Data Science – End 2 End Beginners Course Part 1

Data Science – End 2 End Beginners Course Part 1, available at $54.99, has an average rating of 4.75, with 80 lectures, based on 4 reviews, and has 63 subscribers.

You will learn about Part 1 is a Beginner’s course that covers Machine Learning and Data Analytics Objective is to teach students how to do an End-2-End data science project From problem definition, data sourcing, wrangling, modelling, analyzing and visualizing to deploying and maintaining Part 1 will cover all the basics required for building machine learning models – programming, analytics, maths, process, algorithms and deployment It will provide full maths and logic details for all algorithms Programming (python) and Data analytics (pandas) Maths, Statistics and Probability basics required for understanding the different algorithms Data Science Process – Problem, Wrangling, Algorithm Selection, Model Building , Visualization, Deployment Data Wrangling Build Machine Learning models – Supervised & Unsupervised algorithms using Regression, Classification & Clustering How to Visualize and Evaluate models Model Persistence and Deployment using joblib and flask, Deploying on AWS Cloud using S3 and Elastic Beanstalk, Using AWS Sagemaker End 2 End Project – Building a RoboAdvisor – multi-asset portfolio using global assets and macroeconomic data Detailed python code and data is provided to explain all concepts and algorithms Use popular libraries like scikit-learn, xgboost, numpy, matplotlib, seaborn, joblib, flask, etc This course is ideal for individuals who are This course is for anyone interested in learning data science or From beginners to intermediate level users or Analyst, programmer, non-technical professional, student, etc or Data Analysts, Machine Learning engineers, Data Engineers, Business Analysts who want to become Data Scientists It is particularly useful for This course is for anyone interested in learning data science or From beginners to intermediate level users or Analyst, programmer, non-technical professional, student, etc or Data Analysts, Machine Learning engineers, Data Engineers, Business Analysts who want to become Data Scientists.

Enroll now: Data Science – End 2 End Beginners Course Part 1

Summary

Title: Data Science – End 2 End Beginners Course Part 1

Price: $54.99

Average Rating: 4.75

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Part 1 is a Beginner’s course that covers Machine Learning and Data Analytics

Objective is to teach students how to do an End-2-End data science project

From problem definition, data sourcing, wrangling, modelling, analyzing and visualizing to deploying and maintaining

Part 1 will cover all the basics required for building machine learning models – programming, analytics, maths, process, algorithms and deployment

It will provide full maths and logic details for all algorithms

Programming (python) and Data analytics (pandas)

Maths, Statistics and Probability basics required for understanding the different algorithms

Data Science Process – Problem, Wrangling, Algorithm Selection, Model Building , Visualization, Deployment

Data Wrangling

Build Machine Learning models – Supervised & Unsupervised algorithms using Regression, Classification & Clustering

How to Visualize and Evaluate models

Model Persistence and Deployment using joblib and flask, Deploying on AWS Cloud using S3 and Elastic Beanstalk, Using AWS Sagemaker

End 2 End Project – Building a RoboAdvisor – multi-asset portfolio using global assets and macroeconomic data

Detailed python code and data is provided to explain all concepts and algorithms

Use popular libraries like scikit-learn, xgboost, numpy, matplotlib, seaborn, joblib, flask, etc

Who Should Attend

This course is for anyone interested in learning data science

From beginners to intermediate level users

Analyst, programmer, non-technical professional, student, etc

Data Analysts, Machine Learning engineers, Data Engineers, Business Analysts who want to become Data Scientists

Target Audiences

This course is for anyone interested in learning data science

From beginners to intermediate level users

Analyst, programmer, non-technical professional, student, etc

Data Analysts, Machine Learning engineers, Data Engineers, Business Analysts who want to become Data Scientists

This is a Beginner’s course that covers basic Machine Learning and Data Analytics concepts

The Objective of this course is to teach students how to do an End-2-End data science project

From Problem definition, data sourcing, wrangling and modelling

To analyzing, visualizing and deploying & maintaining the models

It will cover the main principles/tools that are required for data science

This course is for anyone interested in learning data science – analyst, programmer, non-technical professional, student, etc

Having seen available data science courses and books, we feel there is a lack of an End 2 End approach

Quite often you learn the different algorithms but don’t get a holistic view, especially around the process and deployment

Also, either too much or limited mathematical details are provided for different algorithms

The course will cover all the basics in programming, maths, statistics and probability required for building machine learning models

Throughout the course detailed lectures covering the maths and logic of the algorithms, python code examples and online resources are provided to support the learning process

Students will learn how to build and deploy machine learning models using tools and libraries like anaconda, spyder, python, pandas, numpy, scikit-learn, xgboost, matplotlib, seaborn, joblib, flask, AWS Cloud S3, Elastic Beanstalk and Sagemaker

More details are available on our website – datawisdomx

Course material including python code and data is available in github repository – datawisdomx, DataScienceCourse

