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Data Analysis using Python and R for Absolute Beginner[2024]

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Data Analysis using Python and R for Absolute Beginner[2024], available at $74.99, has an average rating of 3.2, with 131 lectures, 10 quizzes, based on 68 reviews, and has 2131 subscribers.

You will learn about Start Data Science career by learning python and R programming Start using powerful Python libraries used in Data Science project Pandas , NumPy with Python Start using powerful ggplot libraries used in Data Science project in R 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 Learn the art of doing EDA on excel Integrate SQL with python and R program Solve the real world problem with the case studies And Probably start applying for the best job in the world Do hands on 8 – real world case studies like Covid19, Bank Marketing, Investment , Uber Demand Supply This course is ideal for individuals who are Looking to change career into Data Science field or Beginner who are passionate about Data It is particularly useful for Looking to change career into Data Science field or Beginner who are passionate about Data.

Enroll now: Data Analysis using Python and R for Absolute Beginner[2024]

Summary

Title: Data Analysis using Python and R for Absolute Beginner[2024]

Price: $74.99

Average Rating: 3.2

Number of Lectures: 131

Number of Quizzes: 10

Number of Published Lectures: 131

Number of Published Quizzes: 10

Number of Curriculum Items: 143

Number of Published Curriculum Objects: 143

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Start Data Science career by learning python and R programming
  • Start using powerful Python libraries used in Data Science project Pandas , NumPy with Python
  • Start using powerful ggplot libraries used in Data Science project in R
  • 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
  • Learn the art of doing EDA on excel
  • Integrate SQL with python and R program
  • Solve the real world problem with the case studies
  • And Probably start applying for the best job in the world
  • Do hands on 8 – real world case studies like Covid19, Bank Marketing, Investment , Uber Demand Supply

Who Should Attend

  • Looking to change career into Data Science field
  • Beginner who are passionate about Data

Target Audiences

  • Looking to change career into Data Science field
  • Beginner who are passionate about Data

Lifetime access to course materials . Udemy offers a 30-day refund guarantee for all courses

The course is packed with real life projects examples

  1. Get Transformed from Beginner to Expert .

  2. Become data literate using Python & R  codes.

  3. Become expert in using Python Pandas,NumPy libraries ( the most in-demand ) for data analysis , manipulation and mining.

  4. Become expert in R programming.

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

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

  7. Start python and R programming professionallyand bring up the actionable insights.

  8. Extract data from various sources like websites, pdf files, csv and RDBMS databas

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

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

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

  12. Learn to play with SQL on R and Python Console.

  13. Integrate RDBMS database with R and Python

Real world Case Studies Include the analysis from the following datasets

1. Melbourne Real Estate ( Python )

2. Market fact data.( Python )

3. Car Datasets( Python )

4. Covid 19 Datasets( Python )

5. Uber Demand Supply Gap ( R )

6. Bank Marketing datasets ( R )

7. Investment Case Study (Excel)

8. Market fact data.( SQL)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Python and R

Lecture 2: Course Outline

Chapter 2: Introduction-Python

Lecture 1: Installation of Developer Environment -Anaconda & Jupyter

Lecture 2: Crash Course -1 : Python Basic Program

Lecture 3: Crash Course -2 : Python Basic Program

Chapter 3: 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 4: 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 5: 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 6: 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 7: Data Extractions and Website Scrapping

Lecture 1: Intro to Data Extraction

Lecture 2: Reading Delimited files and RDBMS data

Lecture 3: Scrapping Websites

Chapter 8: 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 9: 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 10: 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 11: Extrapolatory Data Analysis for Covid 19 ( Case Study )

Lecture 1: Introduction to Case Study for Covid 19

Chapter 12: R – Introduction

Lecture 1: Introduction to the Course – R

Lecture 2: Installation of R and R Studio

Lecture 3: Understanding R Studio

Lecture 4: Understanding Datatypes in R

Lecture 5: Crash Course in R – 1

Lecture 6: Crash Course in R – 2

Lecture 7: Crash Course in R – 3

Lecture 8: Introduction to Vectors

Lecture 9: Vectors in R – 1

Lecture 10: Vectors in R – 2

Lecture 11: Factors in R -1

Lecture 12: Factors in R -2

Lecture 13: Introduction to Matrices

Lecture 14: Matrices

Chapter 13: Dataframes in R

Lecture 1: Introduction to Dataframes

Lecture 2: Creating Dataframes

Lecture 3: Accessing Dataframes

Lecture 4: Operations in Dataframes

Lecture 5: File Upload into Dataframe

Lecture 6: Introduction to List

Lecture 7: List

Chapter 14: Constructs in R programming

Lecture 1: Introduction to Constructs in R

Lecture 2: Relational and Logical Operators

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 3 votes
  • 3 stars: 18 votes
  • 4 stars: 20 votes
  • 5 stars: 25 votes

Frequently Asked Questions


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