Data Science and Machine Learning: Mathematical and Statistical Methods Course Overview

Last Updated : 12 Feb 2024

Data Science and Machine Learning: Mathematical and Statistical Methods Course Overview

The "Data Science and Machine Learning: Mathematical and Statistical Methods" course is designed to provide a comprehensive foundation in the key mathematical and statistical concepts necessary for data science and machine learning. It covers a broad spectrum of topics that will equip learners with the skills to analyze data effectively and build predictive models. The course starts with basic statistical measures, such as mean, median, mode, and extends to more complex topics like Outlier detection, Hypothesis testing, and various types of machine learning algorithms including regression, classification, and clustering.

By delving into these areas, learners will gain proficiency in important tools for data analysis, such as BoxPlot analysis, Correlation coefficients, and A/B testing. The inclusion of modules on advanced topics like Naive Bayes, ROC curves, and Hyperparameter tuning ensures that participants are well-prepared for real-world data science challenges. This data science and machine learning certification is ideal for those seeking to enhance their skill set in machine learning and data science courses and pursue a career in this dynamic field.

5.0

Intermediate

Purchase This Course

USD

1,575^♱

View Fees Breakdown

Course Fee	1,575
Total Fees	1,575 (USD)

USD

1,150^♱

View Fees Breakdown

Course Fee	1,150
Total Fees	1,150 (USD)

USD

^♱

View Fees Breakdown

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)

Fee On Request

Live Training (Duration : 24 Hours)
Per Participant
Guaranteed-to-Run (GTR)
Classroom Training fee on request

4 Hours

Week Days

8 Hours

Weekends

Select Date
CST(united states)

Select Time

Day	Time
to	to

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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

Happiness Guaranteed

Live Training (Duration : 24 Hours)
Per Participant
Classroom Training fee on request

Koeing Learning Stack

Koenig Learning Stack

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

Scroll to view more course dates

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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

Happiness Guaranteed

Can't Attend Live Online Classes? Choose Flexi- A self paced learning option

Request More Information

Email: WhatsApp:

Koenig's Unique Offerings

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.

Learn More

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.

Learn More

Download Course Contents

Course Prerequisites

To ensure you are well-prepared and can get the most out of the Data Science and Machine Learning: Mathematical and Statistical Methods course, the following prerequisites are recommended:

Basic understanding of mathematics, including familiarity with algebra and elementary statistics.
Some knowledge of probability concepts, as you will be dealing with statistical methods.
Ability to work with data in spreadsheets or similar tools, as data manipulation is a key part of data science.
Basic computer literacy, as the course will likely involve the use of data science software or programming languages.
A willingness to learn and engage with complex concepts, as data science and machine learning can be challenging but rewarding fields of study.

Please note that a deep expertise in mathematics or programming is not required to begin this course; however, a foundation in the above areas will enable you to grasp the concepts more quickly and fully.

Data Science and Machine Learning: Mathematical and Statistical Methods

DP-100T01: Designing and Implementing a Data Science Solution on Azure

Machine Learning with Python

R Programming

Target Audience for Data Science and Machine Learning: Mathematical and Statistical Methods

This course offers an in-depth exploration of data science and machine learning through mathematical and statistical methods. Ideal for analytics-focused professionals.
Target Audience and Job Roles:

Data Scientists
Machine Learning Engineers
Data Analysts
Statisticians
Business Analysts
Research Scientists
AI Engineers
Software Developers interested in data science
Quantitative Analysts
PhD and Masters students specializing in data science or related fields
Data Science Instructors/Educators
Technical Project Managers overseeing data-driven projects
Product Managers in tech companies focusing on analytics-driven features
Data Engineers looking to enhance analytical skills
Marketing Analysts interested in consumer data analysis
Finance Professionals leveraging predictive analytics

Learning Objectives - What you will Learn in this Data Science and Machine Learning: Mathematical and Statistical Methods?

Introduction to Course Learning Outcomes

This course aims to equip students with a thorough understanding of statistical methods and machine learning techniques essential for data analysis and predictive modeling.

Learning Objectives and Outcomes

Understand the foundational concepts of descriptive statistics, including measures of central tendency (mean, median, mode) and measures of variability (variance, standard deviation).
Gain proficiency in identifying and handling outliers and anomalies within data sets to ensure the accuracy of statistical analysis.
Learn the principles of probability distributions, particularly the normal distribution, and how it applies to data science methodologies.
Develop skills in creating and interpreting visual data representations such as histograms, box plots, and scatterplots to extract insights from data.
Acquire knowledge of correlation and regression analysis for determining relationships between variables and making predictions.
Master the concepts of hypothesis testing, p-values, and A/B testing to validate data-driven decisions and scientific conclusions.
Explore various machine learning algorithms, including Naive Bayes, K-Nearest Neighbors, and K-means clustering, for classification and pattern discovery.
Understand how to construct and analyze confusion matrices, ROC curves, and other performance metrics to evaluate the effectiveness of machine learning models.
Learn the importance of hyperparameter tuning to optimize the performance of machine learning algorithms.
Develop the ability to implement sampling techniques, understand biases, and apply resampling methods to ensure representative data analysis.