Data Science using R

  1. Introduction
  2. Objective of BASE SAS Program
  3. Why SAS
  4. Program editor window
  1. Creating a SAS Programs
  2. Submitting SAS Program
  3. Reading SAS Log
  4. Browsing the Data Portion
  5. Components of SAS Programs
  1. List input
  2. Column input
  3. Formatted input
  4. Column Input
  5. Format Input
  6. Date and Time Input
  1. Arithmetic Functions
  2. Character Functions
  3. Date Functions
  4. Subsetting a SAS Data Set
  5. Creating More Than One Subset Data Set in One DATA Step
  6. Adding Observations to a SAS Data Set

Introduction to R

  1. Vectors
  2. Matrices
  3. Dataframes
  4. Lists
  1. Numeric Functions
  2. Character Functions
  3. Date Functions
  4. User Defined Functions
  1. Computing New Variables
  2. Duplicate Values
  3. Sorting
  1. Labelling
  2. Numeric Formatting
  3. Character Formatting
  4. Date Formatting
  1. Merge
  2. Append
  1. If-else statements
  2. For loops
  3. While loops
  4. Repeat, Next, Break
  1. Scatterplots, Histograms, Barcharts, Dotplots
  2. Labels, Legends, Titles, Axes
  1. Introduction
  2. Need of Machine Learning
  3. Difference between Statistics and Machine Learning
  4. Popular libraries in Machine Learning
  1. Basic Statistics
  2. Hypothesis Testing
  3. T Test, Z Test, F Test
  4. Correlation
  5. Hypothesis Testing
  6. ANOVA, MANOVA, ANCOVA
  1. Linear regression
    1. Introduction
    2. Assumption Testing
    3. Interpretation
    4. Validation
    5. Implementation
  2. Logistic regression
    1. Introduction
    2. Difference between linear and logistic
    3. Variable Selection Methods
    4. Interpretation
    5. Validation
  3. Cluster Analysis
    1. Introduction
    2. Hierarchal Clustering
    3. K-Means Clustering
    4. Finalization of Clusters
    5. Interpretation
    6. Output Validation
  4. Time Series
    1. Introduction
    2. Components
    3. Methods of Forecasting
    4. ARIMA/ARMA
    5. Interpretation
    6. Validation
    7. Forecasting
  5. Decision Tree
    1. Introduction
    2. Pruning of Trees
    3. CHAID
    4. CART
    5. C
    6. Interpretation
    7. Ensemble Method
  6. Random Forest
    1. Introduction
    2. Random Forest for Regression
    3. Random Forest for Classification
    4. Interpretation
    5. Validation
  7. Text Analytics & Word Cloud
    1. Tokenization
    2. Term Document Matrix
    3. Stemming
    4. Corpus
    5. Different Text Cleansing Method
  8. Sentimental Analysis using Twitter Data
    1. Introduction
    2. Setting Twitter Development Login and account
    3. Generating API and Secret Keys
    4. Extracting Data from Twitter
    5. Library for Positive and Negative Keywords
    6. Text Cleansing
    7. Classification of Negative and Positive Keywords

Introduction to Tableau

  1. Introduction
  2. Prepare Files
  1. Deep Dive into Tableau
  2. Getting Data from different Sources
  3. Tableau Joins
  4. MetaData
  5. Data Blening
  6. Hierarchy and Groups
  1. Dual Axis Graphs
  2. Histogram
  3. Pareto Chart
  4. Motion Chart
  5. Box Plot
  6. Funnel Chart
  7. Waterfall Charts
  1. Numeric Calculations
  2. String Calculations
  3. Date Calculations
  4. User Defined Calculations
  5. Logical Calculations

Introduction to Advance Excel

  1. Use the Function Wizard, Common functions (AVERAGE, MIN, MAX, COUNT,COUNTA, ROUND, INT,LEN,TRIM)
  2. Nested functions , Name cells /ranges /constants
  3. Relative, Absolute, Mixed cell references : >,<,= operators
  4. Logical functions using IF, AND, OR, NOT
  5. The LOOKUP function , Date and time functions , Annotating formulas
  1. Sub Total Reports, Auto Filter
  2. Password Protecting Worksheets
  3. Linking Moltiple Sheets
  4. Sheet Referencing
  5. Linking Between Word/Excel/Ppt
  6. What-if-analysis, GOAL SEEK
  7. Pivot Tables, NESTED IF
  1. Create charts , Enhance charts, Drawing toolbar features
  2. Interactive charts
  3. Dashboards in Excel
Course Detail
Fees:
Rs 24,000

 

Class Mode:
Classroom/Online

 

Duration:
3 Months

 

Course Contents:
S-A-S Script, R, Predictive Analytics, Machine Learning

 

Course Benefits:
Student Will able to use S-A-S Script, R, Predictive Analytics
and perform Analytics

 

RS 35,000

5 Months

Data Science Pro

Data Science with SAS, R & Python

RS 29,000

3 Months

Data Science with using Python

Data Science with SAS, R, Python

KEY FEATURES

3 Months of Classroom Training.

 

Session have assignments and daily task.

 

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

 

Interview Questions for related topic.

 

Used Case Study and Projects to understand real scenarios.

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Data
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Machine
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Artificial
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Python
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R
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Data
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