Mega Python – Pandas, Numpy, ML, APIs, GraphQL, AWS, PySpark

Getting Started
1 Install python
2 Python3 and python
3 The python interpreter
4 Writing our first python code
5 Python IDLE program
6 Installing Anaconda
7 Create your first python notebook
8 Jupyter Notebook – The Dashboard
9 Jupyter Notebook – Coding commands
10 Setting up IDE – Visual Studio Code

Python Strings and Numbers
11 Variables and Strings
12 Working with Comments
13 How to load sample jupyter notebook
14 Working with Strings and Numbers
15 String functions
16 String formatting
17 Manipulating String
18 Intro to Numbers
19 Fun with Numbers
20 Numbers – modulus and floor division
21 Built-in functions for numbers
22 More math functions with math module
23 Formatting Numbers
24 The double equality sign
25 Getting User Input
26 Python Operators
27 Logical Operators
28 Comparison Operators
29 Boolean Operators

Python List
30 Python List
31 Adding and removing elements in a list
32 Popping items from a list
33 Removing an item by value
34 Sorting a list permanently or temporarily
35 Reverse a list
36 Avoiding Index errors
37 The list() constructor
38 Looping an entire list
39 Indentation
40 Numerical List
41 min, max and sum functions
42 Negative Indexing
43 Multi-diementional list
44 Range function
45 Looping multi-dimentional list
46 Slicing of a list
47 Slicing a List Part 2
48 Iterate over multiple list
49 Check if an item exist or not
50 Count total occurrence of an item
51 Membership operators
52 Find most common item
53 Nested List
54 List Comprehensions
55 List Comprehensions with if clause
56 Nested List Comprehensions
57 Flatten a list of lists
58 Remove duplicates from the list
59 Combine lists

Python Tuple
60 Introduction to Tuple
61 tuple constructor
62 Access tuple items
63 Nested Tuples
64 Slicing a tuple
65 Change Tuple item
66 Writing over a tuple
67 Concatenation and Repetition
68 Iterate through a tuple
69 Tuple Sorting
70 Tuple Packing & Unpacking
71 Tuple count() method
72 Tuple index() method
73 all() function with Tuples
74 any() function with tuples
75 sum() function with tuples
76 enumerate() function with tuples

Python Set
77 Create, Set Constructor, Add and remove methods
78 Find Length, clear all elements, and iterate all elements
79 Check if an item exist or not
80 pop method

NUMPY
81 Introduction to Numpy arrays
82 array attributes – shape
83 array attributes – ndim, size, dtype, nbytes
84 Array Data types
85 Create arrays from constant values
86 Create arrays from space values
87 Create arrays from set diagnals
88 Create arrays from functions
89 Indexing and slicing – Single dimension array
90 Indexing and slicing – Multi-dimension array
91 Creating views and copies
92 Array Indexing
93 Array indexing – multi dimensional array
94 Boolean indexing
95 Reshaping Numpy arrays
96 Joining arrays
97 Splitting arrays
98 Searching arrays
99 Sorting arrays
100 Sorting techniques
101 Sorting a matrix
102 Iterating 1-D, 2D, and 3-D arrays
103 Iterating arrays via nditer(), ndenumerate()
104 Arithmetic operations
105 Mathematical functions
106 Comparing arrays
107 Conditional functions
108 Aggregation functions

PANDAS
109 Creating a DataFrame from list or dictionaries
110 Creating an empty DataFrame
111 Create a dataframe from lists of lists
112 Rename DataFrame columns and indexes
113 Create a Dataframe from list of dictionaries
114 Create a Dataframe from tuples with zip function

Pandas Tricks
115 Check Equality
116 Using asset series for equality
117 Calculate memory usage
118 No of words in a column
119 Convert one set of values to another
120 Convert continuous data into categorical data
121 Create a datetime column from multiple columns
122 Resample by date time column
123 Create a cross-tabluation
124 Fill missing values using interpolation
125 Transpose a wide DataFrame
126 Create example DataFrames
127 Identify missing rows
128 Use query to avoid intermediate variables
129 Reshape a DataFrame from wide to long format

BUILDING APIs
130 Introduction to FastAPI
131 FastAPI installation and first route
132 Path parameters
133 Built-in documentation
134 Query Parameters
135 Request Body and Pydantic models
136 Setup SQLAlchemy, Postgresql on cloud
137 Setup Database configurations
138 Setup SQLAlchemy models
139 Setup Pydantic schemas
140 Populate all tables on postgreSQL
141 PUT Request
142 Defining HTTP Status codes

Python with GraphQL
143 What is GraphQL
144 Setting up GraphQL with Python Flask Server
145 Adding a cloud postgreSQL database
146 Creating a model
147 Creating a GraphQL Schema
148 GraphQL with Ariadne library
149 Setup Apollo GraphQL IDE
150 Write resolver for list all posts
151 Write resolver to list a query by id
152 Mutation – Create a new post
153 Mutation – Update a post
154 Mutation – Delete a post

Python with Amazon DynamoDB
155 Create a table
156 Get an item
157 Delete an item
158 Add an item
159 Update an item

