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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Do you want to explore  Data Wrangling with Python 3? Do you want to learn more about Python3? You will learn many new things about Data wrangling with the help of Python 3.This course will give you a thorough understanding in this topic. 

If you want to learn Wrangling but you don't know where to start from then this course is for you. Maybe you already know about Python3 but you would like to learn a little bit more about its ways, ideas and importance. This is an in depth course in Data wrangling which is presented in a way that it can be structured easily and you can know about it easily. 

This is an  in depth course in Data wrangling where you will learn all the pre-process data which can be structured or unstructured before any analysis done on the dataset. You will also know how to retrieve data from given sources and learn about the amazing data storing industry. You will also know how to hack all the tips and techniques which will be important in your data science career. 

With a certificate in Python 3 you can get a job as a software developer, python developer, IT professional too and a lifetime access to this course is given. 

If you love data wrangling and you are interested in python you are welcomed here in this course. 

This course is for anyone who wants to explore Data analytics to current projects. Python developers, data analysts, and IT professionals can also take this. 

So are you waiting further? Come and enroll your name now.

Course Content

Total: 38 lectures
  • The Course Overview
  • Installing Anaconda Navigator on Windows/Linux
  • Importing and Parsing CSV in Python
  • Importing and Parsing JSON in Python
  • Scraping Data from Public Web – Part 1
  • Scraping Data from Public Web – Part 2
  • Importing and Parsing Excel Files – Part 1
  • Importing and Parsing Excel Files – Part 2
  • Manipulating PDF Files in Python – Part 1
  • Manipulating PDF Files in Python – Part 2
  • Difference between Relational and Non-Relational Databases
  • Storing Data in SQLite Databases
  • Storing Data in MongoDB
  • Storing Data in Elasticsearch
  • Comparative Study of Databases for Storage
  • The Most Important Step in Data Analysis
  • Viewing/Inspecting DataFrames
  • Renaming/Adding/Removing the DataFrame Columns
  • Dropping Duplicate Rows
  • Indexing DataFrame to Retrieve Specific Columns and Rows
  • Merging/Concatenating/Joining DataFrames
  • Dealing with Missing Values
  • Filtering and Sorting of DataFrame
  • Encoding/Mapping Existing Values – Part 1
  • Encoding/Mapping Existing Values – Part 2
  • Rescale/Standardize Column Values
  • Common Cleaning Operations
  • Exporting Datasets for Future Use
  • Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)
  • Types of Column Names/Features/Attributes in Structured Data
  • Split-Apply-Combine (Performing Group By Operation)
  • Descriptive Statistics Using Python – Part 1
  • Descriptive Statistics Using Python – Part 2
  • Using Visualizations
  • Cool Visualization of Real-World Datasets of World Population Evolution
  • Visualizations in Python – Part 1
  • Visualizations in Python – Part 2
  • Exploring an Online Visualization Tool (RAWGraphs)


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