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      Data Analysis with Python and Pandas

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      Online

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      Important information

      Typology Course
      Methodology Online
      • Course
      • Online
      Description

      Simplilearn is the World’s Largest Certification Training Provider, with over 400,000+ professionals trained globally
      Trusted by the Fortune 500 companies as their learning provider for career growth and training
      2000+ certified and experienced trainers conduct trainings for various courses across the globe
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      Achievements for this centre

      2017
      2016

      How do you get the CUM LAUDE seal?

      All courses are up to date

      The average rating is higher than 3.7

      More than 50 reviews in the last 12 months

      This centre has featured on Emagister for 3 years

      What you'll learn on the course

      Data analysis
      Analysis

      Course programme

      Course Preview
      • Data Analysis with Python and Pandas
        • Module 01 - Introduction to the Course
          • 1.1 Course Introduction
          • 1.2 getting pandas and fundamentals
          • 1.3 Section Outro
        • Module 02 - Introduction to Pandas
          • 2.1 Section Intro
          • 2.2 Creating and Navigating a Dataframe
          • 2.3 Slices, head and tail
          • 2.4 Indexing
          • 2.5 Visualizing The Data
          • 2.6 Converting To Python List Or Pandas Series
          • 2.7 Section Outro
        • Module 03 - IO Tools
          • 3.1 Section Intro
          • 3.2 Read Csv And To Csv
          • 3.3 io operations
          • 3.4 read hdf and to hdf
          • 3.5 Read Json And To Json
          • 3.6 Read Pickle And To Pickle
          • 3.7 Section Outro
        • Module 04 - Pandas Operations
          • 4.1 Section Intro
          • 4.2 Column Manipulation (Operatings on columns, creating new ones)
          • 4.3 Column and Dataframe logical categorization
          • 4.4 Statistical Functions Against Data
          • 4.5 Moving and rolling statistics
          • 4.6 rolling apply
          • 4.7 Section Outro
        • Module 05 - Handling for Missing Data / Outliers
          • 5.1 Section Intro
          • 5.2 drop na
          • 5.3 Filling Forward And Backward Na
          • 5.4 detecting outliers
          • 5.5 Section Outro
        • Module 06 - Combining Dataframes
          • 6.1 Section Intro
          • 6.2 Concatenation
          • 6.3 appending data frames
          • 6.4 merging dataframes
          • 6.5 joining dataframes
          • 6.6 Section Outro
        • Module 07 - Advanced Operations
          • 7.1 Section Intro
          • 7.2 Basic Sorting
          • 7.3 sorting by multiple rules
          • 7.4 resampling basics time and how (mean, sum etc)
          • 7.5 resampling to ohlc
          • 7.6 Correlation And Covariance Part1
          • 7.7 Correlation and Covariance part 2
          • 7.8 Mapping custom functions
          • 7.9 graphing percent change of income groups
          • 7.10 Buffering Basics
          • 7.11 Buffering Into And Out Of Hdf5
          • 7.12 Section Outro
        • Module 08 - Working with Databases
          • 8.1 Section Intro
          • 8.2 writing to reading from database into a data frame
          • 8.3 resampling data and preparing graph
          • 8.4 Finishing Manipulation And Graph
          • 8.5 ection And Course Outro
          • 8.6 Section And Course Outro

      Additional information

      • What is the course about?

        This course is tailored to impart knowledge on the fundamentals of data analysis and data-intensive applications using Python with Pandas and NumPy libraries.

        Python is one of the most popular programming languages used for analyzing Big Data. Our training in Python will equip you to work with Big Data and gain better understanding of data analysis techniques.

      • What are the course objectives?

        By the end of this course, you will be able to:
        • Understand basic and advanced NumPy (Numerical Python) features
        • Perform data analysis with tools in the Pandas library
        • Manipulate, process, transform, merge and reshape large volumes of data
        • Solve data analysis problems in web analytics, social sciences, finance, and economics
        • Measure data by points in time, specific instances, fixed periods, or intervals

      • Who should do this course?

        Data Analysis with Python and Pandas course prepares you for real-world data analysis with python, regardless of data types. It is best suited for:
        • Data Analysts, who are new to Python language
        • Python Programmers, who are new to Data Analysis
        • Graduates


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