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Python + Data Science: Practical Guide

This course with a simple idea in mind: Teach you the basics of Python + Data Science in a practical way, so that you can acquire, test and master your Python skills gradually. If you check the co...

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Course Description

This course with a simple idea in mind: Teach you the basics of Python + Data Science in a practical way, so that you can acquire, test and master your Python skills gradually. If you check the courses curriculum, you will see that you will learn all these things with Python: Using Variables & Strings Using Booleans & Logical Operators Using Functions & Packages Using Lists, Tuples and D...

This course with a simple idea in mind: Teach you the basics of Python + Data Science in a practical way, so that you can acquire, test and master your Python skills gradually. If you check the courses curriculum, you will see that you will learn all these things with Python: Using Variables & Strings Using Booleans & Logical Operators Using Functions & Packages Using Lists, Tuples and Dictionaries Using For & While Loops Using Panda & Data Frames Doing Data Visualization Scraping Web Data Doing some basic Natural Language Processing (NLP) Basics of Machine Learning & Deep Learning And much more to come..

What you’ll learn
  • Using Variables & Strings
  • Using Booleans & Logical Operators
  • Using Functions & Packages
  • Using Lists, Tuples and Dictionaries
  • Using For & While Loops
  • Using Panda & Data Frames
  • Doing Data Visualization
  • Scraping Web Data
  • Doing some basic Natural Language Processing (NLP)
  • Basics of Machine Learning & Deep Learning
  • And much more to come..

Covering Topics

1
Section 1 : Introduction
2
Section 2 : Environment
3
Section 3 : Integers and Strings
4
Section 4 : If Statements and Basic Programming Logic
5
Section 5 : Lists, Tuples, Dictionaries and For/While Loops
6
Section 6 : Functions and Packages
7
Section 7 : Pandas and Data Frames
8
Section 8 : Visualization - Scatter Plots, Bar Plots
9
Section 9 : Scraping the Web with Python
10
Section 10 : Basics of Natural Language Processing (NLP)
11
Section 11 : Introduction to Machine Learning
12
Section 12 : Optional Classes
13
Section 13 : Project #1 - Analyze and visualize data on Kaggle
14
Section 14 : Project #2 - Natural language processing
15
Section 15 : Project #3 - Create a simple support vector machine

Curriculum

      Section 1 : Introduction
1
Introduction
      Section 2 : Environment
2
Introduction to data science
3
Installing dependencies
4
Real world examples
5
Using Anaconda and Jupyter notebooks
      Section 3 : Integers and Strings
6
Introduction to variables Preview
7
Integers, floats, and math operators
8
Strings and indexing part 1
9
Strings and indexing part 2
10
Using Modulo with Strings
11
Problem
12
Answer
      Section 4 : If Statements and Basic Programming Logic
13
Booleans and Comparison Operators Preview
14
If / Else Statements
15
Elif and Logic Operators
16
Try / Except Statements
17
Finally Statements and Advance Try/Except Logic
18
Exception Types
19
Problem
20
Answer
      Section 5 : Lists, Tuples, Dictionaries and For/While Loops
21
Lists and tuples
22
Dictionaries
23
For loops
24
While loops
25
Loop logic
26
List/Dictionary comprehensions
27
Problem
28
Answer
      Section 6 : Functions and Packages
29
Scripting in Python
30
Functions
31
Function parameters and scope
32
Packages and pip
33
Reading Files and the with statement
34
Writing Out to Files
35
Problem
36
Answer
      Section 7 : Pandas and Data Frames
37
Pandas and data Frames
38
Pandas part 2
39
Introduction to statistics
40
Statistics part 2
41
Practical analysis
42
Problem
43
Answer
      Section 8 : Visualization - Scatter Plots, Bar Plots
44
Introduction to visualization
45
Plotting and styling
46
Scatter plots
47
Bar Plots and standard deviation
48
Problem
49
Answer
      Section 9 : Scraping the Web with Python
50
Scraping and HTML
51
Building a simple crawler
52
Scraping data from HTML
53
Problem
54
Answer
      Section 10 : Basics of Natural Language Processing (NLP)
55
Introduction to NLP
56
Basic NLP
57
Introduction to sentiment analysis
58
Problem
59
Answer
      Section 11 : Introduction to Machine Learning
60
Introduction to machine learning
61
Introduction to reinforcement learning
62
Introduction to random forest modeling
63
Introduction to deep learning
64
Problem
65
Answer
      Section 12 : Optional Classes
66
Python Style Guidelines
67
Navigating Directories and os Package
68
Creating a Class Type
      Section 13 : Project #1 - Analyze and visualize data on Kaggle
69
Project 1 - Analyze and visualize data on Kaggle
70
Project 1 Answer
      Section 14 : Project #2 - Natural language processing
71
Project 2 - Natural language processing
72
Project 2 Answer
      Section 15 : Project #3 - Create a simple support vector machine
73
Project 3 - Create a simple support vector machine
74
Project 3 Answer

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