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What you'll learn?
- Build a Solid Foundation in Data Analysis with Python
- Manipulate data quickly and efficiently
- How to code with Pandas toolkit
- Able to analyze a large file
- Learn hundreds of methods and attributes across numerous pandas objects
- Create dataframes with Pandas and recognize analytical approaches to data
Course Overview
Pre-requisites
- No Prior Knowledge or Experience Needed, Only a Passion to Learn !
- Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and pandas 0.19.2.
- Basic knowledge of data types (Basic Maths, integers, floating point numbers, Logic Conditions) etc, but not necessary
- Basic Or intermediate experience with Microsoft Excel, but not necessary.
Target Audience
- Data Analysis Beginner
- Business and Analyst
- Students and Other Professionals
- Beginner Python developers Curious to learn about Data Science
- Aspiring data scientists who want to add Python to their tool arsenal
Curriculum 103 Lectures 15:44:16
-
Section 1 : Getting Started
- Lecture 2 :
- How To Get Most Out Of This Course
- Lecture 3 :
- Better To Know These Things
- Lecture 4 :
- How To Install Python IPython And Jupyter Notebook
- Lecture 5 :
- How To Install Anaconda For macOS And Linux Users
- Lecture 6 :
- How To Work With The Jupyter Notebook Part - 1
- Lecture 7 :
- How To Work With The Jupyter Notebook Part - 2
-
Section 2 : Pandas Building Blacks
- Lecture 1 :
- How To Work With The Tabular Data
- Lecture 2 :
- How To Read The Documentation In Pandas
-
Section 3 : Pandas Data Structures
- Lecture 1 :
- Theory On Pandas Data Structures
- Lecture 2 :
- How To Construct The Pandas Series
- Lecture 3 :
- How To Construct The DataFrame Objects
- Lecture 4 :
- How Construct The Pandas Index Objects
- Lecture 5 :
- Practice_Part_01
- Lecture 6 :
- Practice_Part_01 Solution
-
Section 4 : Data Indexing and Selection In Pandas
- Lecture 1 :
- Theory On Data Indexing And Selection
- Lecture 2 :
- Data Selection In Series Part 1
- Lecture 3 :
- Data Selection In Series Part 2
- Lecture 4 :
- Indexers Loc And Iloc In Series
- Lecture 5 :
- Data Selection In DataFrame Part 1
- Lecture 6 :
- Data Selection In DataFrame Part 2
- Lecture 7 :
- Accessing Values Using Loc Iloc And Ix In DataFrame Objects
- Lecture 8 :
- Practice_Part_02
- Lecture 9 :
- Practice_Part_02 Solution
-
Section 5 : Essential Functionalities
- Lecture 1 :
- Theory On Essential Functionalities
- Lecture 2 :
- How To Reindex Pandas Objects
- Lecture 3 :
- How To Drop Along Rows and Columns Axis
- Lecture 4 :
- Arithmetic And Data Alignment
- Lecture 5 :
- Arithmetic Methods With Fill Values
- Lecture 6 :
- Broadcasting In Pandas
- Lecture 7 :
- Apply And Applymap In Pandas
- Lecture 8 :
- How To Sort And Rank In Pandas
- Lecture 9 :
- How To Work With The Duplicated Indices
- Lecture 10 :
- Summarising And Computing Descriptive Statistics
- Lecture 11 :
- Unique Values Value Counts And Membership
- Lecture 12 :
- Practice_Part_03
- Lecture 13 :
- Practice_Part_03 Solution
-
Section 6 : Data Handling
- Lecture 1 :
- Theory On Data Handling
- Lecture 2 :
- How To Read The Csv Files Part - 1
- Lecture 3 :
- How To Read The Csv Files Part - 2
- Lecture 4 :
- How To Read Text Files In Pieces
- Lecture 5 :
- How To Export Data In Text Format
- Lecture 6 :
- How To Use Python's Csv Module
- Lecture 7 :
- Practice_Part_04
- Lecture 8 :
- Practice_Part_04 Solution
-
Section 7 : Data Cleaning and Preparation
- Lecture 1 :
- Theory On Data Preprocessing
- Lecture 2 :
- How To Handle Missing Values
- Lecture 3 :
- How To Filter The Missing Values
- Lecture 4 :
- How To Filter The Missing Values Part 2
- Lecture 5 :
- How To Remove Duplicate Rows And Values
- Lecture 6 :
- How To Replace The Non Null Values
- Lecture 7 :
- How To Rename The Axis Labels
- Lecture 8 :
- How To Discretize And Bin The Data
- Lecture 9 :
- How To Filter And Detect The Outliers
