Datascience Online Training

Who's Using Datascience?

Datascience intrioduces common datascience theory and techniques to help programmers, technical professionals and mathematicians expand their datascience

What you will learn Data Science

  • Certified and expert teachers
  • Extensive doumentation provided
  • Became an exepert few days

DATASCIENCE

Data science simply defines as organizing, packing and delivering the data, this is known as OPD process. In organizing the structure and physical location of data is planned. In packing the visualization of data is created. In delivering the exact value of data is obtained. Data Science is simple moving people systems between current and new technologies.

COURSE OBJECTIVES:

  • Getting started with GITHUB
  • Getting started with R
  • Data Extraction , preparation and manipulation
  • Principal of analytical graph
  • Predictive models and machine learning algorithm
  • Text mining, Natural language processing
  • Social network analysis
  • Data science in detail
  • WHO SHOULD LEARN DATA SCIENCE
  • Data analysts
  • Data base developers
  • Freshers who have the knowledge of Database

  • PRE-REQUISITES FOR THE DATA SCIENCE

    Having the knowledge of database will help in learning data science.A1 Trainers provides all the basics of data base .our trainers have 8+ years of experience in software and in teaching field. They guide you for sample live project development during the course.


    Course Details:
    Getting Started with Github
    Introduction to Git
    Introduction to Github
    Creating a Github Repository
    Basic Git Commands
    Basic Markdown

    Getting Started with R
    Overview of R
    R data types and Objects
    Getting Data In and Out of R
    Subsetting R Objects
    Dates and Times

    Getting Started with R
    Control structures
    Functions
    Scoping rules of R
    Coding Standards for R
    Dates and times

    Getting Started with R
    Loop Functions
    Vectorizing a Function
    Debugging
    Profiling R Code
    Simulation

    Data Extraction, Preparation and Manipulation ( R, MYSQL, HDFS, HIVE and SQOOP
    Data Extraction
    Downloading Files
    Reading Local Files
    Reading Excel Files
    Reading JSON
    Reading XML
    Reading From WEB
    Reading From API

    Data Extraction
    Reading From HDFS
    Reading From MYSQL
    SQOOP
    Reading FROM HIVE
    Saving and Transporting Object
    Reading Complex Structure

    Data Preparation
    Subsetting and Sorting
    Summarizing Data
    Creating New Variable
    Regular Expression
    Working With Dates

    Data Manipulation
    Managing DataFrame with dplyr package
    Reshaping Data
    Merging Data

    Descriptive Statistics
    Univariate Data and Bivariate Data
    Categorical and Numerical Data
    Frequency Histogram and Bar Charts
    Summarizing Statistical Data
    Box Plot, Scatter Plot, Bar Plot, Pie Chart

    Probability
    Conditional Probability
    Bayes Rule
    Probability Distribution
    Correlation vs Causation
    Average
    Variance
    Outliers
    Statistical Distribution
    Binomial Distribution
    Central Limit Theorem
    Normal Distribution
    68-95-99.7 % Rule
    Relationship Between Binomial and Normal Distribution

    Hypothesis Testing
    Hypothesis Testing
    Case Studies

    Inferential Statistics
    Testing of Hypothesis
    Level of Significance
    Comparison Between Sample Mean and Population Mean
    z- Test
    t- Test

    ANOVA (f- Test)
    ANCOVA
    MANOVA
    MANCOVA

    Regression and Correlation
    Regression
    Correlation
    CHI-SQUARE
    Principal Of Analytic Graph

    Introduction to ggvis
    Exploratory and Explainatory
    Design Principle
    Load ggvis and start to explore
    Plotting System in R
    ggvis - graphics grammar

    Lines and Syntax
    Properties for Lines
    Properties for Points
    Display Model Fits

    Transformations
    ggvis and dplyr

    HTML WIDGET
    Geo-Spatial Map
    Time Series Chart
    Network Node

    Predictive Models and Machine Learning Algorithm - Supervised Regression
    Regression Analysis
    Linear Regression
    Non- Linear Regression
    Polynomial Regression
    Curvilinear Regression

    Multiple Linear Regression
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Logistic Regression
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance
    Time Series Forecast

    Predictive Models and Machine Learning Algorithm - Supervised Classification
    Naïve Bayes
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Support Vector Machine
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Random Forest
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    K- Nearest Neighbors
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Classification and Regression Tree (CART)
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Predictive Models and Machine Learning Algorithm - Unsupervised
    K Mean Cluster
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Apriori Algorithm
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Case Study : Customer Analytic - Customer Lifetime Value
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Text Mining, Natural Language Processing and Social Network Analysis
    Natural Language Processing
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Social Network Analysis
    Collect Data
    Explore and Prepare the data
    Train a model on the data
    Evaluate Model Performance
    Improve Model Performance

    Capstone Project
    Saving R Script
    Scheduling R Script

    RIZE Trainings offers wide range of courses belonging to business, IT, students, developers, data and working professionals, freshers etc; We regulate training about your expected courses and help you to advance your career. RIZE come up with numerous courses and offer training throughout the world like France, United states, China, India, Singapore, Germany, Australia, Canada etc; by using interface like webex and GoToMeeting.

    RIZE trainers assist in developing Real time experience of the required course and also improving interview skills, knowledge, communication, content of the course, innovative project ideas, future scope etc,. This makes our students are becoming future experts and working as professionals in top rated companies.

    The course begins in a few days, and I still do not have my login information. What do I do?
    Once your registration for a course is completed, we will send activation link to start your sessions.

    What happens if I miss my training date, do I have any options?
    In case you miss training date, we will send recorded sessions to you. Otherwise, we assign for next live batch.

    How does the online training work?
    All of our online courses are live instructor led online courses. You will have the ability to interact directly with the trainer. Once registered for a class you will receive detailed instructions on how to access your class.

    How do I receive the course materials?
    You receive a link to download the course materials when you register. Our trainers assist you in your assessments, case studies, sample projects, interview skills etc;

    Will I be able to view the sessions again at a later time?
    Yes, We provide recorded sessions.

    Who are your trainers?
    All our trainers are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts. So that participants get a great learning experience.

    Can I get the same in-class experience at home or work?
    Yes. You can attend courses anywhere that has an Internet connection. If you choose to participate from your home or workplace, you must validate your own equipment to ensure it meets the required specifications.

    What should I bring to class?
    You need to have your course materials, Internet, computer headset and the Link to your Rizetrainings classroom e-mail, which contains the link and LOGIN credentials you need to take your course.

    Where can I get more information?
    If you need more information, please call us at +91-970 39 767 53 or send mail to contact@rizetrainings.com . Rize Trainings Advisors will be happy to assist you.

    Do you offer placements?
    We help our customers for preparing their resumes, work on sample live projects and provide assistance for interview preparation. We don't offer any placements however if you go through the course diligently and complete the project you will have a very good hands on experience to work on a Live project.

    What happens if I have technical problems during my class?
    If you have technical difficulties, you call us at +91-970 39 767 53 . Our support team will resolve any issues you might encounter.

    ENQUIRY FORM HERE :
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