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Data Analysis & ML Fundamentals

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Learn essential data analysis and machine learning fundamentals. Gain practical skills in Python, statistics, and predictive modeling

5 Modules
with Certifications
7:42 Hours
of Recorded Content
5.0 Ratings
by 2500 Learners
English
Language
Paid Course
Get this Course @ ₹799
2500 enrolled in this course

Data Analysis & ML Fundamentals

Data Analysis & ML Fundamentals is designed to provide learners with a practical understanding of the complete machine learning lifecycle. Beginning with data collection, cleaning, exploratory analysis, and statistical concepts, the course gradually introduces data preprocessing, feature engineering, supervised and unsupervised learning techniques, and real-world predictive analytics. Through multiple hands-on projects involving credit scoring, stress prediction, social media analysis, electricity forecasting, and groundwater prediction, learners gain practical exposure to industry-relevant data science workflows while building a strong foundation for advanced machine learning and artificial intelligence applications.
MASTERING THE DATA SCIENCE LIFECYCLE

Comprehensive Syllabus Outline

A comprehensive curriculum designed to take you from beginner to professional.

M1

Module 1 — Data Science Workflow

Data Collection, Data Wrangling and Data Cleaning Techniques for Analytics Projects
Exploratory Data Analysis and Data Visualization for Business Insights
Probability and Statistical Foundations for Data Science and Machine Learning

M2

Module 2 — Introduction to Machine Learning

Introduction to Machine Learning Concepts, Workflow and Real-World Applications

M3

Module 3 — Data Preprocessing and Feature Engineering

Data Preprocessing Techniques for Machine Learning Models
Feature Selection and Feature Engineering for Predictive Analytics
Handling Missing Values and Data Quality Issues in Datasets
Encoding Categorical Variables for Machine Learning Algorithms

M4

Module 4 — Machine Learning Models

Building Supervised Learning Models for Regression and Classification
Applying Unsupervised Learning Techniques using Clustering Algorithms

M5

Module 5 — Classification and Prediction Projects

Credit Score Classification using Machine Learning Techniques
Stress Level Prediction using Machine Learning Models
Social Media Advertisement Response Classification using Machine Learning
Electricity Price Forecasting using Predictive Machine Learning Models
Groundwater Level Prediction using Machine Learning Techniques

Predictive Analytics Portfolio

Develop a professional-grade suite of machine learning solutions that tackle real-world volatility and behavioral data.

Credit Scoring Classification Model with 90%+ Accuracy
PROJECT

Credit Scoring Classification Model with 90%+ Accuracy

Access hands-on simulation modules to master production level engineering challenges.

Stress Detection Pipeline utilizing behavioral sensor data
PROJECT

Stress Detection Pipeline utilizing behavioral sensor data

Access hands-on simulation modules to master production level engineering challenges.

Marketing Conversion Engine for social media ad spend optimization
PROJECT

Marketing Conversion Engine for social media ad spend optimization

Access hands-on simulation modules to master production level engineering challenges.

Time-Series Forecasting Model for electricity price volatility
PROJECT

Time-Series Forecasting Model for electricity price volatility

Access hands-on simulation modules to master production level engineering challenges.

Environmental Impact Model for groundwater level estimation
PROJECT

Environmental Impact Model for groundwater level estimation

Access hands-on simulation modules to master production level engineering challenges.

CORE TOOLING MASTERY

Tech Stack

Master the primary professional software development packages and workflow tools.

Python
Scikit-learn
Pandas
NumPy
Matplotlib
Seaborn
Jupyter Notebook
Career Impact

After this Course, You will be Able to

Observe the real-world utility outcomes you gain after program completion.

Architect end-to-end data pipelines for structured and unstructured datasets.
Execute deep exploratory data analysis to uncover hidden business trends.
Implement advanced feature engineering to optimize model training performance.
Deploy robust Supervised and Unsupervised learning models in Python.
Quantify model performance using precision, recall, and F1-score metrics.
Build a high-impact portfolio containing five unique industry-specific applications.