- Lecture 10 :
- How To Reorder And Select Randomly
- Lecture 11 :
- Converting The Categorical Variables Into Dummy Variables
- Lecture 12 :
- How To Use 'map' Method
- Lecture 13 :
- How To Manipulate With Strings
- Lecture 14 :
- Using Regular Expressions
- Lecture 15 :
- Working With The Vectorized String Functions
- Lecture 16 :
- Practice_Part_05
- Lecture 17 :
- Practice_Part_05 Solution
-
Section 8 : Data Wrangling
- Lecture 1 :
- Theory On Data Wrangling
- Lecture 2 :
- Hierarchical Indexing
- Lecture 3 :
- Hierarchical Indexing Reordering And Sorting
- Lecture 4 :
- Summary Statistics By Level
- Lecture 5 :
- Hierarchical Indexing With DataFrame Columns
- Lecture 6 :
- How To Merge The Pandas Objects
- Lecture 7 :
- Merging On Row Index
- Lecture 8 :
- How To Concatenate Along An Axis
- Lecture 9 :
- How To Combine With Overlap
- Lecture 10 :
- How To Reshape And Pivot Data In Pandas
- Lecture 11 :
- Practice_Part_06
- Lecture 12 :
- Practice_Part_06 Solution
-
Section 9 : Data Grouping and Aggregation
- Lecture 1 :
- Theory On Data Groupby And Aggregation
- Lecture 2 :
- Groupby Operation
- Lecture 3 :
- How To Iterate Over Groupby Object
- Lecture 4 :
- How To Select Columns In Groupby Method
- Lecture 5 :
- Grouping Using Dictionaries And Series
- Lecture 6 :
- Grouping Using Functions And Index Level
- Lecture 7 :
- Data Aggregation
- Lecture 8 :
- Practice_Part_07
- Lecture 9 :
- Practice_Part_07 Solution
-
Section 10 : Time Series Analysis
- Lecture 1 :
- Theory On Time Series Analysis
- Lecture 2 :
- Introduction To Time Series Data Types
- Lecture 3 :
- How To Convert Between String And Datetime
- Lecture 4 :
- Time Series Basics With Pandas Objects
- Lecture 5 :
- Date Ranges, Frequencies And Shifting Part - 1
- Lecture 6 :
- Date Ranges Frequencies And Shifting Part - 2
- Lecture 7 :
- Time Zone Handling
- Lecture 8 :
- Periods And Period Arithmetic’s
- Lecture 9 :
- Practice_Part_08
- Lecture 10 :
- Practice_Part_08 Solution
-
Section 11 : Analysing Real Life Projects Using Open Source Data
- Lecture 1 :
- A Brief Introduction To The Pandas Projects
- Lecture 2 :
- Project_1 Description
- Lecture 3 :
- Project_1 Solution Part - 1
- Lecture 4 :
- Project_1 Solution Part - 2
- Lecture 5 :
- Project_2 Description
- Lecture 6 :
- Project_2 Solution
- Lecture 7 :
- Project_3 Description
- Lecture 8 :
- Project_3 Solution Part - 1
- Lecture 9 :
- Project_3 Solution Part - 2
- Lecture 10 :
- Project Assignment
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Instructor
293986 Course Views
3 Courses
Hi, I am Pruthivraja L, with more than 7 plus years of teaching and training experience from various technical institutes. Teaching is my passion. I'm a Certified Data Analyst. I got certifications from various eLearning centers including Udemy, Intellipaat-Bengaluru, LinkedIn eLearning center, etc.
My teaching skills are Matlab, Python, SAS, R, AI and Machine learning, Data Science and Data Analysis.
I love teaching and training because I knew sharing knowledge with the globe through digital media makes us feel proud and we also get satisfaction for empowering and enlightening the new learning aspirants & enthusiasts of different skills.
I'm a multi-faceted software professional aspirant with demonstrated capability in deploying analytical and programming methodologies to extract insights for boosting and bolstering user requirements. Adept at conducting statistical analysis and data modeling for transforming raw data into actionable strategies.
I've written a student-friendly textbook in the electrical engineering field titled 'Elements of Electrical Engineering' under the publication of 'I.K. International Publishing House Pvt Ltd', New Delhi, India. The book is available in many countries including the USA and UK via Amazon and many other seller portals. The book is now started distributing under Wiley India Pvt. Ltd.
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