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1 Months

Course Duration

Hands-On

Projects

30

Lab Days

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Course Overview

Home All Courses Data Analysis with Python

Data Analysis with Python

As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data.

This course provides a comprehensive introduction to data analysis using Python. Participants will learn to use Python libraries such as Pandas, Numpy and Matplotlib to clean, manipulate and visualize data.

By the end of this course, participants will have a solid understanding of data analysis techniques and be able to apply these techniques to real-world problems

(2K) 15K+ Learners
30 Days Cloud Lab Access
Estimated 11.5M new Data Science jobs(US)
Avg. Salary of over $84000 in Data Science roles
High demands in Tech, Finance, E-Commerce, Healthcare
Highly transferable mainstream skills

Program Highlights

Key Highlights

Certificate of Completion by CloudxLab
30 Days of Lab access
Timely Doubt Resolution
Best In Class Curriculum
Placement assistance
Lifetime Access to Course Material
Doubt clearing all weekdays
Hands-on experience in cloud labs

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  • Why Cloudxlab?

    CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable and future-ready.

Hands-on Learning

hands-on lab
  • Gamified Learning Platform
    Making learning fun and sustainable

  • Auto-assessment Tests
    Learn by writing code and executing it on lab

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    Lab comes pre-installed softwares and accessible everywhere

  • Accessibility
    Access the lab anywhere, anytime with an internet connection

Course Creators

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Jatin

Jatin Shah

Yale CS, Ph.D. IIT-Bombay

Past: Ex-LinkedIn, Yahoo

Curriculum

30+
Hours of Online Training
30
Days of Lab Access
3000+
Ratings
16K+
Learners

Data Analysis with Python

Foundation Courses

1. Programming Tools and Foundational Concepts
1. Linux
2. Getting Started with Git
3. Python Foundations
4. Getting Started with SQL

Course on Data Analysis

1. Getting Started with Numpy
1. Introduction to Numpy for numerical computing
2. Understanding and manipulating Numpy arrays
3. Mathematical operations and broadcasting
4. Statistics and data analysis using Numpy
5. Various Numpy Functions
2. Getting Started with Pandas
1. Introduction to Pandas library for data analysis
2. Manipulating and transforming data using Pandas DataFrames
3. Visualizing and exploring data using Pandas
4. Data cleaning, manipulation, and transformation using Pandas
5. Various Pandas Methods
3. Getting Started with Matplotlib
1. Introduction to Matplotlib for data visualization
2. Understanding and using different types of plots in Matplotlib
3. Customizing plots with labels, titles, legends, and annotations
4. Integrating Matplotlib with Pandas and Numpy for advanced data visualization.
5. Various Matplotlib functions
4. Analytics and Data Science
1. Probability
2. Statistics
3. Data Visualization
4. Data Cleaning
5. Feature Scaling
5. Real-world End-to-End Data Analysis
1. Perform Data Analysis in an end-to-end manner on a real-world dataset
6. Introduction to Machine Learning (Optional)
1. What is Machine Learning?
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
7. Training Machine Learning Models (Optional)
1. Training Machine Learning models
2. Evaluating Machine Learning models
3. Linear Regression
4. Model Optimization using Gradient Descent
5. Polynomial Regression
6. Regularization
7. Learning Curves
8. Logistic Regression

Projects

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