# Data Science Online Training|Online Data Science Training in USA, UK, Canada, Australia, India - PowerPoint PPT Presentation

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

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### A1Trainings best Online Training Institute provides best Data Science online training by our Highly Professional and certified Trainers Live projects in Hyderabad, Bangalore, Chennai, Pune @ 91-7680813158 – PowerPoint PPT presentation

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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

8
Data Extraction
• SQOOP
• Saving and Transporting Object

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
• 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