DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS
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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.Who this course is for:All graduates are eligible to learn this course
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About this course
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
All graduates are eligible to learn this course
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Subjects
- Install
- Programming
- Engineering
- Data Collection
- Statistics
- Data analysis
- SQL
- Java
- Data Mining
- Computing
Course programme
- What is Data Science?
- Who is Data Scientist?
- Who can be Data Scientist?
- Data Science Process
- Modern Data Scientist
- Data Science Workflow
- Technologies used in Data Science
- Data science is a "concept to statistics, data analysis, machine learning and their related methods" in order to "understand and analyze” with data.
- Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
- Data Science is also called as "The Sexiest Job of the 21st Century".
- Data analysis is the process of extracting information from data. It involves multiple stages including establishing a data set, preparing the data for processing, applying models, identifying key findings and creating reports.
- The goal of data analysis is to find actionable insights that can inform decision making.
- Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.
- Statistician + Software Engineer
- A person who is better at statistics than any software engineer or a person who is better at software engineering than any statistician is a data scientist.
- Math & Statistics
- Programming & Database
- Domain Knowledge & Soft Skills
- Communication & Visualization
- Problem definition
- Data Collection & Preparing
- Model Development
- Model Deployment
- Performance Improvement
- R
- Python
- Weka etc.......
- It is similar like Human Learning
- Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
- Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
- Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
- In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
- In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
- R
- Python
- Weka
- Amazon Machine Learning
- Java etc.....
- R is a programming language
- Free software
- Statistical computing, graphical representation and reporting.
- Designed by: Ross Ihaka, Robert Gentleman, Developed at University of Aukland
- Derived from S and S-plus language (commercial product)
- Typing discipline: Dynamic
- Stable release: 3.5.1 ("Feather Spray") / July 2, 2018; 55 days ago
- First appeared: August 1993; 25 years ago
- License: GNU GPL
- Functional based language
- Interpreted programming language
- Distributed by CRAN (Comprehensive R Archive Network)
- Open source product (R-Community)
- Functions are available as a package
- Default packages are already attached to the R-console eg base, utils, stats, graphics etc
- Attach the package to the R-application
- Install Add-on packages from CRAN Mirrors.
- Once goto official website of R i.e., "R" in Google and click on first link (The R Project for Statistical Computing).
- Click on "Download R".
- Click on any one of the CRAN Mirror. Eg: https//cloud.r-project.org
- Click on Download R for Windows.
- Click on Install R for the first time.
- Finally click on Download R 3.5.1 for Windows (32/64 bit).
- R come with a lot of packages.
- By default only some packages will be attached to the R environment.
- library(help="package name")
- help(function name)
- or
- ?function name
- An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations.
- We have the following types of operators in R Programming:
- R is called as a Dynamic typed language, which means that we can change a variable's data type of the same variable again and again when using it in a program.
- Dynamic typed language (No Declaration)
- Returns the internal storage data type.
- a <- 10
- typeof(a)
- double
Additional information
DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS