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Home>Training Library>Introduction to Data Science with Python

Introduction to Data Science with Python

129 Learners

Practice Tests

Unavailable

Video Course

52 Videos

Hands-on Labs

68 Labs

Cloud Sandbox

Unavailable

Course Overview

This Introduction to Data Science with Python course aims to help you master Python programming concepts that are essential for Data Science.

Key Features of the Course

8+ hours of training videos
Very extensive coverage to all the topics
Unlimited access for 2 years

Included in this course

Video Course52 Videos Available
Hands-on Labs68 Labs Available

Topic-wise Content Distribution

Introduction

lecture

Course Intro
Try now
5m 35s

Data Pre-processing

8 lectures

Types of Machine Learning Problems

6 lectures

Linear Regression

2 lectures

Optimization and Gradient Descent

3 lectures

Logistic Regression

3 lectures

Deep Learning Introduction & Perceptrons

3 lectures

Modern Deep Learning Libraries

5 lectures

Feedforward Neural Networks

3 lectures

Recurrent Neural Networks

3 lectures

Convolutional Neural Networks

4 lectures

Machine Learning on the Cloud

11 lectures

Created by Certified Experts, these hands-on labs offer an excellent introduction to data science with Python. You'll learn to write efficient Python code for data analysis, visualization, and manipulation. The labs provide step-by-step guidance for tasks in a practical, real-time environment

Topic-wise Content Distribution

Data Pre-processing

36 labs

Exploring Data Frame Basics
1h 30m
Getting started with Matplotlib
1h
Understanding Space and Time Complexity
2h
Understanding Binary Search
1h
Finding Elements Common in Two Lists
1h
Getting started with IRIS dataset and 2D scatter plot
1h
Exploring 3D scatter plot
30m
Exploring Pair plots
30m
Getting started with Probability Density Function (PDF)
1h
Understanding Univariate Analysis using PDF
1h
Getting started with Cumulative Distribution Function (CDF)
1h
Exploring Mean, Variance, Standard Deviation and median
1h
Introduction to Vectors (2D, 3D, nD) Row Vector & Column Vector
30m
Understanding Dot Product and Angle between 2 vectors
45m
Exploring Projection & Unit Vector
1h
Exploring Equations of Line, Plane and Hyperplane
1h
Understanding Concepts of Linear Algebra
2h
Getting started with probability and statistics
1h
Exploring Gaussian or Normal Distribution
1h
Understanding the Concepts of Standardization & Kernel Density Estimation (KDE)
1h 30m
Key Operations on Data Frames
2h
Exploring the Concept of Q-Q Plot
1h
Understanding Chebyshev’s inequality
1h
Exploring Discrete and Continuous Uniform Distributions
2h
Random Number Generator (Uniform Distirbution)
30m
Exploring Bernoulli and Binomial distributions
1h
Exploring Log-Normal and Power Law Distributions
1h
Understanding the Box-Cox Transformation
1h 30m
Introduction to Confidence Interval (C.I)
45m
Exploring the Foundations of Hypothesis Testing
45m
Understanding the Two-Sample Kolmogorov-Smirnov Test (K-S Test)
45m
Understanding Dimensionality Reduction Using PCA
45m
Understanding how to Represent a Dataset using Python
1h
Exploring the Concept of Data Preprocessing
45m
Calculating Mean, Variance & Co-Variance of a Data Matrix
1h
Understanding the concept of weight vector in machine learning
1h

Types of Machine Learning Problems

5 labs

Linear Regression

3 labs

Optimization and Gradient Descent

4 labs

Logistic Regression

6 labs

Deep Learning Introduction & Perceptrons

4 labs

Feedforward Neural Networks

1 lab

Recurrent Neural Networks

3 labs

Convolutional Neural Networks

2 labs

Project

4 labs

Video Lectures

52 Videos Available

Topic-wise Content Distribution

Introduction

Course Intro
Try now
5m 35s

Data Pre-processing

Types of Machine Learning Problems

Linear Regression

Optimization and Gradient Descent

Logistic Regression

Deep Learning Introduction & Perceptrons

Modern Deep Learning Libraries

Feedforward Neural Networks

Recurrent Neural Networks

Convolutional Neural Networks

Machine Learning on the Cloud

Labs

68 Labs Available

Topic-wise Content Distribution

Data Pre-processing

Exploring Data Frame Basics
1h 30m
Getting started with Matplotlib
1h
Understanding Space and Time Complexity
2h
Understanding Binary Search
1h
Finding Elements Common in Two Lists
1h
Getting started with IRIS dataset and 2D scatter plot
1h
Exploring 3D scatter plot
30m
Exploring Pair plots
30m
Getting started with Probability Density Function (PDF)
1h
Understanding Univariate Analysis using PDF
1h
Getting started with Cumulative Distribution Function (CDF)
1h
Exploring Mean, Variance, Standard Deviation and median
1h
Introduction to Vectors (2D, 3D, nD) Row Vector & Column Vector
30m
Understanding Dot Product and Angle between 2 vectors
45m
Exploring Projection & Unit Vector
1h
Exploring Equations of Line, Plane and Hyperplane
1h
Understanding Concepts of Linear Algebra
2h
Getting started with probability and statistics
1h
Exploring Gaussian or Normal Distribution
1h
Understanding the Concepts of Standardization & Kernel Density Estimation (KDE)
1h 30m
Key Operations on Data Frames
2h
Exploring the Concept of Q-Q Plot
1h
Understanding Chebyshev’s inequality
1h
Exploring Discrete and Continuous Uniform Distributions
2h
Random Number Generator (Uniform Distirbution)
30m
Exploring Bernoulli and Binomial distributions
1h
Exploring Log-Normal and Power Law Distributions
1h
Understanding the Box-Cox Transformation
1h 30m
Introduction to Confidence Interval (C.I)
45m
Exploring the Foundations of Hypothesis Testing
45m
Understanding the Two-Sample Kolmogorov-Smirnov Test (K-S Test)
45m
Understanding Dimensionality Reduction Using PCA
45m
Understanding how to Represent a Dataset using Python
1h
Exploring the Concept of Data Preprocessing
45m
Calculating Mean, Variance & Co-Variance of a Data Matrix
1h
Understanding the concept of weight vector in machine learning
1h

Types of Machine Learning Problems

Linear Regression

Optimization and Gradient Descent

Logistic Regression

Deep Learning Introduction & Perceptrons

Feedforward Neural Networks

Recurrent Neural Networks

Convolutional Neural Networks

Project

Why Choose Us?

Online Course

8+ hours of Training Videos for all the objectives. You will be amazed by the way of explaining the concepts that are very easy to understand.

Unlimited Access

Our Courses come with the validity. Once purchased, you can access them for 2 years.

Experts Support

Our support team consists of Experts, ready to clarify all your questions.

Testimonials/Feedback

Introduction to Data Science with Python Reviews from Our Customers.

Frequently Asked Questions

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Maria

4.0

Good layout

Useful resource.

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Great study resource

Whizlabs has consistently delivered quality course.

4 years ago

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4.0

Highly recommended

Gives details of the service and help us well to understand concepts well.

4 years ago

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5.0

Well explained

Detailed explanation with high clarity.

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Knowledgeable content

The content was clear and easily understandable and presented in a concise manner.

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