management for large data |
Resources Required
- The course does not require any previous programming knowledge
- A computer with a stable internet connection
- Although the course runs on Windows 10, it can also be taken on other operating systems
Featured Review
Miguel (5/5): One of the best courses on these topics that I’ve taken till now. Clear and simple, also short but on point!
Pros
- Mateus Rizzardi (5/5): This is great for beginners! Very easy to follow and is taught at a great pace throughout!
- Sourav Kedia (5/5): Yes, it is a good match for me……Teaching me from the scratch.
- Nour Bou Nasr (5/5): Very good and high-quality stuff and the teacher is awesome
- Omar Ouahid (5/5): The course covers everything about the python language with an amazing explanation
Cons
- Christian D. (3.5/5): The course, generally was okay. I just thought links to the CSV files used should have been made available. Thanks a lot.
- Vamsi K. (3/5): The installation process for Linux users is not clearly mentioned. I recommend adding these to the video for getting better knowledge for Linux users.
- Karthick G. (2/5): Anaconda installation not clearly explained
- Lukasz (1/5): Windows oriented. Not useful for linux. Spending too much time on the obvious while skipping important pieces of info.
About the Author
AI 4 MY are the instructors of this course. It is an AI Educational Company. With a 4.5 instructor rating and 1,099 reviews on Udemy, they offer only this course and have taught 45,752 students so far.
- AI 4 MY is a cutting-edge educational business specializing in data mining and new technologies.
- Their main objective is to provide students with useful and repeatable material using straightforward language that everyone can understand, from novices to the most seasoned IT professionals.
Comparison Table
| Parameters | Python & Introduction to Data Science | Pandas with Python Tutorial | Python & Cryptocurrency API: Build 5 Real-World Applications |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 9 hours | 6 hours | 3 hours |
| Rating | 4.5/5 | 4.1/5 | 4.2/5 |
| Student Enrollments | 45,752 | 40,803 | 94,688 |
| Instructors | AI 4 MY | EDUCBA Bridging the Gap | Ian Annase |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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