Practical Data Science with Python Training

This course uses several graphs and visuals to explain all fundamental Data Science principles and their applications rather than complex mathematical calculations.

📘 Data Science 👥 1493 Enrolled ⏱️ 3 Days 💼 Level ⭐ 4.7 | 113 Reviews

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Course Overview

You can learn practical data science with Python to gradually comprehend data science and its methods.

With the help of several examples, this course is created to aid in your understanding of data science in the most straightforward manner possible.

This course uses several graphs and visuals to explain all fundamental Data Science principles and their applications rather than complex mathematical calculations.

This course adopts a distinct methodology centered on providing straightforward illustrations of each Data Science technique's operation and builds incrementally on those illustrations to cover Data Science.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will learn

Data collection, exploration, cleaning, munging, and manipulation
Explore the foundations of machine learning.
Use models like k-nearest Logistic regression and neighbors.
Study the fundamentals of linear algebra, statistics, and probability and comprehend their applications in data science.

Who Should Attend This Course?

Beginners interested in Data Science but hesitant to begin due to their fear of complex math
Beginners in the data science and computer science fields
Experts in the social sciences and data
Professors, lecturers, or tutors who want to simplify and improve how they teach their students new material
Especially those who focus on Data Science are students and academics.

Prerequisites

Required

Little programming experience is required.

📞 Talk to a Learning Advisor

📘 Practical Data Science with Python Outline

The Ascendance of Data
What Is Data Science?
Motivating Hypothetical: DataSciencester

The Basics
The Not-So-Basics

matplotlib
Bar Charts
Line Charts
Scatterplots

Vectors
Matrices

Describing a Single Set of Data
Correlation
Simpson's Paradox
Some Other Correlational Caveats
Correlation and Causation

Dependence and Independence
Conditional Probability
Bayes's Theorem
Random Variables
Continuous Distributions
The Normal Distribution
The Central Limit Theorem
For Further Exploration

Modeling
What Is Machine Learning?
Overfitting and Underfitting
Correctness
The Bias-Variance Trade-off
Feature Extraction and Selection

The Model
Example: Favorite Languages
The Curse of Dimensionality

The Model
Using Gradient Descent
Maximum Likelihood Estimation

The Model
Further Assumptions of the Least Squares Model
Fitting the Model
Interpreting the Model
Goodness of Fit
Digression: The Bootstrap
Standard Errors of Regression Coefficients
Regularization

The Problem
The Logistic Function
Applying the Model
Goodness of Fit
Support Vector Machines

Still have questions?

Reach out to our learning advisors for personalized guidance on choosing the right course, group training, or enterprise packages.

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What You Get with Microtek Learning

Instructor-Led Excellence

✓ Certified Instructor-led Training
✓ Top Industry Trainers
✓ Official Student Handbooks

Measurable Learning Outcomes

✓ Pre- & Post-Training Assessments
✓ Practice Tests
✓ Exam-Oriented Curriculum

Real-World Skill Building

✓ Hands-on Activities & Scenarios
✓ Interactive Online Courses
✓ Peer Collaboration (Not in self-paced)

Full Support & Perks

✓ Exam Scheduling Support ^*
✓ Learn & Earn Program ^*
✓ Support from Certified Experts
✓ Gov. & Private Pricing ^*

Our Clients

For over 10 years, Microtek Learning has helped organizations, leaders, students and professionals to reach their maximum potential. We have led the path by addressing their challenges and advancing their performances.