Data Analysis with Pandas and NumPy in Python [2024]

Data Analysis with Pandas and NumPy in Python [2024], available at $19.99, has an average rating of 4.05, with 71 lectures, 6 quizzes, based on 55 reviews, and has 2750 subscribers.

You will learn about Start Data Science python programming professionally Do Case Study on Real World Data for Covid19 and Car Price Data Start using libraries used in Data Science project like Pandas and NumPy Extract data from various sources like websites,twitter, pdf files, csv and RDBMS databases Start doing the extrapolatory data analysis ( EDA) on any kind of data and start making the meaningful business decisions Start making visualisations charts – bar chart , box plots which will give the meaningful insights And Probably start applying for the best job in the world This course is ideal for individuals who are Beginner who are passionate about Data or Looking to change career into Data Science field It is particularly useful for Beginner who are passionate about Data or Looking to change career into Data Science field.

Enroll now: Data Analysis with Pandas and NumPy in Python [2024]

Summary

Title: Data Analysis with Pandas and NumPy in Python [2024]

Price: $19.99

Average Rating: 4.05

Number of Lectures: 71

Number of Quizzes: 6

Number of Published Lectures: 71

Number of Published Quizzes: 6

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Start Data Science python programming professionally

Do Case Study on Real World Data for Covid19 and Car Price Data

Start using libraries used in Data Science project like Pandas and NumPy

Extract data from various sources like websites,twitter, pdf files, csv and RDBMS databases

Start doing the extrapolatory data analysis ( EDA) on any kind of data and start making the meaningful business decisions

Start making visualisations charts – bar chart , box plots which will give the meaningful insights

And Probably start applying for the best job in the world

Who Should Attend

Beginner who are passionate about Data

Looking to change career into Data Science field

Target Audiences

Beginner who are passionate about Data

Looking to change career into Data Science field

Recent Reviews:

Thanks to Piyush Sir, for explaining basics of python in a lucid way.

I would recommend this course, whoever is very much new to Python and interested in understanding basic concepts of Numpy and Pandas from scratch.”

Yes, this is course i was looking for my knowledge which will eventually help me in my professional growth.

I will surely recommend this course to anyone who is interested in Data science or Data Engineering”

Lifetime access to course materials . 100% money back guarantee

The course is packed with real life projects examples and include Covid19 Data Analysis ( real life Hospital Data ) which you will be doingas part of Assignment. Solutions and source code is provided

1. Get Transformed from Beginner to Expert .

2. Become expert in using Python Pandas,NumPy libraries ( the most in-demand )

3. Source Codes are provided for each session so that you can practise along with the lectures..

4. Start doing the extrapolatory data analysis ( EDA) on any kind of data and start making the meaningful business decisions

5. Start python programming professionally

6. Extract data from various sources like websites, pdf files, csv and RDBMS database.

7. Start using the highest in-demand libraries used in Data Science / Data Analysis project : Pandas , NumPy 

8. Start making visualizations charts – bar chart , box plots which will give the meaningful insights.

9. Become expert in storytelling

10. Learn the art of Data Analysis , Visualizations for Data Science Projects

We will also go through Melbourne Real estate data , market data , Covid 19 Data and analyse which factors are key to decide car price .

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why Learn Python ?

