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Python For Data Analysis Training Course

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Python For Data Analysis ?

Why Data Science

If you want to try something creative that’s never done before; then Python is perfect for you. It’s ideal for developers who want to script applications and websites

Why Data Science
Open Source

There are many open-source Python libraries such as Data manipulation, Data Visualization, Statistics, Mathematics, Machine Learning.

Why Data Science
Sea of Data Sanitising Libraries

Libraries, such as NumPy, Pandas, and Matplotlib, help the data analyst carry out his or her functions, and should be looked at once you have Python’s basics nailed down

Who is This program for

  • This course is intended for people with little to no background in data analysis and computer programming
  • Fresh graduates who wish to make a career in the field of Big Data,Data Analytics
  • Final year engineering or MBA students who are planning to learn about predictive analytics
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.


  • Data Analysis
  • Knowledge Domains of the Data Analyst
  • Computer Science
  • Mathematics and Statistics
  • Machine Learning and Artificial Intelligence
  • Professional Fields of Application
  • When the Data Become Information
  • When the Information Becomes Knowledge
  • Types of Data
  • Problem Definition
  • Data Extraction
  • Data Preparation
  • Data Exploration/Visualization
  • Predictive Modeling
  • Model Validation
  • Deployment
  • NumPy: A Little History
  • The NumPy Installation
  • Ndarray: The Heart of the Library
  • Create an Array
  • Types of Data
  • The dtype Option
  • Intrinsic Creation of an Array
  • Arithmetic Operators
  • The Matrix Product
  • Increment and Decrement Operators
  • Universal Functions (ufunc)
  • Aggregate Functions
  • Indexing
  • Slicing
  • Iterating an Array
  • Conditions and Boolean Arrays
  • Shape Manipulation
  • Vectorization
  • Broadcasting
  • Structured Arrays
  • Loading and Saving Data in Binary Files
  • Reading File with Tabular Data
  • The pandas Library—An Introduction
  • Understanding Exceptions
  • Installation from Anaconda
  • Installation from PyPI
  • Installation on Linux
  • Installation from Source
  • A Module Repository for Windows
  • Test Your pandas Installation
  • Getting Started with pandas


  • Flexible Arithmetic Methods
  • Operations between DataFrame and Series
  • Functions by Element
  • Functions by Row or Column
  • Statistics Functions
  • Sorting and Ranking
  • Correlation and Covariance
  • Assigning a NaN Value
  • Filtering Out NaN Values
  • Filling in NaN Occurrences
  • Reordering and Sorting Levels
  • Summary Statistic by Level
  • Pandas: Reading and Writing Data
  • I/O API Tools
  • CSV and Textual Files
  • Using RegExp for Parsing TXT Files
  • Reading TXT Files into Parts or Partially
  • Writing Data in CSV
  • Writing Data in HTML
  • Reading Data from an HTML File
  • Reading Data from XML
  • Serialize a Python Object with cPickle
  • Pickling with pandas
  • Loading and Writing Data with SQLite
  • Loading and Writing Data with PostgreSQL
  • Reading and Writing Data with a NoSQL Database: MongoDB
  • pandas in Depth: Data Manipulation
  • Built-in Methods for Manipulation of Strings
  • Regular Expressions
  • GroupBy
  • A Practical Example
  • Hierarchical Grouping
  • Chain of Transformations
  • Functions on Groups
  • Advanced Data Aggregation
  • Data Visualization with matplotlib
  • Installation of matplotlib Library
  • IPython and IPython QtConsole

  • Backend Layer
  • Artist Layer
  • Scripting Layer (pyplot)
  • pylab and pyplot
  • A Simple Interactive Chart
  • Set the Properties of the Plot
  • matplotlib and NumPy
  • Saving the Code
  • Converting Your Session as an HTML File
  • Saving Your Chart Directly as an Image
  • Handling Date Values
  • Chart Typology
  • Horizontal Bar Chart
  • Multiserial Bar Chart
  • Multiseries Bar Chart with pandas DataFrame
  • Multiseries Stacked Bar Charts
  • Stacked Bar Charts with pandas DataFrame
  • Other Bar Chart Representations
  • 3D Surfaces
  • Scatter Plot in 3D
  • Bar Chart 3D
  • Display Subplots within Other Subplots
  • Grids of Subplots
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Event Data Analysis using AWS ELK Stack

This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation

Insurance Pricing Forecast Using Regression Analysis

In this project, we are going to talk about insurance forecast by using regression techniques

Sentiment Analysis of Twitter Data using PySpark and Live Graphs

In this project, Sentiment Analysis Application is developed using Pyspark which is combination of Apache Spark and Python. This application fetches Twitter data in live stream and classifies tweets into positive and negative categories


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Learn From Home

First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

Learn From Home

Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

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Learn from Classroom

Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

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All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

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What do you benefit from this programs
  • Comprehensive Hands-on with Data Preprocessing
  • Expertise Knowledge Level on Statistics
  • Gain Knowledge on Pandas, Numpy, Plotly, Matplotlib
  • Integration of Python With SQL DataBase For DataAnalysis

I’m Interested

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