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New Batches Starts For Java Full Stack Training From November

Data Wrangling Training


New Batches Starts For Data Wrangling Training From November

New Batches Starts For Data Wrangling Training From November

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Data Wrangling Training

This training course provides you with several different data wrangling techniques and enough example information to begin exploring your own data. You’ll learn how to clean and format data, how to manipulate data structures, and how to build models using Python. By the end of this course, you’ll be able to confidently wrangle and analyze data with Python.

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Hr/Week Lab
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Topic : 1 introduction to data wrangling

  • Sets 
  • Introduction to Sets 
  • Union and Intersection of Sets 
  • Creating Null Sets 
  • Dictionary 
  • Tuples 
  • Creating a Tuple with Different Cardinalities 
  • Unpacking a Tuple 
  • Strings 
  • String Functions

Importance of Data Wrangling 

Python for Data Wrangling 

Lists, Sets, Strings, Tuples, and Dictionaries 

Topic 2 : Advanced Operations on Built-In Data Structures


  • Iterator 
  • Stacks 
  • User-Defined Methods 
  • Lambda Expressions 
  • Queue 
  • File Handling 
  • The with Statement 
  • Opening a File Using the with Statement 

Topic 3 : Introduction to Numpy, Pandas, and Matplotlib


  • NumPy Arrays and Features 
  • Conditional SubSetting 
  • Stacking Arrays 
  • Pandas DataFrames 
  • Indexing and Slicing Columns 
  • Indexing and Slicing Rows 
  • Refresher on Basic Descriptive Statistics 
  • Random Variables and Probability Distribution 
  • What is a Probability Distribution? 
  • Discrete Distributions 
  • Continuous Distributions 
  • Using NumPy and Pandas to Calculate Basic Descriptive Statistics 
  • Random Number Generation Using NumPy

Topic 4 : A Deep Dive into Data Wrangling with Python


  • Sales Data in an Excel File 
  • Subsetting the DataFrame 
  • An Example Use Case – Determining Statistics on Sales and Profit 
  • Conditional Selection and Boolean Filtering 
  • The GroupBy Method 
  • Conditional SubSetting 
  • Stacking Arrays 
  • Pandas DataFrames 
  • Indexing and Slicing Columns 
  • Indexing and Slicing Rows 
  • Merging by a Common Key 
  • The join Method 
  • Randomized Sampling 
  • The value_counts Method 
  • Pivot Table Functionality 
  • Functions with the apply Method

Topic 5: Getting Comfortable with Different kinds of Data Sources


  • Data Files Provided with This Chapter 
  • Libraries to Install for This Chapter 
  • Reading Data Using Pandas 
  • Data from a CSV File 
  • Delimiters Are Not Commas 
  • Headers of a CSV File 
  • Reading Only the First N Rows 
  • Setting the skip_blank_lines Option 
  • Reading CSV Data from a Zip File 
  • Reading from an Excel File Using sheet_name and Handling a Distinct sheet_name 
  • Reading HTML Tables Directly from a URL 
  • Reading from a JSON file 
  • Reading a PDF File 

Topic 6: Learning the Hidden Secrets of Data Wrangling


  • Introduction to Generator Expressions 
  • The % operator 
  • Using the format Function 
  • Additional Software Required 

Topic 7: Advanced Web Scraping and Data Gathering


  • Checking the Encoding of a Web Page 
  • Extracting Text from a Section 
  • Extracting Important Historical Events that Happened on Today’s Date 
  • Reading from a Local XML File into an ElementTree Object 
  • Extracting and Printing the GDP/Per Capita Information Using a Loop
  • Finding All the Neighboring Countries for Each Country and Printing Them 
  • Defining the Base URL (or API Endpoint)
  • Using the Built-In JSON Library to Read and Examine Data
  • Printing All the Data Elements 
  • Using a Function that Extracts a DataFrame Containing Key Information 
  • RegEx in the Context of Web Scraping 
  • Using the compile Method to Create a RegEx Program 
  • Finding the Number of Words in a List That End with “ing” 
  • The search Method in RegEx 
  • Sets of Matching Characters 
  • The findall Method 

Topic 8: RDBMS and SQL


  • How Is an RDBMS Structured? 
  • SQL 
  • Using an RDBMS (MySQL/PostgreSQL/SQLite) 
  • DDL and DML Commands in SQLite 
  • Reading Data from a Database in SQLite 
  • The ALTER Command 
  • The GROUP BY clause 
  • Adding Rows in the comments Table
  • Retrieving Specific Columns from a JOIN Query 
  • Deleting Rows from Tables 
  • Updating Specific Values in a Table 

Topic 9: Applications in Business Use Cases


  • Additional Skills Required to Become a Data Scientist 
  • Basic Familiarity with Big Data and Cloud Technologies 
  • What Goes with Data Wrangling? 
  • Tips and Tricks for Mastering Machine Learning


D. Anil

The instructor's wealth of knowledge and ability to communicate complex concepts made the training easy to follow. The real life examples for data wrangling were especially helpful in putting the material in perspective. I would highly recommend this data wrangling course to anyone looking to expand their skillset.

S. Sudhir

This course is extremely useful for introducing students to various data wrangling techniques and topics. I myself have learned a great deal from it and would highly recommend it to others looking to enter the field or simply learn more about working with data.

Ch. Latha

This training course is perfect for Python beginners. It covers several different data wrangling techniques and provides you with enough example information to start exploring your own data.

N. Anand

real-lifereal life examples for data wrangling are great and help put ideas into perspective! I would definitely recommend this data wrangling course to others and have already done so!

G. Divyavani

This course is extremely useful in helping to introduce data wrangling techniques and topics. The amount of knowledge and practical skills I have gained with Invictus training is invaluable. Thank you, Invictus