Introduction To python

2. Why should you think career as Data
Scientist?
• Data Scientist is the best job of the 21st century -
Harvard Business Review
• Global Data science market to reach $122B in revenue
by 2025 – Frost & Sullivan
• The US alone could face a shortage of 1.4 -1.9 million
Data Analysts by 2019 – Mckinsey
There is a serious shortage of Data Scientists and this is a
major concern for Top MNCs around the world. All this
means the major corporations are ready to pay top dollar
salaries for professionals with the right Data Science
skills.

4. Why Python ???

5. What is Python?
• An all-purpose, general language that works
on multiple platforms
• High level and easy to learn.
• More commonly used for machine learning
and predictive modeling (particularly good for
academics and data scientists)
• Open source and free to learn and use more
commonly by developers.

6. Source: https://www.technotification.com/2018/04/python-
best-programming-language-2018.html

7. Why Is Python So Popular?
• Java
public class Main { public static void
main(String[] args) {
System.out.println("hello world"); } }
• Python
print(‘hello world’)
Minimal setup is another of Python’s perks.

8. Why Python is so Popular?
• The language continued to rank highly on various
lists of the world’s most popular programming
languages.
• Many programmers view Python as a language
with a clean syntax and an expansive library.
• Python’s massive user base has created
something of a positive feedback loop
• In Python’s case, it’s Google, which uses the
programming language in a number of
applications (a corporate sponsor).

11. What do businesses use python for?
• Building “data pipelines”:
•New data is coming in all the time
•Needs to be extracted, transformed and loaded
•Needs to be fast
• Descriptive Analytics
• These skills are in demand.
• Businesses want to know about their historical data.
• They also want to know what is happening right now.
• New marketing opportunities? Save time and money in
current processes?
• Machine learning and data science?
• Can our customers be divided into clusters?
• Can we predict what a customer is likely to buy and make
recommendations?
• Can we detect fraud? Can we predict risk?

12. Working as an analyst/scientist
• You may be familiar with some tools already, depending
where you’ve come from:
• Excel and Office tools
• SPSS, MATLAB
• SQL
• BI and analytics are a bit of a continuous process:
• Cleaning data –missing values? Bad data?
• Reshape data –is the data in the right format?
• Loading –how much is there?
• Find patterns –do these patterns add value?
• Presentation –can you tell a story?

13. Working as an analyst/scientist

14. Data Science with Python
Python Environment
Setup and Essentials
Data Science with
Python
Advanced Data
Science Concepts
Job Readiness
Pro-Degree Program
Contents