3.9 out of 5
3.9
17 reviews on Udemy

Ai/Data Scientist – Python/R/Big Data Master Class 2020

includes Data Science, Machine Learning-R/Python, Big Data-Hive, Flume,Sqoop, Pig and more.(Beginners To Expert Level)
Instructor:
Rupak Roy
143 students enrolled
Analytics For Beginners: Learn the interdisciplinary concepts of analytics with the help of success stories.
Analytics For Beginners: Become familiar of other companies using analytics as a core part of success stories.
Analytics For Beginners: Understand why and how analytics is so important in every profession.
Analytics For Beginners: Do data exploration and manipulation like transpose, remove duplicates, pivot table, manipulate data with time & Filter using Excel
Analytics For Beginners: Explore and perform advance data manipulation like Merge and Unmerge, cells Text To Column Function, Vlookup, Data Visualization, Data Scaling, Consolidation, Conditional Operator If-Else and more.
Analytics For Beginners: Even perform Ai in excel using built-in predictive analytics.
Data Science: Learn the interdisciplinary concepts of data science like Binomial, Poisson, Hyper Geometric, Negative Binomial, Geometric discrete probability distributions also learn and implement Normal distribution and T-distribution of continuous probability distribution.
Data Science: Perform hypothesis testing with Normal Distribution and T-distribution using One-Tail and Two-Tail Directional hypothesis even if the sample size is low or the standard deviation is not available or again if the population distribution is not Normal Distribution.
Data Science: Chi-square Test-Of-Association, Goodness-Of-Fit and more. Follow the program syllabus in our course curriculum to know more in detail.
Data Science: Learn how to perform One-Way and Two Way anova for multiple levels or factors influencing the outcome with and without replication and for count and categorical data using Chi-square Test-Of-Association & Goodness-Of-Fit
Big Data Analytics: Learn the architecture of Hadoop, Map Reduce, YARN, Hadoop Distribution File System, Name node check-pointing, Hadoop Rack Awareness in detail.
Big Data Analytics: Master and perform big data analysis with on-demand big data tools like PIG, HIVE, Impala and automate to stream live data & workflow with Flume and Scoop.
Big Data Analytics: Control parallel processing and create User Define Functions to automate the scripting language without writing a line of code.
Big Data Analytics: Master and perform External Table to share the data among different applications and even partition the table for faster processing. Follow our program syllabus in course curriculum to know more in detail.
R-programming: Learn and master how to manipulate data, impute missing values and visualization using base graphics, ggplot & geo-spatial plots.
R-programming: Learn and perform exploratory analysis and work with different file type & data sources.
Machine Leaning: Learn to master how to create supervised models like linear and logistic regression, support vector machine and more to solve real world problems.
Machine Learning: Also master to create unsupervised models like k-means and hierarchical clustering, decision trees, random forest to automate solutions for real world problems.
Machine Learning: Learn and implement the concepts of Feature Engineering, Principle Component Analysis, Times and more. Follow our program syllabus in course curriculum to know more in detail.
NLP: Learn and master data transformation, create text corpus, remove spare terms with Tm package and manipulate text data using regular expression.
NLP: Perform sentiment analysis to know the negative or the positive response and topic modeling using LDA to identify the topics of 1000 documents without being going through each
NLP: Understand the connection of each words using Network analysis or even cluster the words used to solve problems like search keywords used to arrive on the website. Follow our program syllabus in course curriculum to know more in detail.
Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free
5 Types Regression in 45 lines of code. Full Guide to Linear Regression, Polynomial Regression, Support Vector Regression, Decision Trees Regression, Random Forest Regression and more.
7 Types of Classification using python. Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest classification.
Let’s Develop Artificial Neural Network in 30 lines of code. Simple yet Complete Guide on how to apply ANN for classification
Let’s Develop Artificial Neural Network in 30 lines of code — II. Part — II Simple yet Complete Guide on how to apply ANN for Regression with K-Fold Validation for accuracy over accuracy.
Reinforcement Learning | In 31 Steps. using Upper Confidence Bound(UCB) & Thompson Sampling for Social Media Marketing Campaign Click Through Rate Optimization(using Python & R)
What is PCA and How can we apply Real Quick and Easy Way? Learn how to apply Principal Component Analysis (PCA) using python
What is Supervised Linear Discriminant Analysis(LDA) ~ PCA. Let’s understand and perform supervised dimensionality reduction
What is Kernel PCA? using R & Python. 4 easy line of codes to apply the most advanced PCA for non-linearly separable data.
Association Rule Learning using Apriori and Eclat (R Studio) to predict Shopping Behavior.
Multi-Layer Perception Time Series Apply State of the Art Deep Learning MLP models for predicting sequence of numbers/time series data.
LSTMs for regression. Quick and easy guide to solve regression problems with Deep Learnings’ different types of LSTMs
Uni-Variate LSTM Time Series Forecasting. Apply State Of The Art Deep Learning Time Series Forecasting with the help of this template.
Multi-variate LSTM Time Series Forecasting. Apply state of the art deep learning time series forecasting using multiple inputs together to give a powerful prediction.
Multi-Step LSTM Time Series Forecasting. Apply Advanced Deep Learning Multi-Step Time Series Forecasting with the help of this template.
Grid Search For ML & Deep Learning Models. Full guide to grid search on finding the best hyper parameters for our regular ml models to deep learning models
7 types of Multi*-Classification using python. Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest, Deep Learning and even with Grid Search Multi-Classification.