Python with Apache Cassandra
160 Introduction to Cassandra
161 Setup Docker to spin cassandra
162 Read data
163 Read data via prepare statement
164 Write data synchronously and asynchronously
165 Docker cleanup

DATA VISUALIZATIONS
166 Introduction

Data Visualization with Dash
167 Introduction to Dash
168 Creating a quick Dash interactive application
169 Create a medal dashboard app – setting up layout
170 Creating Layout Components – Dropdown
171 Callback functions
172 Introduction
173 Create a Barchart component
174 Create interactive bar chart with callbacks
175 Applying styling
176 Dash Callbacks – Simple
177 Callback function with graph and a slider
178 Callback function with multiple inputs
179 Cross Filtering – Interactive Graphing

Building Interactive data apps with Streamlit
180 Crypto Currency Market Dashboard Application

Data Visualization with Plotly
181 Scatter plots
182 Bar Chart
183 Facet Plots
184 Facet Plot Grid
185 Adding lines to Facets
186 Pie Charts
187 Bar Chart – Horizontal
188 Gant Chart
189 Sunburst Chart
190 Treemaps
191 Financial Charts
192 Histogram
193 Animations

REALTIME DATA APPLICATIONS
194 Welcome to Realtime Data Applications

Realtime Blog Activity Feed
195 Setup the system with Pusher
196 Setup Pusher and Routes
197 Define backend API endpoints
198 Create the blog post view
199 Handling form events for add, delete and deactivate
200 Add pusher events to blog post view
201 How data is broadcast via pusher
202 View Realtime blog post events

Asynchronous programming in Python
203 Coding program to run sequentially
204 Async programming with aiohttp and asyncio
205 ‘s of Web site fetching asynchronously

DATA SCIENCE AND MACHINE LEARNING
206 Introduction to Machine Learning
207 Machine Learning Terminologies
208 Machine Learning Types
209 Data Exploration using seaborn
210 Data Processing
211 Create your own Simple Linear Regression

DEV OPS
212 Introduction

Amazon S3 Services
213 Introduction
214 Using Python for AWS S3
215 S3 – List Buckets
216 S3 – List objects
217 S3 – Upload a file
218 S3 – Download a file
219 S3 – Create a bucket
220 S3 – Get object metadata

Amazon SNS Services
221 Introduction to SNS
222 Create a topic
223 Publish messages

AWS Lambda Function
224 What is a Lambda Function
225 Create a function
226 Invoking Lambda function from another function – Create policy
227 Invoking Lambda function from another lambda function

AWS Step Functions
228 What are STEP Functions
229 Amazon step language (ASL)
230 Create lambda functions
231 Create state machine and trigger lambda functions

PySpark – SparkSQL and Dataframes
232 PySpark
233 Introduction to PySpark
234 Spark Components
235 Setup python spark on google colabs
236 What is a dataframe
237 What is RDD
238 Creating RDDs
239 Creating Python functions and lambda functions
240 Apply transformation to RDD, map and filter methods
241 flatMap and Set transformations
242 Doing multiple transformations
243 PySpark Dataframes
244 Create a dataframe from a schema
245 Create a dataframe from a CSV file
246 Convert PySpark Dataframe to Pandas dataframe
247 SparkSQL – Creating Dataframes
248 SparkSQL – applying groupBy and aggregation data
249 SparkSQL – multiple aggregation and filtering data
250 SparkSQL – filtering data with filter
251 SparkSQL – Apply pure SQL queries
252 Get Stock Data from yahoo finance

Analyzing Sales Transaction Data
253 Reading Sales transaction data
254 Running queries and find unique values
255 Introduction
256 Create custom function to plot heat maps and bar plot
257 Find total sales per payment method
258 Find average unit price per product
259 Find average purchase by client group
260 Find AverageTotal value by weeks and months
261 Find average per client group and warehouse
262 Find average per product group and warehouse
263 Find average per weekmonth and warehouse
264 Find average quantity with month and product group
265 Find correlations

Analyzing Employees Turnovers Churn Rates
266 The data and import libraries
267 Quick intro to pywaffle module
268 Import sample data
269 View distinct values and running queries
270 Create a waffle visual to display employee turnover
271 Visualize Employees Satisfaction score
272 Create employees groups per satisfaction bucket
273 Satisfaction Analysis
274 Number of employees vs department
275 Number of project vs employees analysis
276 What is the workload look like for those who left
277 What is the spread of tenure across employee segments
278 # of years employees stayed in the company
279 How long did the employees stay
280 # of years of service per employe segments

Global Earthquakes Analysis
281 Get Earthquake data with USGS API
282 Examine structure of the data
283 Summary statistics for categorial columns
284 Get stats for a particular column
285 Selecting column via list comprehensions, get and more
286 Slicing data
287 loc and iloc
288 Filtering information
289 Adding new data and using assign()

Monkey Pox Virus – Data Analysis
290 Reported cases by country
291 Plot cases distribution by country and city
292 Confirm Cases, and case status per country
293 Analyze frequency of symptomssigns
294 Time series analysis – cases per day

Thank You!
295 Your feedback is very valuable!
296 Bonus Lecture