English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 35m | 1.18 GB
Learn the data life cycle—from acquisition to processing to analysis—in Python
You might be working in an organization, or have your own business, where data is being generated continuously (structured or unstructured) and you are looking to develop your skillset so you can jump into the field of Data Science. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access.
In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip you with the tools and technologies you need to analyze datasets (real-life datasets containing lots of anomalies) using Python so that you can confidently jump into the field and enhance your skillset. The best part of this course is the take-away code templates generated using the real-life dataset.
Towards the end of the course, you’ll build an intuitive understanding of all the aspects available in Python for Data Wrangling.
By the end of this course, you will be comfortable with using R and its associated libraries to solve any problem associated with quantitative finance.
This hands-on course demonstrates concepts via slides, to make sure they’re explained in simple ways. Throughout the course, we will be using datasets downloaded from Kaggle and other sources on the public web for concepts practical intuition. We will also be creating a GitHub repo throughout the whole course, to help you maintain a good profile on GitHub and use those code templates in other problems. We also provide coding assignments because practice makes perfect.
What You Will Learn
- Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset.
- Retrieving data from different data sources (CSV, JSON, XML, Excel, PDF) and parse them in Python to give them a meaningful shape.
- Learn about the amazing data storage places in an industry which are being highly optimized.
- Perform statistical analysis using in-built Python libraries.
- Different techniques used to get meaningful insights out of unstructured data
- Hacks, tips, and techniques that will be invaluable throughout your Data Science career.
Table of Contents
Gathering and Parsing Data
1 The Course Overview
2 Installing Anaconda Navigator on WindowsLinux
3 Importing and Parsing CSV in Python
4 Importing and Parsing JSON in Python
5 Scraping Data from Public Web – Part 1
6 Scraping Data from Public Web – Part 2
Working with Data from Excel and PDF Files
7 Importing and Parsing Excel Files – Part 1
8 Importing and Parsing Excel Files – Part 2
9 Manipulating PDF Files in Python – Part 1
10 Manipulating PDF Files in Python – Part 2
Storing Data in Persistent Storage
11 Difference between Relational and Non-Relational Databases
12 Storing Data in SQLite Databases
13 Storing Data in MongoDB
14 Storing Data in Elasticsearch
15 Comparative Study of Databases for Storage
Cleaning Structured Data
16 The Most Important Step in Data Analysis
17 ViewingInspecting DataFrames
18 RenamingAddingRemoving the DataFrame Columns
19 Dropping Duplicate Rows
20 Indexing DataFrame to Retrieve Specific Columns and Rows
21 MergingConcatenatingJoining DataFrames
22 Dealing with Missing Values
More Data Cleaning and Transformation
23 Filtering and Sorting of DataFrame
24 EncodingMapping Existing Values – Part 1
25 EncodingMapping Existing Values – Part 2
26 RescaleStandardize Column Values
27 Common Cleaning Operations
28 Exporting Datasets for Future Use
Performing Statistical Analysis
29 Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)
30 Types of Column NamesFeaturesAttributes in Structured Data
31 Split-Apply-Combine (Performing Group By Operation)
32 Descriptive Statistics Using Python – Part 1
33 Descriptive Statistics Using Python – Part 2
Let the Visualizations Tell the Story
34 Using Visualizations
35 Cool Visualization of Real-World Datasets of World Population Evolution
36 Visualizations in Python – Part 1
37 Visualizations in Python – Part 2
38 Exploring an Online Visualization Tool (RAWGraphs)
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