Python Pandas for Business Analytics/ Data Science _ Level 1
Learn faster way to analyze your data using Python's mighty Pandas library_ No coding & No Stats background required
Development ,Data Science,Python
Lectures -23
Resources -5
Duration -5 hours
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Course Description
Python is one of the most popular tools for analytics or data science today.
Do you know that in 2013, 21.9% of the developers surveyed said that they had done extensive development work in Python over the past year? By 2022, that number had more than doubled to a whopping 48.1%! This growth is bonkers! And in the world of Python, the Pandas library [which stands for Python for Data Analysis] is really a game-changer when it comes to data importing, filtering, wrangling, manipulating, summarizing, or quickly plotting the data. The major story that emerges is that Pandas’ popularity has doubled in the past four years, with a rise from 12.7% to 25.0%.
This is remarkable: Pandas are now as popular as ALL OF PYTHON WAS IN 2016!
This course will make you a pro in using the mighty Pandas for analytics. So, are you ready to roll up your sleeves to jump into analytics? Do not miss the opportunity to ace the most sought-after & marketable library, join the course NOW!
This course is designed for:
- Beginners willing to enter Analytics/Data Science/Machine Learning.
- Analysts (in any domain) wanting to learn Python.
- Python developers wanting to learn Analytics/Data Science.
- Learners with a passion to explore new areas.
Goals
- Using Google Colaboratory to run Python code on a virtual machine [without needing to install Python].
- Create one or two-dimensional [tabular] data sets in Python Pandas using various methods.
- Import & Export external data sets [various file formats like Text, CSV, Excel, HTML, etc.] using Python Pandas.
- Filter/Slice data based on indices and names, or use some condition to answer some questions from the given data set.
- Visualizing data as per the requirement.
- Clean Data for missing or invalid values in Pandas.
- Explore data to find hidden insights [Typecasting variables, renaming columns, deleting rows/columns, descriptive stats, distribution, Cross tabulation, finding aggregate summaries for different groups & much more].
- Combine multiple data sets [merging/joining or appending similar to various SQL joins and much more].
- Applying your learnings to complete an analytics project.
Prerequisites
- You don't need to have a programming background. You will learn everything you need to know.
- You don't need to have a mathematics/statistics background. You will learn everything you need to know.
- You just need a passion for learning & love for data crunching.
- Basic familiarity with data is a plus.
- Basic familiarity with Python is a plus [For this, you can find free tutorials on my YouTube].

Curriculum
Check out the detailed breakdown of what’s inside the course
About this course
3 Lectures
-
What to expect from the course? 04:00 04:00
-
What NOT to expect from the course? 04:44 04:44
-
How this course is organized? 12:09 12:09
About the Instructor
1 Lectures

Getting Started with Python
5 Lectures

Pandas Data Structures
3 Lectures

Data Import & Export using Pandas
1 Lectures

Indexing/Slicing/Filtering/Sub-Setting the data
1 Lectures

Analyzing The Data
3 Lectures

Combining Data Sets
2 Lectures

Source Code for the Course
1 Lectures

Download Assignments & Solutions
1 Lectures

Free Data Sets & Resources
1 Lectures

Projects to get your hands dirty!
1 Lectures

Instructor Details

DR NISHA ARORA
Trainer, Course Creator & Speaker | ~ 1.8 million learners reached | Empowering Professionals with Practical Skills | Python, Data Analytics, ML, DS, R, ExcelOver the years, I have delivered training sessions in Advanced Python, R, Excel, Data Analysis, Machine Learning, and Statistical Thinking for learners ranging from undergraduate students to working professionals.
My teaching style is focused on simplifying complex concepts through relatable explanations, real-world examples, and interactive learning experiences. I also actively share knowledge through educational content, including video tutorials, blog posts, and professional updates. My focus has been on Python for data analysis, especially using libraries like Pandas, and I’ve created learning resources that support structured as well as self-paced learning. These platforms have helped me engage with a wide learner base and continuously refine my teaching approach based on real learner feedback and evolving trends in the industry.
I have also conducted workshops, masterclasses, and webinars at national and international levels, including sessions for Women in Tech Global, Women in Data Science (WiDS)_ Stanford, National Statistics Institute, Malta, Europe, BIJB Study_New Jersey. My educational content has reached over 1.7 million learners worldwide across platforms. With a strong foundation in mathematics, statistics, and programming, I strive to build confidence in learners and help them apply analytical thinking to practical problems.
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