DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS, available at $44.99, has an average rating of 4, with 86 lectures, based on 59 reviews, and has 483 subscribers.

You will learn about DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R, PYTHON, WEKA and SQL This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python programming, WEKA tool kit and SQL. This course is ideal for individuals who are All graduates are eligible to learn this course. It is particularly useful for All graduates are eligible to learn this course.

Enroll now: DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

Summary

Title: DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

Price: $44.99

Average Rating: 4

Number of Lectures: 86

Number of Published Lectures: 86

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: ₹7,900

Quality Status: approved

Status: Live

What You Will Learn

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R, PYTHON, WEKA and SQL
This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python programming, WEKA tool kit and SQL.

Who Should Attend

All graduates are eligible to learn this course.

Target Audiences

All graduates are eligible to learn this course.

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R Programming, PYTHON Programming, WEKA Tool Kit and SQL.

This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL.

Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis. So we need a programming language which can cater to all these diverse needs of data science. R and Python shines bright as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science.

In this course we will cover these the various techniques used in data science using the R programming, Python Programming, WEKA tool kit and SQL.

The most comprehensive Data Science course in the market, covering the complete Data Science life cycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, programming languages like R programming, Python are covered extensively as part of this Data Science training.

Course Curriculum

Chapter 1: DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

Lecture 1: Introduction to Data Science

Lecture 2: Introduction to Machine Learning

Lecture 3: Introduction to R Programming

Lecture 4: R Installation & Setting R Environment

Lecture 5: Variables, Operators & Data types

Lecture 6: Structures

Lecture 7: Vectors

Lecture 8: Vector Manipulation & SubSetting

Lecture 9: Constants

Lecture 10: RStudio Installation & Lists Part 1

Lecture 11: Lists Part 2

Lecture 12: List Manipulation, Sub-Setting & Merging

Lecture 13: List to Vector & Matrix Part 1

Lecture 14: Matrix Part 2

Lecture 15: Matrix Accessing

Lecture 16: Matrix Manipulation, rep function & Data Frame

Lecture 17: Data Frame Accessing

Lecture 18: Column Bind & Row Bind

Lecture 19: Merging Data Frames Part 1

Lecture 20: Merging Data Frames Part 2

Lecture 21: Melting & Casting

Lecture 22: Arrays

Lecture 23: Factors

Lecture 24: Functions & Control Flow Statements

Lecture 25: Strings & String Manipulation with Base Package

Lecture 26: String Manipulation with Stringi Package Part 1

Lecture 27: String Manipulation with Stringi Package Part 2 & Date and Time Part 1

Lecture 28: Date and Time Part 2

Lecture 29: Data Extraction from CSV File

Lecture 30: Data Extraction from EXCEL File

Lecture 31: Data Extraction from CLIPBOARD, URL, XML & JSON Files

Lecture 32: Introduction to DBMS

Lecture 33: Structured Query Language, MySQL Installation & Normalization

Lecture 34: Data Definition Language Commands

Lecture 35: Data Manipulation Language Commands

Lecture 36: Sub Queries & Constraints

Lecture 37: Aggregate Functions, Clauses & Views

Lecture 38: Data Extraction from Databases Part 1

Lecture 39: Data Extraction from Databases Part 2 & DPlyr Package Part 1

Lecture 40: DPlyr Package Part 2

Lecture 41: DPlyr Functions on Air Quality Data set

Lecture 42: Plylr Package for Data Analysis

Lecture 43: Tidyr Package with Functions

Lecture 44: Factor Analysis

Lecture 45: Prob.Table & CrossTable

Lecture 46: Statistical Observations Part 1

Lecture 47: Statistical Observations Part 2

Lecture 48: Statistical Analysis on Credit Data set

Lecture 49: Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts

Lecture 50: Box Plots

Lecture 51: Histograms & Line Graphs

Lecture 52: Scatter Plots & Scatter plot Matrices

Lecture 53: Low Level Plotting

Lecture 54: Bar Plot & Density Plot

Lecture 55: Combining Plots

Lecture 56: Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot

Lecture 57: Mat Plot, ECDF & Box Plot with IRIS Data set

Lecture 58: Additional Box Plot Style Parameters

Lecture 59: Set.Seed Function & Preparing Data for Plotting

Lecture 60: Q Plot, Violin Plot, Statistical Methods & Correlation Analysis

Lecture 61: ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Analysis

Lecture 62: Data Exploration and Visualization

Lecture 63: Machine Learning, Types of ML with Algorithms

Lecture 64: How Machine Solve Real Time Problems

Lecture 65: K-Nearest Neighbor (KNN) Classification

Lecture 66: KNN Classification with Cancer Data set Part 1

Lecture 67: KNN Classification with Cancer Data set Part 2

Lecture 68: Navie Bayes Classification

Lecture 69: Navie Bayes Classification with SMS Spam Data set & Text Mining

Lecture 70: WordCloud & Document Term Matrix

Lecture 71: Train & Evaluate a Model using Navie Bayes

Lecture 72: MarkDown using Knitr Package

Lecture 73: Decision Trees

Lecture 74: Decision Trees with Credit Data set Part 1

Lecture 75: Decision Trees with Credit Data set Part 2

Lecture 76: Support Vector Machine, Neural Networks & Random Forest

Lecture 77: Regression & Linear Regression

Lecture 78: Multiple Regression

Lecture 79: Generalized Linear Regression, Non Linear Regression & Logistic Regression

Lecture 80: Clustering

Lecture 81: K-Means Clustering with SNS Data Analysis

Lecture 82: Association Rules (Market Basket Analysis)

Lecture 83: Market Basket Analysis using Association Rules with Groceries Data set

Lecture 84: Waikato Environment for Knowledge Analysis (WEKA)

Lecture 85: Analysis & Prediction using WEKA Machine Learning Toolkit

Lecture 86: Python Libraries for Data Science

Instructors

DATAhill Solutions Srinivas Reddy
Data Scientist

Rating Distribution

1 stars: 3 votes
2 stars: 1 votes
3 stars: 7 votes
4 stars: 25 votes
5 stars: 23 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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