The Course is Designed from scratch for Beginners as well as for Experts.

*Updated with Bonus: Machine Learning, Deep Learning with Python – Premium Self Learning Resource Pack Free

Learn the skills of tomorrow, the silicon valley way

Focus on extracting insights from data of any form or shape using multitude of statistical disciplines for the purpose of creating new products & services or improve the existing ones by predicting its probability in a event. And as the enormity of data is on the rise, there is a desperate need for professionals with data science skills to get valuable insights on it. According to NYTimes there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 candidates each year.

Why data science is so important?

• Twitter Since 2015, the number of posts increased 45% to more than 850,000 tweets per minute. 

• YouTube usage has more than tripled in the last two years with user uploading 400 hours of new video each minute of every day.

• Instagram users like 2.5 million posts every minute! 

• Google Around 4 million Google searches are conducted worldwide each minute of everyday. 

• Finally, data send and received by mobile internet users 1500 000TB. 
So, with the above examples of how much data gets generated, now how much hidden insights and patterns for accurate future predictions that we can actually achieve by using data science.

According to Forbes, annual demand for Data Scientist jobs for United States itself will increase by 364 million by 2020.
The average salary for a Data Scientist is $113,436.

What are the career progression path for data science professionals?

• Data Scientist: with a vast knowledge of Data Science, with Machine Learning and Business Intelligence tools. Data Scientist stands high as the Everest. 

• Data Analyst: in 2020, the world will generate data 50times more than now and with each day passes by the data generated is infinity and with that to analyze those data, data analyst jobs will never have to see the face of recession. In linkedin itself there are average 400 new jobs for every 12 hour.

• Data Science Trainer: in this present date with a lack of the knowledge of these advance data science techniques gives a vast opportunity to become the fountain of data science for others.

• Business analyst: with the role of defining and managing the business requirements, business analyst takes the lead in every business decision making process of organization.

Program Brochure

1
Machine Learning Making Sense of a Messy World

#the Video is used for Self Motivation

Video Courtesy: Google Ai

2
R-programming Program Brochure
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Machine Learning Program Brochure
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Data Science Program Brochure
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Big Data Program Brochure
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Natural Language Processing (NLP) Program Brochure

Program Syllabus

1
Analytics For Beginners Program Syllabus
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R-programming Program Syllabus
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Machine Learning Program Syllabus
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Data Science Program Syllabus
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Big Data Program Syllabus
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Natural Language Processing (NLP) Program Syllabus

Analytics For Beginners: Welcome To The World Of Analytics

1
Introduction To Analytics
2
Types Of Analytics & The Structure Of Data
3
Types Of Analytics & The Structure Of Data - II
4
Summary Statistics

Analytics For Beginners: Data Exploration And Manipulation

1
Transpose and Remove Duplicates
2
Remove Duplicates and Data Dictionary
3
Pivot Table and Filter
4
Manipulate Data with Time and Filter

Analytics For Beginners: Data Manipulation - II

1
Merge and Un-merge cells
2
Text To Column Function
3
Vlookup
4
Data Visualization
5
Data Scaling
6
Data Consolidation
7
Conditional Operator If-Else

Analytics For Beginners: Advance Analytics

1
Regression Analysis
2
Congrats! Here's what's next... using Excel
3
More resources for you

Ai To Restore Work Life Balance, Tokyo Japan

1
Ai To Restore Work Life Balance, Tokyo Japan

#the Video is used for Self Motivation

Video Courtesy: Google Ai

Introduction to R and R-Studio

1
Introduction

2.R Program: Data types and Structures in R

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2.Data types and Structures in R
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2.Data types and Structures in R(Lab.)

3.R Program: Import and Export

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3.1Import Data in R
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3.2Export Data
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3.Import-Export (Lab.)

4.R Program: Import-Export Big Data

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4.Import and Export Big Data using R
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4.Import and Export Big Data using R(Lab.)

5.R Program: Import-Export Excel files

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5.1.XLConnect
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5.2.Troubleshooting XLConnect
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5.3.OpenXLSX
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5.Import and Export excel files using openxlsx(lab.)

6.R Program: Import using RODBC

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6.Import Database Data using RODBC
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6.Import Database Data using RODBC(Lab.)

7.R Program: Import Web Data

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7.Import Web Data
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7.Import Web Data(Lab.)

8.R Program: Manipulating Data

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8.Manipulating Data Using base R package
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8.Manipulating Data Using base R package(Lab.)

9.R Program: Manipulating Data using DPLYR

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9.Manipulate Data using DPLYR()

10.R Program: Manipulating Dates

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10.Manipulating Dates
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10.Manipulating Dates (Lab.)

11.R Program: Merging Tables

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11.Merging Tables
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11.Merging Tables (Lab.)

12.R Program: Missing Value Treatment

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12.Missing Value Treatment
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12.Missing Value Treatment(Lab.)

13.R Program: Transpose and Pattern Matching Replacement

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13.Transpose and Pattern Matching Replacement
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13.Transpose and Pattern Matching Replacement(Lab.)

14.R Program: Data Visualization

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14.1.Data Visualization using base graphics
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14.2.Data Visualization using the Grammar of Graphics
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14.3.Data Visualization with multiple groups using ggplot2
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14.4.Data Visualization using case study(Lab.)

15.R Program: Geo-Spatial Plots

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15.Geo-Spatial Plots
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15.Geo-Spatial Plots(Lab.)

Introduction To Machine Learning

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Machine Learning Solving Problems Big, Small, and Prickly

#the Video is used for Self Motivation

Video Courtesy: Google Ai

2
1 Introduction To Machine Learning
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2.1 Data Preparation - Analytics Methodology
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2.2 Impute Missing Values for Continuous/Categorical variables
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2.3 Create Train and Test Data set

Linear Regression

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3.1 Regression Anlaysis
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3.2 Linear Regression Part- I
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3.2 Linear Regression Part - II
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3.2 Linear Regression Part - III
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3.3 Handling Singularity Issue
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3.4 Linear Regression Lab. Part - I
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3.4 Linear Regression Lab. Part - II
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Includes

17 hours on-demand video
16 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion