Learn Data Analysis From Scratch Using Python
Learn Data Analysis From Scratch Using Python, available at $39.99, has an average rating of 3.8, with 79 lectures, based on 12 reviews, and has 84 subscribers.
You will learn about Python Important Concepts For Data Analysis Numpy Concept for Data Analysis Python Pandas for Data Analysis Matplot lib for Data Visualization in Data Analysis Exploratory Data Analysis Workflow This course is ideal for individuals who are Beginners Python Developer who want to learn Data Analysis or Student who want to learn Numpy or Student who want to learn Pandas for Data Analysis or Student who want to learn Matplot lib package for data visualization or Student who want to learn Exploratory Data Analysis or Student who want to learn workflow of Data Analysis It is particularly useful for Beginners Python Developer who want to learn Data Analysis or Student who want to learn Numpy or Student who want to learn Pandas for Data Analysis or Student who want to learn Matplot lib package for data visualization or Student who want to learn Exploratory Data Analysis or Student who want to learn workflow of Data Analysis.
Enroll now: Learn Data Analysis From Scratch Using Python
Summary
Title: Learn Data Analysis From Scratch Using Python
Price: $39.99
Average Rating: 3.8
Number of Lectures: 79
Number of Published Lectures: 79
Number of Curriculum Items: 79
Number of Published Curriculum Objects: 79
Original Price: $174.99
Quality Status: approved
Status: Live
What You Will Learn
- Python Important Concepts For Data Analysis
- Numpy Concept for Data Analysis
- Python Pandas for Data Analysis
- Matplot lib for Data Visualization in Data Analysis
- Exploratory Data Analysis Workflow
Who Should Attend
- Beginners Python Developer who want to learn Data Analysis
- Student who want to learn Numpy
- Student who want to learn Pandas for Data Analysis
- Student who want to learn Matplot lib package for data visualization
- Student who want to learn Exploratory Data Analysis
- Student who want to learn workflow of Data Analysis
Target Audiences
- Beginners Python Developer who want to learn Data Analysis
- Student who want to learn Numpy
- Student who want to learn Pandas for Data Analysis
- Student who want to learn Matplot lib package for data visualization
- Student who want to learn Exploratory Data Analysis
- Student who want to learn workflow of Data Analysis
In this course you will learn about Data Analysis in a step by step manner. This course is divided into 4 parts. Following are the course Structure
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
Running Jupyter Notebook
Object introspection
%Run Command
%load Command
Executing Code from Clipboard
Shortcut of Jupyter Notebook
Magic Command
Matplotlib Integration
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
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
Matplotlib Introduction
Matplotlib Architecture
Seaborn Plot Overview
Parameters Of Plot
Types Of Plot By Purpose
1. Correlation
What It Is?
Type Of Graphs In Correlation Category
Scatter plot
Steps To Draw this graph
Step 1: Prepare Data
Step 2 : Plot By Each Category
Step 3 : Decorate the plot
Scatter plot with line of best fit
When To Use
Counts Plot
Marginal Boxplot
Correlogram
Pairwise Plot
P
2. Deviation
Diverging Bars
Diverging Dot Plot
3. Ranking
Ordered Bar Chart
Dot Plot
4. Distribution
Histogram for Continuous Variable
Histogram for Categorical Variable
Density Curves with Histogram
Box Plot
Dot + Box Plot
Categorical Plots
5. Composition
Pie Chart
Treemap
Bar Chart
6. Change
Time Series Plot
Time Series Decomposition Plot
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
Course Curriculum
Chapter 1: PART I : TOOLS FOR DATA ANALYSIS
Lecture 1: Course Pre-requisite
Lecture 2: Ipython Interpreter
Lecture 3: Jupyter Notebook
Lecture 4: Python Refresher – Basic DataTypes
