Data Analytics using Python

This course will take you through the fundamentals of Data Acquisition, Data Cleaning, Data Mining and various analytics & visualization techniques using libraries in Python.

Participants should have a background of programming in Python or should have gone through the Python for Data Science course before taking this up. Applicable to students, working professionals and PMETs.

Applicable to students, working professionals and PMETs.

What you'll learn

Understanding and analyzing data is one of the key skills required in the industry today. This course is completely focused on the various aspects of data analytics using Python. Participants will be taught to use and taken through the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualization as well as the basic statistics knowledge required to understand the concepts in the latter courses.

About this course

Data Analytics using Python is an intermediate level course in understanding how to play with data and perform visualizations and analytics in Python.

Participants will learn to write programs in Python which can analysis, visualization, statistical calculations and data wrangling and manipulation. Participants who complete learning these skills will finish the course at an intermediate level of Python and will be ready to take up advanced courses in machine learning.

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Course Duration

1
Hours
1
Days
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Course Plan

To learn how to get data from external sources into the Python environment and manipulate and analyze data. Before any data analytics project, it is very important to use statistical algorithms and methods to analyze data as part of the data analytics process.

Introduction to python packages for data manipulation

Importing and exporting data

Importing datasets and understanding data

Basics of analyzing the data

To learn about how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics. This is where 80% of today’s data scientists and engineers spend their time and it if very important to know how to do it.

Dealing with data issues and preparation in Python

Data Formatting and conversions in Python

Data Wrangling

Working with Pandas

The objective of this module is to understand how to use appropriate statistical methods and visualizations for descriptive analytics.

Performing descriptive statistics with Python

Correlations, Scatter-plots and charts with matplotlib in Python

Understanding data analysis with respect to various business scenarios

This module gives the participants the very first steps towards forming hypothesis and doing predictive analytics. It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.

Linear Regression and Multiple Regression Models

Model Evaluation Methods

Model selection

The objective of this module is to understand basic metric and KPIs (Key Performance Indicators) of different business cases and plotting advanced interactive visualizations for data analysis to gain insights from data.

Understanding basic metrics and KPIs

Visualizations using Python (Seaborn, Folium)

Looking for something else?

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Computer Vision using Python

Ever wondered how autonomous vehicles work? This course trains you on the fundamentals of image processing, classification and computer vision using opencv and tensorflow.

Join us & launch your career in data science

It's time to upskill for the Industry 4.0