1
00:00:00,090 --> 00:00:02,130
So why Python for analytics?

2
00:00:02,310 --> 00:00:05,220
There are many compelling reasons to add Python to your toolkit.

3
00:00:05,250 --> 00:00:06,060
Here are a few.

4
00:00:06,450 --> 00:00:07,920
First, scalability.

5
00:00:08,430 --> 00:00:12,000
Unlike most analytics tools or self-service business intelligence platforms.

6
00:00:12,030 --> 00:00:15,030
Python is open source, free to use and built for scale.

7
00:00:15,660 --> 00:00:17,130
What do I mean by built for scale?

8
00:00:17,190 --> 00:00:22,770
Well, first, because Python is free, it's easy to rapidly scale across your team or organization.

9
00:00:23,160 --> 00:00:26,570
We don't need to negotiate for budgeting licenses or contracts.

10
00:00:26,580 --> 00:00:30,390
All we need to do is install it on our local machine and get analyzing.

11
00:00:31,050 --> 00:00:36,300
Second, there are no hard limits on the number of rows or columns that can be processed by Python.

12
00:00:36,660 --> 00:00:40,080
This makes it a great alternative to other tools that might run into their own limits.

13
00:00:41,230 --> 00:00:42,850
Second versatility.

14
00:00:43,210 --> 00:00:48,040
With powerful libraries and frameworks, Python can add value at every stage of the analytics workflow,

15
00:00:48,040 --> 00:00:51,310
from data, prep and analysis to machine learning and visualization.

16
00:00:51,670 --> 00:00:54,640
We'll take a look at some of the most important libraries in Python shortly.

17
00:00:56,000 --> 00:00:57,860
The Python community is amazing.

18
00:00:58,340 --> 00:01:01,970
It's one of the largest and most active programming communities in the world, which means you'll have

19
00:01:01,970 --> 00:01:06,920
access to forums where you can share resources, get help, offer support and connect to other users.

20
00:01:07,310 --> 00:01:11,960
I've gotten a lot of great ideas by engaging with the Python community and I'm excited for you to join

21
00:01:11,960 --> 00:01:12,500
it as well.

22
00:01:13,040 --> 00:01:13,760
Automation.

23
00:01:14,600 --> 00:01:18,890
This is perhaps one of the most underrated aspects of Python as a tool in the Analyst Toolkit.

24
00:01:19,550 --> 00:01:24,800
Python can act as a glue between disparate tools and systems, which allows us to automate complex tasks

25
00:01:24,800 --> 00:01:29,360
and workflows out of the box without the need for complicated integrations or custom plug ins.

26
00:01:29,720 --> 00:01:32,450
One example of this would be school to excel.

27
00:01:32,540 --> 00:01:37,130
A traditional workflow might look something like Go to our Ask Your Workbench, write a bunch of ask

28
00:01:37,130 --> 00:01:37,970
you all queries.

29
00:01:38,210 --> 00:01:39,440
Get the table we need.

30
00:01:39,710 --> 00:01:45,510
Export that to a flat file imported into Excel and hope that Excel interprets all of the columns correctly.

31
00:01:45,830 --> 00:01:50,420
You might spend a bit of time formatting our data correctly before we begin analyzing our data.

32
00:01:50,870 --> 00:01:56,510
With Python, we can connect directly to a database, pass through SQL queries, retrieve the data we

33
00:01:56,510 --> 00:02:01,820
need, write that out immediately to excel while making sure that the formatting is correct along the

34
00:02:01,820 --> 00:02:02,150
way.

35
00:02:02,810 --> 00:02:07,640
Automating your workflows like this will supercharger efficiency as an analyst and is one of the reasons

36
00:02:07,640 --> 00:02:10,699
why there's so much demand for Python.

37
00:02:11,180 --> 00:02:15,920
Python skills are valuable and highly sought after and are becoming increasingly popular in the world

38
00:02:15,920 --> 00:02:17,600
of analytics and business intelligence.

39
00:02:18,170 --> 00:02:22,340
One of the reasons so many people are learning Python, after all, is because there are so many opportunities

40
00:02:22,340 --> 00:02:22,940
in this field.

41
00:02:23,450 --> 00:02:28,070
Not only will Python make you a more well-rounded analyst, but it will also open up doors to new roles

42
00:02:28,070 --> 00:02:29,120
and new opportunities.

43
00:02:30,410 --> 00:02:35,600
This isn't to say that Python is the end all be all of analytics tools when it comes to data analytics.

44
00:02:35,630 --> 00:02:37,640
Each tool has unique strengths and weaknesses.

45
00:02:38,120 --> 00:02:42,620
Python shouldn't be the only tool in your stack, but it can add tremendous value when combined with

46
00:02:42,620 --> 00:02:45,830
other tools like Excel, SQL, Power, BI and Tableau.