Course Stats

₹10.5 LPA

Average Salary

₹38 LPA

Highest Salary

120%

Salary Hike

9,500+

Job Vacancies

Key Features

Mentorship

Receive guidance and insights from industry experts

Hands-on Experience

Gain practical skills in a real-world cutting-edge projects.

Networking

Connect with professionals and peers in your field

Skill Development

Enhance your technical and soft skills

Career Advancement

Boost your resume with valuable experience

Dual Certificate

Get a certification to showcase your achievements
DIGITAL VERIFIABLE CREDENTIAL

Let Your Certificates Speak For You

Our certification formally validates your skill set in recruiter searches with unique QR code verification and LinkedIn-ready structures.

Unique Credential ID & QR Code
Recruiters can scan to instantly verify your project files, source repository, and official completion marks.

Linkedin Certified Recognition
Easily push to your Linkedin profile with 1-click credential linking to increase high-end corporate recruiter views.

Certificate Sample
Status
Verified

Where Our Learners Work

Our alumni are driving innovation at the world's most prestigious technology companies.

Flipkart
Freshworks
Juspay
Chargebee
Zoho
PayPal
PREMIER PLATFORM EXPERIENCE

Why Pantech?

An expert-crafted learning infrastructure built for technical fluency.

Industrial Expert Mentors

Direct guidance and weekly doubt clearing sessions hosted by hardware, embedded, and software engineering veterans.

24/7 Interactive Support

Ask coding doubts anytime on our student community workspace and receive instant assistance.

Self-Paced Learning Engine

Access lifetime recorded modules with adaptive pacing to balance academics and professional work.

Career Guidance Support

Exclusive resume review, mock interviews, and placement assistance from industry experts.

We are Accredited by

Our Awards & Achievements
Award 1
VERIFIED STUDENT REVIEWS

What Our Students Say

See how Pantech courses accelerated career transitions across India.

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Get exclusive lifetime access to coursework, simulators, custom templates, and direct placement opportunities.

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HAVE QUESTIONS?

Frequently Asked Questions

Find instant answers to all common questions about our technical certificate courses.

1. Do I need prior experience?

No prior experience is required for foundational courses. Advanced modules may recommend basic knowledge in programming or electronics.

2. What if I face technical issues?

You can reach our support team at training@pantechelearning.com or call +91 89255 334 88 / +91 89255 334 89.

3. What will I learn in this course?

You’ll cover essential data analysis and machine learning fundamentals, including Python programming, statistics, data preprocessing, visualization, and predictive modeling. You’ll also gain hands‑on experience with libraries such as Pandas, NumPy, Matplotlib, and Scikit‑Learn.

4. How long is the course?

The program includes 5 structured modules, designed to be completed at your own pace, typically within 4–6 weeks.

5. Will I get a certificate?

Yes, upon completion you’ll receive a verified certification from Pantech eLearning, which can be shared on LinkedIn and with employers.

6. What is the course fee?

The Data Analysis and Machine Learning Fundamentals course is available for ₹1,499, inclusive of all modules and certification.

7. Is there any project work included?

Yes, you’ll apply your skills in a guided data analysis and ML mini‑project to reinforce concepts and demonstrate practical learning outcomes.

8. Can I access the course materials anytime?

Absolutely. Once enrolled, you’ll have lifetime access to the course videos, notes, and resources.

Is this course suitable for beginners?

Yes. The course starts with data science fundamentals before progressing into machine learning concepts and practical projects.

Do I need prior programming knowledge?

Basic Python knowledge is recommended, but key concepts required for machine learning are introduced throughout the course.

Will I work on real-world datasets?

Yes. The curriculum includes multiple predictive analytics projects based on practical business and research scenarios

Which machine learning libraries are covered?

The course primarily focuses on Python-based tools including Scikit-learn, Pandas, NumPy, Matplotlib, and Seaborn.

Is deep learning included?

The primary focus is on data analysis and classical machine learning fundamentals. Deep learning is covered in advanced AI-focused courses.

Will this course help prepare for advanced AI programs?

Yes. It establishes a strong foundation in data preprocessing, machine learning algorithms, and predictive analytics required for advanced AI and deep learning learning paths.

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