The ‘Python Data Analysis & Visualization Bootcamp’ course will teach you Financial Data Analysis and Visualization with Python. This course will take you from the basics of Python to exploring many different types of data.
In this course, you will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more. The course is usually available for INR 2,299 on Udemy but you can click on the link and get the ‘Python Data Analysis & Visualization Bootcamp’ for INR 499.
Who all can opt for this course?
- Data analysts new to Python should enrol in this course
- Data analysts with intermediate Python skills should enrol in this course
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 499 ( |
| Duration | 08 Hours |
| Rating | 4.2/5 |
| Student Enrollment | 8,155 students |
| Instructor | Siranjeevi – Python Data Analysis and Visualization https://www.linkedin.com/in/siranjeevi-pythondataanalysisandvisualization |
| Topics Covered | Data Manipulation, Data Analysis, Data Visualization |
| Course Level | Beginner |
| Total Student Reviews | 3,559 |
Learning Outcomes
- Perform complete data analytics efficiently and effectively
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Setup and Jupyter Environment (Python 3) (34 minutes) | Introduction to the Study Kit |
| #1 Downloading Setup and Installation | ||
| #2 Installing Work Environment – Jupyter Notebook | ||
| #3 Exploring Jupyter Notebook functionalities | ||
| #4 Python Package Index – Using Command line interface and Jupyter Notebook | ||
| 2. | Data Manipulation with Numpy (Python 3) (01 hour 43 minutes) | #1 Getting Started – Numpy Arrays (Numerical Python) |
| #2 Scalar Operations on Numpy Arrays | ||
| #3 Array Indexes – Part 1 | ||
| #4 Array Indexes in Multi-Dimensional Numpy Arrays | ||
| #5 – Premium Array Operations | ||
| #6 Saving And Loading Arrays To External Memory | ||
| #7 Statistical Processing And Sketching Graphs | ||
| #8 Conditional Clauses And Boolean Operation | ||
| 3. | Data Manipulation with Pandas (Python 3) (01 hour 57 minutes) | #1 Getting Started with Series |
| #2 Introduction to DataFrames in Pandas | ||
| #3 Learning to access elements with indexes | ||
| #4 – Re-indexing in pandas Series and Dataframes | ||
| #5 – Dropping values from Series and DataFrames | ||
| #6 – Handling Null or NAN values in pandas | ||
| #7 Selecting and Modifying entries in Pandas | ||
| #8 Coordinate and Regulate data in Series and Dataframes | ||
| #9 – Ranking and Sorting in Series | ||
| #10 Statistical Data Analysis and Graphs in Pandas | ||
| 4. | Starting with File Operations (Python 3) (21 minutes) | #1 File Operations – Dataframes And Csv |
| #2 Import Data From Excel File | ||
| 5. | Data Analysis and Methodologies – Learn to perform Operations on datasets (Py 3) (01 hour 26 minutes) | #1 Pandas – Merging along columns in DataFrames |
| #2 Concatenation of Arrays, Series and Dataframes | ||
| #3 Combining values of a DataFrame or Series | ||
| #4 Reshaping Datasets – Series and Dataframe | ||
| #5 Pivot Tables | ||
| #6 Duplicates Analysis in dataset | ||
| #7 Mapping in DataFrame | ||
| #8 Replace values in Series | ||
| #9 Renaming Indexes in DataFrame | ||
| #10 Observation, Filtering and Basic Analysis | ||
| 6. | Data Visualization (02 hours 02 minutes) | Data Visualization and Introduction to Seaborn Visualization Library |
| Histogram Visualization in seaborn | ||
| Seaborn Kernel Density Estimation (KDE) Plot on Univariates | ||
| Seaborn KDE Plot for multivariates | ||
| Plotting multiple charts with seaborn | ||
| Box Plot Visualization | ||
| Regression Plots with seaborn | ||
| Violin plot Visualization | ||
| Heat Maps Visualization | ||
| Cluster Map Visualization |
Resources Required
- Windows, Linux, or Mac computer using experience
- Basic Mathematical operations
Featured Review
Ezechi Ekeoba (5/5) : Awesome, I learnt how to use Numpy and Pandas in analyzing data. I especially loved the visual/graphical representation. it made it look so simple and easy to understand.
Pros
- Dhruv Samant (4/5) : Best part is, this knowledge of data analytics stays with me forever.
- Rutik Ambre (5/5) : The python code as well as its output was perfectly explained .
- Nikita Mahajan (5/5) : The python code as well as its output was perfectly explained .
- Ayush Aman (5/5) : It is really a awesome experience to learn and do project on the basis of this course.
Cons
- Krutarth Pujara (1/5) : see, you have to understand this, that the course you are providing is on python 2.7 and not on Python 3.6 Audio Quality is the Worst I have ever heard, I would probably won’t even watch a youtube video with such audio.
- Rohan Patel (1/5) : Worst course do not take this it is a complete joke, they will ask for a 5-star rating in order to get the certificate and make you write 500 word long reviews for it.
- Manu Singh (1/5) : The instructor is confused and needs to pause the video to google the answers himself every now and then, the audio quality is bad, not sure if what I am learning is helpful or not will have to complete other similar courses to find out
- Michael Kerenza Gouw (2/5) : Too basic and no problem-solution example provided last assignment is also too hard and the logic is never given a lot of unnecessary review in the end of the course in order to get the certificate.
About the Author
The instructor of this course is Siranjeevi – Python Data Analysis and Visualization who is a Data Scientist Group. With 4.4 Instructor Rating and 3,559 Reviews on Udemy, Instructor offers 1 Course and has taught 8,155 Students so far.
- As he has been a product manager for a while, the author has offered his expertise on how to efficiently analyse data points
- He has experience managing technology goods, visualising data, and managing data analysis
Comparison Table
| Parameters | Python Data Analysis & Visualization Bootcamp | Google Data Studio A-Z: Looker Studio for Data Visualization | Artificial Neural Networks (ANN) with Keras in Python and R |
|---|---|---|---|
| Offers | INR 499 ( | INR 455 ( | INR 455 ( |
| Duration | 8 hours | 4.5 hours | 8.5 hours |
| Rating | 4.2/5 | 4.5 /5 | 4.5 /5 |
| Student Enrollments | 8,153 | 171,360 | 159,666 |
| Instructors | Siranjeevi – Python Data Analysis and Visualization | Start-Tech Academy | Start-Tech Academy |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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