Python Data Analytics

Course Duration : 90 Hours

Learn Python Data Analytics with main coverage of Python Panda Module. Get the thorough insights on implementing each modules and using it in a daily routine.

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The Python Data Analytics training is designed to teach engineers, data scientists, statisticians, and other quantitative professionals the Python skills they need to use with the Python programming language to analyze and creating customized chart as per the actual data. 

Python Data Analytics Training, Objective & Outcomes:

  • Learn about previous Python versions. Similarities and differences
  • Data Types in Python, learn about lists, tuples, dictionary and many more
  • Creating variables, declaring global and local variables and reusing variables
  • Creating mixed data sets with various data types in python
  • Generating sequential and random number series in numerics, date, time
  • Learn to generate binary, hexacodes using python with various exercises
  • Learn to create python modules, classes
  • Creating and implementing existing python collections
  • Design and create python parametric and non-parametric functions
  • Looping and Data Flow in Python, learn to play with various loops
  • Exception handling in python, learn to use {try} {except} block in python
  • Practice sessions with the help of assignments, notes and assessment
  • File handling in python with various unorganized data in python like csv, txt, tbdl and many more
  • Data handling with the help of Pandas python framework
  • Learn to installing and un-installing external python libraries
  • Using external libraries like pandas, dataframe(df), numpy skypy, ndarrays and xlrd
  • Identifying bad data / missing data  and data cleaning technique with the help of python
  • Creating Graphs, Charts, Maps in Python for GUI representation
  • Merging various data sets in python
  • Data statistics in python: learn linear regression, data modelling, differentiation, T-Test, F-Test, Sampling, Co-relations and many more
  • Machine Learning (ML) and various other approaches to statistics
  • Want to explore other courses too?

Topics Covered


Python Introduction

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  • Comparison of Python with other languages and prior history
  • Strings and Numbers in python
  • Data Structures in Python using lists, tuples & Dictionary
  • The powerful Datetime Module in Python
  • Handling Data and Memory in Python
  • Various data flow control in python (While, Do Loop, For and For Each)
  • Usage of Functions in Python (Parametric Vs. Non-parametric)
  • Error or Exception Handling in Python

Handling data: Query and Questionnaire

  • Assignment 1 on Binary, Decimal and Hexacodes
  • Assignment 2: Handling inputs through looping, multiple question
  • Assignment 3: String handling in python. The interactive 32 questions in python
  • Correcting bad Data in Python with various methods

Reading / Writing Data Files

  • Reading / writing Structured Data
  • Working with Unstructured Data Types (CSV, TBDL, XML, XLS and many more)

Handling Data with NumPy

  • Installing and activating NumPy
  • Handling 1D, 2D and ND Array Data
  • Using Axis Parameters
  • Deploying NumPy Data
  • Handling Bad / Missing data in NumPy

Data Analytics with Pandas

  • Sorting and Filtering Raw Data using Pandas
  • Defining your own variable
  • Data Frames and Series
  • Accessing elements by Index
  • Reading and Writing
  • Agreegating and Grouping in Pandas
  • Pivoting tables in Pandas
  • Time Series Analysis in Pandas
  • Visualization in Pandas
  • Statistical Analysis using Pandas
  • Assignments 1: on Pandas Data Handling
  • Assignment 2: on Pandas Data Framing and Pivoting
  • Assignemnt 3: Pandas Stats Problem solving exercises

Representing Data graphically

To get a little overview here are a few popular plotting libraries:

  • Matplotlib: low level, provides lots of freedom
  • Pandas Visualization: easy to use interface, built on Matplotlib
  • Seaborn: high-level interface, great default styles
  • ggplot: based on R’s ggplot2, uses Grammar of Graphics
  • Plotly: can create interactive plots

Multivariate Statistics

  • Linear Regression
  • Logistic regression

Python Data Analytics made for

Python Data Analytics training is designed for data engineers, data scientists, statisticians, and other quantitative professionals

Pre-requisites for Python Data Analytics

All attendees should have prior programming experience and an understanding of basic statistics.

What You Need To Bring

  • Anaconda Python 3.5
  • Spyder IDE (Comes with Anaconda)
  • For classes delivered online, all participants need either dual monitors or a separate device logged into the online session so that they can do their work on one screen and watch the instructor on the other. A separate computer connected to a projector or large screen TV would be another way for students to see the instructor’s screen simultaneously with working on their own.

Key Takeaways

Strong Analytical Skill in Python
Handling multi varitate data as per the need
Multiple file handling and data sources

About Trainer

  • International Certifications on Python Pandas, NumPi, XLRD
  • Microsoft certified trainer
  • Certified Azure cloud network engineer
  • 12+ Years in Data Analysis, Reporting and Automation
python data analytics

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