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Data Science Online Training|Online Data Science Training in USA, UK, Canada, Australia, India

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Title: Data Science Online Training|Online Data Science Training in USA, UK, Canada, Australia, India


1
A1Trainings
  • Data Science Online TrainingOnline Data Science
    Training in USA, UK, Canada, Australia, India

2
DATA SCIENCE COURSE CONTENT
3
Data Science Introduction and Toolbox Getting
Started with Github
  • Introduction to Git
  • Introduction to Github
  • Creating a Github Repository
  • Basic Git Commands
  • Basic Markdown

4
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

5
Getting Started with R
  • Control structures
  • Functions
  • Scoping rules of R
  • Coding Standards for R
  • Dates and times

6
Getting Started with R
  • Loop Functions
  • Vectorizing a Function
  • Debugging
  • Profiling R Code
  • Simulation

7
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

8
Data Extraction
  • Reading From HDFS
  • Reading From MYSQL
  • SQOOP
  • Reading FROM HIVE
  • Saving and Transporting Object
  • Reading Complex Structure

9
Data Preparation
  • Subsetting and Sorting
  • Summarizing Data
  • Creating New Variable
  • Regular Expression
  • Working With Dates

10
Data Manipulation
  • Managing DataFrame with dplyr package
  • Reshaping Data
  • Merging Data

11
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

12
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

13
Hypothesis Testing
  • Hypothesis Testing
  • Case Studies

14
Inferential Statistics
  • Testing of Hypothesis
  • Level of Significance
  • Comparison Between Sample Mean and Population
    Mean
  • z- Test
  • t- Test

15
ANOVA (f- Test)
  • ANCOVA
  • MANOVA
  • MANCOVA

16
Regression and Correlation
  • Regression
  • Correlation
  • CHI-SQUARE

17
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

18
Lines and Syntax
  • Properties for Lines
  • Properties for Points
  • Display Model Fits

19
Transformations
  • ggvis and dplyr

20
HTMLWIDGET
  • Geo-Spatial Map
  • Time Series Chart
  • Network Node

21
Predictive Models and Machine Learning Algorithm
- Supervised Regression Regression Analysis
  • Linear Regression
  • Non- Linear Regression
  • Polynomial Regression
  • Curvilinear Regression

22
Multiple Linear Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

23
Logistic Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

24
Time Series Forecast
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

25
Predictive Models and Machine Learning Algorithm
- Supervised ClassificationNaïve Bayes
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

26
Support Vector Machine
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

27
Random Forest
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

28
K- Nearest Neighbors
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

29
Classification and Regression Tree (CART)
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

30
Predictive Models and Machine Learning Algorithm
- UnsupervisedK Mean Cluster
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

31
Apriori Algorithm
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

32
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

33
Text Mining, Natural Language Processing and
Social Network AnalysisNatural Language
Processing
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

34
Social Network Analysis
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

35
Capstone Project
  • Saving R Script
  • Scheduling R Script

36
Contact Info
  • Address Madhapur, Hyderabad.
  • Email contact_at_a1trainings.com
  • Call us 7680813158
  • Web www.a1trainings.com
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