3. Dr. Nisha Arora is a proficient educator, passionate
trainer, You Tuber, occasional writer, and a learner
forever.
✓ PhD in Mathematics.
✓ Works in the area of Data Science, Statistical
Research, Data Visualization & Storytelling
✓ Creator of various courses
✓ Contributor to various research communities and
Q/A forums
✓ Mentor for women in Tech Global
4
About Me
An educator by heart & a
trainer by profession.
4. ✓ In-depth Technical Answers
✓ Coding Queries
✓ Layman’s Term Explanation
✓ Research Queries
✓ Blog Posts
✓ Slide Decks
✓ Publications
✓ Tutorials /My Talks
✓ Mentoring
✓ Articles & Much More
My Contribution to the Community
5. Data Science Job Roles
✓ Product Owner
✓ Data Engineer
✓ Data Analysts
✓ Data Scientist/ ML Engineer
✓ Visualization Expert/ Storyteller
✓ Software Engineer
✓ Domain Expert
✓ Operations Team
6. Required Technical Skills
Data Analysts Data Scientists Data Engineer
Excel
Statistical Analysis Python/ R
Databases Data Viz & Storytelling
SQL/ Databases
R/Python/SAS EDA/Data Wrangling/Data Prep Data Warehousing/Data Architecture
Tableau/PowerBI Machine Learning/Deep Learning
Distributed Computing (Hadoop/Map
Reduce)
Statistical Analysis SQL Data Management & Data Security
Data Viz & Storytelling R, Python, SAS (sometimes)
Docker, Kubernaties, Kafka, Spark, Git
GitHub
EDA/Data Wrangling/Data Prep Tensorflow, Keras Cloud Tech (AWS/Azure/GCP)
Dashboards & Reporting Git/ GitHub Big Data Services
11
Soft Skills
Domain Knowledge
10. Python for Data Science
15
Coding Best
Practices
Tips & Tricks
✓ Programming Basics
✓ Data Science/ML Concepts
✓ Libraries for Data Science/ML
✓ Projects
11. Agenda
1. Getting started with Google Colab for
Python
2. Python Basics
3. Data Structure in Python
4. Numpy, Pandas, Matplotlib, Seaborn, Scikit-
learn
5. Data Analysis & Processing in Pandas
6. Data Visualization & Storytelling with Python
Bonus for Python Beginners
1. Downloading & Installing Python
2. Selecting the IDE
3. Python files (.py files) Vs ipython
notebooks (.ipynb files)
4. Getting started with Jupyter &
Jupyter Labs
5. Setting-up start-up folder in Jupyter
6. Getting started with Spyder
DR NISHA ARORA
12. What will you get?
DR NISHA ARORA
✓ Presentations
✓ Quick Summary of each lesson
✓ Code Files
✓ Data Files
✓ Use-cases
✓ Web-resources
✓ Recommended Readings
✓ Tips & Tricks
✓ Bonus Videos
13.
14. Data Types in Python
Numbers – int, float, complex
Strings
Boolean
Object
DR NISHA ARORA
21. Strings
Strings in Python are immutable, iterable and ordered.
which means
- we can not updates or delete values from a string
- we can iterate over elements of a string
- we can perform indexing & slicing over a string
DR NISHA ARORA
22. Escape Sequence
Escape character ``
Escaping allows the normal interpretation of the character to be suspended and that string to be
defined as normal.
DR NISHA ARORA
24. String Slicing
1. Indexing/counting starts with 0. First character gets counted as zero
2. `[a:b]` means starting at ath position and extracting until (b-1)th position
3. `[a:]` means starting at ath position till end
4. `[:a]` means starting at the beginning until (a-1)th position
DR NISHA ARORA
25. String Slicing
5. negative sign with indices mean counting from right instead of natural left.
6. `[:-a]` means starting at the beginning and going up til ath position from end. Counting from end also starts
with 0.
7. `[-a:]` means starting at (a+1)th character from the end and going up till last character.
8. `[a:-b]` means starting at ath character at the beginning and going till bth position from end
9. `[-a:-b]` means starting at (a+1)th character from the end and going up till bth position from end
DR NISHA ARORA
26. String Slicing _ stride
The optional, third number in a slice specifies the stride.
If omitted, the default is 1: return every character in the requested range
To return every kth letter, set the stride to k.
Negative values of k reverse the string.
DR NISHA ARORA