Lecture 5: Python Refresher – Collection Types – Lists
Lecture 6: Python Refresher – Collection Types – Dictionaries
Lecture 7: Python Refresher – Collection Types – Sets
Lecture 8: Python Refresher – Collection Types – Tuples
Lecture 9: Python Refresher – Functions
Lecture 10: Python Refresher – Classes And Objects
Lecture 11: Numpy – What Is Numpy And Why To Use Numpy
Lecture 12: Numpy – Array Revisited
Lecture 13: Numpy – Ways To Create Arrays In Numpy
Lecture 14: Numpy – Numpy Array Internal
Lecture 15: Numpy – DataTypes And Casting
Lecture 16: Numpy – Slicing And Indexing Numpy Arrays
Lecture 17: Numpy – Numpy Array Operations
Lecture 18: Numpy – Broadcasting Concept
Lecture 19: Numpy – Vectorization Concept
Lecture 20: Pandas – What is Pandas
Lecture 21: Pandas – Creating DataFrame in Pandas
Lecture 22: Pandas – DataFrames Basics
Lecture 23: Pandas – Handling Missing Data
Lecture 24: Pandas – GroupBy
Lecture 25: Pandas – Aggregation
Lecture 26: Pandas – Transform
Lecture 27: Pandas – Window Functions
Lecture 28: Pandas – Filter
Lecture 29: Pandas – Join Merge And Concat
Lecture 30: Pandas – Apply Method
Lecture 31: Pandas – DataFrame Reshape
Lecture 32: Pandas – Calculate Frequency Distribution
Chapter 2: Part II : Data Analysis Core Concepts
Lecture 1: Data Analysis Core Concepts Introduction
Lecture 2: What is Data
Lecture 3: All About DataSet
Lecture 4: Types Of Variables / DataTypes
Lecture 5: Descriptive Statistics Concepts – Types Of Statistics
Lecture 6: Understand Normal Distribution
Lecture 7: Central Tendency (Mean Median Mode)
Lecture 8: Measure Of Variablility
Lecture 9: Z-Score (Standardized Score)
Lecture 10: Modality – Skewness – Kurtosis
Chapter 3: PART III : Tools For Data Visualization
Lecture 1: Matplotlib Introduction
Lecture 2: Matplotlib Architecture
Lecture 3: Seaborn Plot Overview
Lecture 4: Parameters Of Plot
Lecture 5: Types Of Plot By Purpose – Introduction
Lecture 6: Plot Type – Correlation – Scatter Plot
Lecture 7: Plot Type – Correlation – Scatter Plot With Best Fit Line
Lecture 8: Plot Type – Correlation – Counts Plot
Lecture 9: Plot Type – Correlation-Distribution – Marginal Boxplot
Lecture 10: Plot Type – Correlation – Correlogram-Heatmap
Lecture 11: Plot Type – Correlation – Pairwise Plot
Lecture 12: Plot Type – Deviation Plot – Diverging Bars
Lecture 13: Plot Type – Deviation Plot – Diverging Dot Plot
Lecture 14: Plot Type – Ranking Plot – Ordered Bar Plot
Lecture 15: Plot Type – Ranking Plot – Dot Plot
Lecture 16: Plot Type – Distribution Plot – Histogram for Continuous Variable
Lecture 17: Plot Type – Distribution Plot – Histogram for Categorical Variable
Lecture 18: Plot Type – Distribution Plot – Density Plot
Lecture 19: Plot Type – Distribution Plot – Box Plot
Lecture 20: Plot Type – Composition Plot – Pie Chart
Lecture 21: Plot Type – Composition Plot – Tree Map
Lecture 22: Plot Type – Composition Plot – Bar Chart
Lecture 23: Plot Type – Change Plot – Time Series Plot
Lecture 24: Plot Type – Change Plot – Time Series Decomposition Plot
Chapter 4: PART IV : STEP BY STEP EXPLORATORY DATA ANALYSIS
Lecture 1: Exploratory Data Analysis Workflow – Introduction
Lecture 2: Lets Understand The Big Picture
Lecture 3: What is Exploratory Data Analysis ?
Lecture 4: EDA – Step 1 – Variable Identification
Lecture 5: EDA – Step 2 -Univariate Analysis
Lecture 6: EDA – Step 3 -Concept – BiVariate Analysis
Lecture 7: EDA – Step 3 -LAB – BiVariate Analysis
Lecture 8: EDA – Step 4 – Missing values treatment
Lecture 9: EDA – Step 5 – Outlier Detection and Treatment
Lecture 10: EDA – Step 6 – Variable transformation
Lecture 11: EDA – Step 7 – Variable creation
Lecture 12: What Next ?
Chapter 5: Resources For Course
Lecture 1: Resource Location
Instructors
-
Mukesh Ranjan
Technical Consultant
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 3 votes
- 4 stars: 2 votes
- 5 stars: 5 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024