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Learn Data Analysis From Scratch DVD
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Learn Data Analysis From Scratch
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Learn Data Analysis From Scratch
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 81 Lessons (11h 24m) | Size: 2.77 GB

In this course you are going to learn about following topics
LEARN DATA ANALYSIS FROM SCRATCH
Part I : Tools For Data Analysis
Python Refresher
01 Course Pre-Requisite
Learn Coding From Scratch With Python3
02 Ipython Interpreter
03 Jupyter Notebook
04 Python Refresher - Basic DataTypes
05 Python Refresher - Collection Types - Lists
06 Python Refresher - Collection Types - Dictionaries
07 Python Refresher - Collection Types - Sets
08 Python Refresher - Collection Types - Tuples
09 Python Refresher - Functions
10 Python Refresher - Classes And Objects
Numpy Core Concept For Data Analysis
Step 1 : Concept : Numpy Introduction
What is Numpy?
Why Use Numpy?
Step 2 : Concept : Arrays Revisited
Types Of Arrays
Step 3 : Lab : Ways to Create Arrays
1. Create Arrays Using Python List
2. Using Numpy's Methods
Step 4 : Concept + Lab : Numpy Array Internals
Dimensions
Shape
Strides
Step 5 : Concept + Lab : Data Types and Casting
Step 6 : Concept + Lab : Slicing And Indexing
1. Understand Slicing and Indexing 1-D Array
2. Understand Slicing and Indexing Multidimensional Array
Step 7 : Concept + Lab : Array Operations
1. Common Operations On Arrays
2. Commonly Used Functions for Numpy Array Operations
Step 8 : Concept + Lab : Broadcasting
Array Broadcasting Principle
Understand Usage of Broadcasting
Step 9 : Concept + Lab : Understand Vectorization
Pandas Core Concept For Data Analysis
Step 1 : What is Pandas
Step 2 : DataFrames
Step 3 : DataFrames Basics
Step 4 : Handling Missing Data
Step 5 : GroupBy
Step 6 : Aggregation
Step 7 : Transform
Step 8 : Window Functions
Step 9 : Filter
Step 10 : Join Merge And Concat
Step 11 : Apply Method
Step 12 : DataFrame Reshape
Step 13 : Calculate Frequency Distribution
Part II : Data Analysis Core Concepts
What is Data
What is DataSet
Types of Variables
Definition
Types of Data Types
Why Data Types are important?
How do you collect Information for Different Data Types
For Nominal Data Type
Ordinal Data
Continuous Data
Descriptive Statistics Concepts
Types Of Statistics
Descriptive statistics
Inferential Statistics
What it is?
Concept 1 : Understand Normal Distribution
Concept 2 : Central Tendency
Concept 3 : Measures of Variability
Range
Interquartile Range(IQR)
Concept 4 : Variance and Standard Deviation
Concept 5 : Z-score or Standardized Score
Concept 6 : Modality
Concept 7 : Skewness
Concept 8 : Kurtosis
How it look like
Mesokurtic
platykurtic
Leptokurtic
Part III : Tools For Data Visualization
MatDescriptionlib Introduction
MatDescriptionlib Architecture
Seaborn Description Overview
Parameters Of Description
Types Of Description By Purpose
1. Correlation
Type Of Graphs In Correlation Category
Scatter Description
Counts Description
Marginal BoxDescription
Correlogram
Pairwise Description
2. Deviation
Diverging Bars
Diverging Dot Description
3. Ranking
Ordered Bar Chart
Dot Description
4. Distribution
Histogram for Continuous Variable
Histogram for Categorical Variable
Density Curves with Histogram
Box Description
Dot + Box Description
5. Composition
Pie Chart
Treemap
Bar Chart
6. Change
Time Series Description
The below time series Descriptions all the the peaks and troughs and annotates the occurence of selected special events.
Time Series Decomposition Description
Time series decomposition Description shows the break down of the time series into trend, seasonal and residual components.
Part IV : Step By Step Exploratory Data Analysis and Data Preparation Workflow With Project
What is Exploratory Data Analysis (EDA)?
Value of Exploratory Data Analysis
Steps of Data Exploration and Preparation
Step 1 : Variable Identification
Step 2 : Univariate Analysis
Step 3 : Bi-variate Analysis
Step 4 : Missing values treatment
Step 5 : Outlier Detection and Treatment
What is an outlier?
What are the types of outliers ?
What are the causes of outliers ?
What is the impact of outliers on dataset ?
How to detect outlier ?
How to remove outlier ?
Step 6 : Variable transformation
Step 7 : Variable creation
Learn Data Analysis From Scratch

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