- Introduction to Data Science
- Introduction to Machine Learning
- Introduction to R Programming
- R Installation & Setting R Environment
- Variables, Operators & Data types
- Structures
- Vectors
- Vector Manipulation & SubSetting
- Constants
- RStudio Installation & Lists Part 1
- Lists Part 2
- List Manipulation, Sub-Setting & Merging
- List to Vector & Matrix Part 1
- Matrix Part 2
- Matrix Accessing
- Matrix Manipulation, rep function & Data Frame
- Data Frame Accessing
- Column Bind & Row Bind
- Merging Data Frames Part 1
- Merging Data Frames Part 2
- Melting & Casting
- Arrays
- Factors
- Functions & Control Flow Statements
- Strings & String Manipulation with Base Package
- String Manipulation with Stringi Package Part 1
- String Manipulation with Stringi Package Part 2 & Date and Time Part 1
- Date and Time Part 2
- Data Extraction from CSV File
- Data Extraction from EXCEL File
- Data Extraction from CLIPBOARD, URL, XML & JSON Files
- Introduction to DBMS
- Structured Query Language, MySQL Installation & Normalization
- Data Definition Language Commands
- Data Manipulation Language Commands
- Sub Queries & Constraints
- Aggregate Functions, Clauses & Views
- Data Extraction from Databases Part 1
- Data Extraction from Databases Part 2 & DPlyr Package Part 1
- DPlyr Package Part 2
- DPlyr Functions on Air Quality Data set
- Plylr Package for Data Analysis
- Tidyr Package with Functions
- Factor Analysis
- Prob.Table & CrossTable
- Statistical Observations Part 1
- Statistical Observations Part 2
- Statistical Analysis on Credit Data set
- Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
- Box Plots
- Histograms & Line Graphs
- Scatter Plots & Scatter plot Matrices
- Low Level Plotting
- Bar Plot & Density Plot
- Combining Plots
- Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
- Mat Plot, ECDF & Box Plot with IRIS Data set
- Additional Box Plot Style Parameters
- Set.Seed Function & Preparing Data for Plotting
- Q Plot, Violin Plot, Statistical Methods & Correlation Analysis
- ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Analysis
- Data Exploration and Visualization
- Machine Learning, Types of ML with Algorithms
- How Machine Solve Real Time Problems
- K-Nearest Neighbor (KNN) Classification
- KNN Classification with Cancer Data set Part 1
- KNN Classification with Cancer Data set Part 2
- Navie Bayes Classification
- Navie Bayes Classification with SMS Spam Data set & Text Mining
- WordCloud & Document Term Matrix
- Train & Evaluate a Model using Navie Bayes
- MarkDown using Knitr Package
- Decision Trees
- Decision Trees with Credit Data set Part 1
- Decision Trees with Credit Data set Part 2
- Support Vector Machine, Neural Networks & Random Forest
- Regression & Linear Regression
- Multiple Regression
- Generalized Linear Regression, Non Linear Regression & Logistic Regression
- Clustering
- K-Means Clustering with SNS Data Analysis
- Association Rules (Market Basket Analysis)
- Market Basket Analysis using Association Rules with Groceries Data set
- Waikato Environment for Knowledge Analysis (WEKA)
- Analysis & Prediction using WEKA Machine Learning Toolkit
- Python Libraries for Data Science
- Home
- Simpliv Learning
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- Data Science With Machine Learning And data Analytics
Data Science With Machine Learning And data Analytics
Learn the data science using R Programming, PYTHON Programming, WEKA Tool Kit and SQL by joining this programme by Simpliv Learning
Online
72 Hours
$ 9 49
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Data Science With Machine Learning And data Analytics course is an online short programme created by DATAhill Solutions Srinivas Reddy that will explore data science using the techniques of Python Programming, R programming, SQL and WEKA tool kit. The course is designed with graduates who are interested in learning data science and Software Professionals in mind. The curriculum will go through all the topics of data science with machine learning and data analytics such as Data Collection, Feature Engineering, Data Integration, Text Mining, Regression Modelling etc.
Data Science With Machine Learning And data Analytics online course, administered by Simpliv Learning, is open for all graduates and demand the prior knowledge of coding in the programming languages of R and Python, apply any python IDE or R IDE and implementation of R or Python programmes. Basic knowledge of programming and mathematical concepts also are needed before starting the programme.
Data Science With Machine Learning And data Analytics certification also provides the learners with unlimited access to the learning materials including the video lecture all the lifetime with one-time payment access. The participants will be awarded a certificate at the end of the course boosting their career opportunities. The interested students can choose the programme by making the payment of $ 49.9 as a fee.
The highlights
- Online course
- 20-Day Money-Back Guarantee
- Learn at your own pace
- Lifetime Access
- Certificate on Completion
- Access on Android and iOS App
Program offerings
- Certificate on completion
- Access on android and ios app 86 lectures
- Instruction in english
- Certification of completion
- 72+ hours completion time
- Lifetime access to content
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After the completion of Data Science With Machine Learning And data Analytics online certification, the learners will be able to gain a detailed understanding of data science with data analytics and machine learning making use of SQL, WEKA, Python, and R. Plus the participants will also obtain the knowledge of data science with machine learning.