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction, Course Structure

Lecture 2: Data Science – Why, What, How

Lecture 3: Guidelines and Expectation

Chapter 2: Chapter 1 – Programming – Python

Lecture 1: Why, How, Basic syntax

Lecture 2: Data Types – Numbers

Lecture 3: Data Types – Variables

Lecture 4: Data structures – List, Array

Lecture 5: Data structures – Sets, Tuples, Dictionary

Lecture 6: Data Types – Strings

Lecture 7: Operators

Lecture 8: Control Flow Statements

Lecture 9: Functions, Error handling

Lecture 10: Read/Write – Console, File, Database

Lecture 11: Object Oriented Programming OOPs

Lecture 12: Modules and Packages

Lecture 13: Sort Algorithm, Recursion, Regex

Chapter 3: Chapter 2 – Data Analytics using Pandas

Lecture 1: Pandas Introduction

Lecture 2: Data Structures – Series, DataFrame

Lecture 3: View, Slicing, Functions

Lecture 4: Missing data, Merge

Lecture 5: Text data, Iteration, Sorting

Lecture 6: Groupby, Casting, Conversion

Lecture 7: Time Series, Sampling

Lecture 8: Read/Write, Plot data

Lecture 9: SQL Operations

Chapter 4: Chapter 3 – Statistics

Lecture 1: Introduction

Lecture 2: Descriptive – Central tendency, Variability

Lecture 3: Descriptive – Distributions

Lecture 4: Descriptive – Skewness, Kurtosis

Lecture 5: Descriptive – Percentile, Range, Boxplot

Lecture 6: Inferential – Estimation, Intervals

Lecture 7: Inferential – Hypothesis Test

Lecture 8: Bivariate Analysis – Correlation, Covariance

Lecture 9: Bivariate Analysis- Regression

Lecture 10: Exponentials, Logarithms

Chapter 5: Chapter 4 – Probability

Lecture 1: Introduction

Lecture 2: Basics

Lecture 3: Conditional probability, Law of Total probability

Lecture 4: Bayes Theorem

Lecture 5: Central Limit Theorem, Probability Mass Function

Chapter 6: Data Sampling

Lecture 1: Data Sampling

Chapter 7: Data Science Process

Lecture 1: Introduction

Lecture 2: Business Problem, EDA, Solutions

Lecture 3: Algorithm (Model) Selection

Lecture 4: Data Wrangling, Sample Splitting, Standardizing

Lecture 5: Model Building, Evaluation and Selection

Lecture 6: Data Visualization, Model Deployment, Retraining and Redeployment

Chapter 8: Data Wrangling

Lecture 1: Data Wrangling

Chapter 9: Supervised Learning Algorithms – Regression

Lecture 1: Regression – Introduction, Error Metrics and Bias-Variance Tradeoff

Lecture 2: Linear Regression

Lecture 3: Multiple Linear Regression

Lecture 4: Polynomial Regression

Lecture 5: Regularization Regression

Lecture 6: Decision Tree Regression

Lecture 7: Ensemble Algorithms

Lecture 8: Random Forest Regression

Lecture 9: XGBoost Regression

Lecture 10: Using GridSearchCV for Regression algorithms

Chapter 10: Supervised Learning Algorithms – Classification

Lecture 1: Classification – Introduction, Error Metrics and Sample Data

Lecture 2: Logistic Regression Classification

Lecture 3: K-Nearest Neighbors Classification

Lecture 4: SVM – Support Vector Machines Classification

Lecture 5: Naive Bayes Classification

Lecture 6: Decision Tree and Ensemble Classification

Lecture 7: Multiclass Classification using SVC

Lecture 8: Using GridSearchCV for Classification

Chapter 11: Unsupervised Learning Algorithms – Clustering

Lecture 1: Clustering Algorithms – Introduction and Error Metrics

Lecture 2: Centroid based K-Means Clustering

Lecture 3: Connectivity based Hierarchical Clustering

Lecture 4: Density based DBSCAN Clustering

Lecture 5: Distribution based Gaussian Mixture Clustering

Chapter 12: Model Persistence and Deployment

Lecture 1: Introduction

Lecture 2: Persist and Deploy model files using joblib and Flask

Lecture 3: Deploy on AWS cloud using Boto3 (python SDK)

Lecture 4: Deploy on AWS cloud using Elastic Beanstalk

Lecture 5: Build and Deploy using AWS Sagemaker – ML cloud platform

Chapter 13: End 2 End Project – Building a RoboAdvisor

Lecture 1: Introduction and Model Logic

Lecture 2: Code explanation

Lecture 3: Conclusion and Results

Chapter 14: Course Conclusion, References, Data Sources, Contact Us

Lecture 1: Course Conclusion, Data Source References and Contact Us

Instructors

Nitin Singhal
Data Science, Product Development, Digital platforms Founder

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5 stars: 3 votes

Frequently Asked Questions

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