Lecture 2: How to make the Best use of this Course

Lecture 3: Installation of Developer Environment -Anaconda & Jupyter

Lecture 4: Crash Course -1 : Python Basic Program

Lecture 5: Crash Course -2 : Python Basic Program

Chapter 2: Python Data Types

Lecture 1: Introduction to Python List

Lecture 2: Understanding Python List

Lecture 3: Introduction to Tuples

Lecture 4: Understanding Tuples

Lecture 5: Introduction to Dictionary

Lecture 6: Understanding Dictionary

Lecture 7: Python Sets

Chapter 3: Control Structure in Python

Lecture 1: If else Conditions – Decisions Making

Lecture 2: Python Loops- Intro

Lecture 3: Understanding For and While Loop

Lecture 4: Python Comprehensions-Intro

Lecture 5: Comprehension ; Loops in List,Dictionary

Lecture 6: Python Function-Intro

Lecture 7: Understanding Python Function

Lecture 8: Map,Reduce,Filter -Introduction

Lecture 9: Map,Reduce ,Filter -Using Lambda Functions

Chapter 4: Python NumPy

Lecture 1: Introduction to NumPy

Lecture 2: Basics of NumPy

Lecture 3: Structures and Contents of Arrays

Lecture 4: Slice and Dice

Lecture 5: NumPy Arrays-Operations

Chapter 5: Python PANDAS

Lecture 1: Introduction to the World of Pandas

Lecture 2: Pandas- Series and Dataframes

Lecture 3: Pandas- File upload and DATA Analysis

Lecture 4: Indexing Dataframe

Lecture 5: Merging Dataframes and Arithmetics

Lecture 6: Summarising , Group by ,Pivoting

Chapter 6: Data Extractions and Website Scrapping

Lecture 1: Intro to Data Extraction

Lecture 2: Reading Delimited files and RDBMS data

Lecture 3: Scrapping Websites

Chapter 7: Data Cleaning for Data Analysis / Business Insights

Lecture 1: Data Cleaning -Introduction

Lecture 2: Imputing Missing Values Techniques – Melbourne Real Estate Data-1

Lecture 3: Imputing Missing Value – Melbourne Real Estate Data-2

Chapter 8: Data Visualisations

Lecture 1: Introduction to the world of Visualisation

Lecture 2: Types of Plots

Lecture 3: Matplotlib Basics

Lecture 4: Matplotlib-Marketing Data Plots ( Boxplots,Histograms,Scatter)

Lecture 5: Searborn-Basics – Beautify the plots ( Boxplots,histograms,scatter )

Lecture 6: Seaborn-Correlation Matrix

Lecture 7: Aggregators Plots ( Bi-variate Analysis ) – Bar Charts , Boxplots , Histograms

Lecture 8: Timeseries Plots – Heatmap Plots

Chapter 9: Extrapolatory Data Analysis – Case Study (Car Price Data Sets )

Lecture 1: Understanding Data and uploading

Lecture 2: Data Manipulation and Analysis-1

Lecture 3: Data Manipulation and Analysis-2

Lecture 4: Data Cleaning

Lecture 5: PPT Presentation to Business Users for Data Insights

Chapter 10: Extrapolatory Data Analysis for Covid 19 ( Case Study )

Lecture 1: Introduction to Case Study for Covid 19

Chapter 11: Bonus Lectures on Machine Learning

Lecture 1: Understanding Machine Learning

Lecture 2: Introduction to Linear Regression

Lecture 3: CSV file Upload and Data Analysis

Lecture 4: Data Visualisations

Lecture 5: Model Building

Lecture 6: Assumptions of Linear Regression

Lecture 7: Model Fitness and Validation – 1

Lecture 8: Model Fitness and Validation – 2

Chapter 12: Investment Case Study for an Asset Management Firm using EXCEL

Lecture 1: Introduction to Investment Case Studies

Lecture 2: Understanding Case Studies and Downloads

Lecture 3: Assignment Solutions:Data Preparation-1

Lecture 4: Data Preparation – 2

Lecture 5: Data Preparation – 3

Lecture 6: Data Preparation – 4

Lecture 7: Data Preparation – 5

Lecture 8: Country Wise Analysis

Lecture 9: Investment Type Analysis

Lecture 10: Company Category Type Analysis

Lecture 11: PPT Presentation to Business Users for Data Insights

Instructors

Piyush S | insightEdge100.com
Data Scientist | Data Engineer | Project Manager

Rating Distribution

1 stars: 0 votes

2 stars: 3 votes

3 stars: 7 votes

4 stars: 19 votes

5 stars: 26 votes

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