Python for Data Analytics and Machine Learning Course Overview

Last Updated : 03 Apr 2025

Python for Data Analytics and Machine Learning Course Overview

The Python for Data Analytics and Machine Learning course is designed to equip learners with the essential skills required to analyze data and build machine learning models using Python. This comprehensive program starts with a Course Introduction to set expectations and outline the learning journey. It progresses through a series of modules beginning with a Jupyter Overview, which is an essential tool for data science.

Learners will get a solid foundation in Python before diving into specialized libraries like Numpy and Pandas for data analysis. Visualization techniques are covered extensively with libraries such as Matplotlib, Seaborn, Plotly, and more. As the course advances, it delves into machine learning topics, starting with an Introduction to Machine Learning and then exploring various algorithms and methods like Linear Regression, Logistic Regression, K Nearest Neighbors, and others.

The course includes practical Data Capstone Projects for hands-on experience, and it concludes with cutting-edge topics such as Neural Nets and Deep Learning and Big Data and Spark with Python. This course is ideal for those looking to enhance their data analytics and machine learning capabilities, providing them with the knowledge and tools to tackle real-world data challenges.

5.0

Successfully delivered 18 sessions for over 32 professionals

Intermediate

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Course Fee	2,300
Total Fees	2,300 (USD)

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1,700^♱

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Course Fee	1,700
Total Fees	1,700 (USD)

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Flexi Video	16,449
Official E-coursebook
Exam Voucher (optional)
Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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1-on-1 Training

Schedule personalized sessions based upon your availability.

Learn More

Customized Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

Learn More

Happiness Guaranteed

Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

Learn More

Destination Training

Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

Learn More

Fly-Me-A-Trainer (FMAT)

Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

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Target Audience for Python for Data Analytics and Machine Learning

Koenig Solutions' Python for Data Analytics and Machine Learning course caters to aspiring data professionals seeking practical Python skills.

Data Analysts
Data Scientists
Machine Learning Engineers
Software Developers interested in data science
Business Analysts looking to leverage data analytics
Graduates pursuing a career in data-driven fields
IT Professionals aiming to expand into data roles
Research Scientists wanting to apply machine learning to their research
Statisticians seeking to enhance their analytical toolset
Product Managers wanting to make data-driven decisions
Entrepreneurs who wish to harness data insights for business intelligence
Marketing Professionals looking to interpret customer data and trends

Learning Objectives - What you will Learn in this Python for Data Analytics and Machine Learning?

Introduction to Learning Outcomes and Concepts

Gain practical skills in Python for data analytics and machine learning, mastering tools and techniques from basic scripting to advanced algorithms and data visualization methods.

Learning Objectives and Outcomes

Understand the Python programming language and its application in data analysis.
Utilize Jupyter Notebooks for interactive coding sessions and data visualization.
Perform data manipulation and analysis using Pandas and Numpy libraries.
Create a variety of data visualizations using Matplotlib, Seaborn, Plotly, Cufflinks, and built-in Pandas functions.
Apply geographical plotting for location-based data insights.
Complete capstone projects that consolidate data analytics skills with real-world datasets.
Grasp fundamental concepts of machine learning and its implementation in Python.
Build predictive models using algorithms such as Linear Regression, Logistic Regression, and K Nearest Neighbors.
Implement advanced machine learning techniques including Decision Trees, Random Forests, Support Vector Machines, and K Means Clustering.
Explore dimensionality reduction with Principal Component Analysis and develop recommender systems and natural language processing applications.