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Data Analytics With
Pandas Training


New Batches Starts For Data Analytics With Pandas Training From November

New Batches Starts For Data Analytics With Pandas Training From November

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Data Analytics With Pandas

Master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool.

By the end of this course, you’ll have not only have grasped the fundamental concepts of data analysis, but through using Python to analyse and manipulate your data, you’ll have gained a highly specific and much in demand skill set that you can put to a variety of practical used for just about any business in the world

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Weeks Duration
Hr/Week Therory
Hr/Week Lab
Students Per Batch

Data Analysis Intro

  •  Collecting Data
  •  Data wrangling
  • Exploratory data analysis (EDA)
  • Drawing conclusions
  • Sampling
  •  Descriptive statistics
  • Prediction and forecasting
  • Inferential statistics
  • Virtual environments
  • Installing Python packages
  • Why choose pandas?
  • Jupyte

Pandas DataFrames

  • Series
  • DataFrame
  • Index
  • A Python object
  • A file
  • A database
  • An API
  • Examining the data
  • Describing and summarizing the data
  • Indexing
  • Selecting columns
  • Slicing
  • Filtering
  • Creating new data & adding data
  • Removing/deleting unwanted data

Data Wrangling

  • Cleaning data
  • Transforming data
  • Data enrichment
  • Renaming DataFrame columns
  • Type conversion of DataFrame columns
  • Reordering, reindexing, and sorting data of DataFrame
  • Transposing DataFrames
  • Pivoting DataFrames
  • Melting DataFrames
  • Finding the problematic/corrupt data
  • Handling the issues in data

Aggregating Data Frames

Querying & Merging DataFrames

  • Arithmetic and statistics
  • Binning
  • Using functions on DataFrames
  • Window calculations
  • Pipes
  • Summarizing DataFrames
  • Aggregating by group
  • Pivot tables and crosstabs
  • Time-based selection and filtering
  • Shifting for lagged data
  • Differenced data
  • Resampling data
  • Merging time series data

Pandas and Matplotlib for Data Visualization

  • Introduction
  • Plot components
  • More options
  • Chart to show evolution over time
  • Chart to show relationships between variables
  • Charts for different distributions
  • Counts and frequencies
  • Scatter matrices
    Lag plots
  • Autocorrelation plots
  • Bootstrap plots

Seaborn for plotting and Customization Techniques

  • Representing categorical data
  • Representing correlations and heatmaps
  • Regression plots
  • Faceting
  • Chart titles and labels
  • Chart legends
  • Formatting chart axes
  • Adding reference lines to the graph
  • Shading chart regions
  • Annotations
  • Colors
  • Textures

Rule-Based Anomaly Detection

  • Presuppositions
  • The login_attempt_simulator package
  • Simulating from the command line
  • Percent difference
  • Tukey fence
  • Z-score
  • Evaluating performance


D. Shaurya

The instructor is so knowledgeable about the program and goes into detailed explanations, explaining why you would do something, where etc... The method of going deep into the fundamentals (such as parameters, arguments, dataframes are made of series and etc) is what inspires me the most to a strong foundation.

M. Viraj

I always tried to avoid programming, most courses and videos were too hard or boring for me to follow... This is first time that I have actually enjoyed learning python and could complete a course, THANK YOU

L. Aayush

Highly recommended for those wanting to learn python data analytics without any coding background.

J. Amith

This is a very comprehensive training. it deserves 5 stars. To anyone who wants to get this course, I totally recommend it. It was a brilliant presentation that explicitly focused on time-series analysis