Why Choose getitcore

getitcore ONLINE TRAINING FEATURES

• Real-Time Expert Trainers (more than 10 years experience in particular
• technology)
• Flexible timings
• Do not worry about your timings because we are always with your timings.
• Industry Specific Scenarios
• Students are provided with all the Real-Time and Relevant Scenarios. With Real time workshops
• live online Training courses
• Industry Specific Scenarios
• Video Recording Sessions
• Soft Copy of Materials
• Resume Preparation for interviews
• Interview Preparation Tips
• 100 %Free job assistance

Online Training Features

1. Real-Time Expert Trainers

We believe to provide our students the Best interactive experience as part of their learning

Flexible timings

Do not worry about your timings because we are always with your timings.

Industry Specific Scenarios

Students are provided with all the Real-Time and Relevant Scenarios.  With Real time workshops

• Live online Training courses
• Real-Time Expert Trainers
• Industry Specific Scenarios
• Video Recording Sessions
• Soft Copy of Materials
• Resume Preparion for interviews
• Interview Preparation Tips interviews
• 100 %Free job assistance

Who can do this course

This course is also suitable for graduation of (UG and PG) university and as well as who is having passion in system knowledge, Business and would like to enter this course.

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What does it covers

After course completion

• Explain and use the basic functions of technology
• Make the necessary settings for consumption-based planning
• Utilize procurement optimization techniques
• Release procedures
• Process invoices and manage discrepancies
• Enter goods movements in the system and make the relevant settings for special function.

Job Opportunities

• (After passing the certification exam)
• Business Process Owner
• Team Lead
• Power User and End User

Certification Exam

• This course will lead you towards the following certification
• Associate Level
• 80 questions for the exam
• 3 Hours duration
• Passing Score is 80%

Course Content

Data Science training in india.
Data Science training in India has become one of the most popular courses, due to demand in innovation of existing jobs. getitcore, India offers you complete training in data science course your aim towards becoming a Data Scientist. As the technological area is growing so are the new fields in IT in sector growing. And specially when coming to the profession of data scientist it has got the demand on course.
Data Science Course at getitcore, India ensures to provide the training with top industry experts and well-trained data scientist who will help you throughout the completion of your data science course.
Data Science training in India and also training uncountable batches for years, getitcore has always been forward to come up with new courses for the learners.

Introduction to Data Analytics
Introduction to Business Analytics
Understanding Business Applications
Data types and data Models
Type of Business Analytics
Evolution of Analytics
Data Science Components
Data Scientist Skillset
Univariate Data Analysis
Introduction to Sampling

Data Handling in R Programming
Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
Sub-setting Data
Selecting (Keeping) Variables
Excluding (Dropping) Variables
Selecting Observations and Selection using Subset Function
Merging Data
Sorting Data
Adding Rows
Visualization using R
Data Type Conversion
Built-In Numeric Functions
Built-In Character Functions
User Built Functions
Control Structures
Loop Functions
Introduction to Statistics
Basic Statistics
Measure of central tendency
Types of Distributions
Anova
F-Test
Central Limit Theorem & applications
Types of variables
Relationships between variables
Central Tendency
Measures of Central Tendency
Kurtosis
Skewness
Arithmetic Mean / Average
Merits & Demerits of Arithmetic Mean
Mode, Merits & Demerits of Mode
Median, Merits & Demerits of Median
Range
Concept of Quantiles, Quartiles, percentile
Standard Deviation
Variance
Calculate Variance
Covariance
Correlation
Introduction to Statistics – 2
Hypothesis Testing
Multiple Linear Regression
Logistic Regression
Market Basket Analysis
Clustering (Hierarchical Clustering & K-means Clustering)
Classification (Decision Trees)
Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)
Introduction to Machine Learning
Overview & Terminologies
What is Machine Learning?
Why Learn?
When is Learning required?
Data Mining
Application Areas and Roles
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement learning
Machine Learning Concepts & Terminologies
Steps in developing a Machine Learning application

Key tasks of Machine Learning
Modelling Terminologies
Learning a Class from Examples
Probability and Inference
PAC (Probably Approximately Correct) Learning
Noise
Noise and Model Complexity
Triple Trade-Off
Association Rules
Association Measures
Regression Techniques
Concept of Regression
Best Fitting line
Simple Linear Regression
Building regression models using excel
Coefficient of determination (R- Squared)
Multiple Linear Regression
Assumptions of Linear Regression
Variable transformation
Reading coefficients in MLR
Multicollinearity
VIF
Methods of building Linear regression model in R
Model validation techniques
Cooks Distance
Q-Q Plot
Durbin- Watson Test
Kolmogorov-Smirnof Test
Homoskedasticity of error terms
Logistic Regression
Applications of logistic regression
Concept of odds
Concept of Odds Ratio
Derivation of logistic regression equation
Interpretation of logistic regression output
Model building for logistic regression
Model validations
Confusion Matrix
Concept of ROC/AOC Curve
KS Test

Basic Operations in R Programming
Introduction to R programming
Types of Objects in R
Naming standards in R
Creating Objects in R
Data Structure in R
Matrix, Data Frame, String, Vectors
Understanding Vectors & Data input in R
Lists, Data Elements
Creating Data Files using R

Introduction to Probability
Standard Normal Distribution
Normal Distribution
Geometric Distribution
Poisson Distribution
Binomial Distribution
Parameters vs. Statistics
Probability Mass Function
Random Variable
Conditional Probability and Independence
Unions and Intersections
Finding Probability of dataset
Probability Terminology
Probability Distributions
Data Visualization Techniques
Bubble Chart
Sparklines
Waterfall chart
Box Plot
Line Charts
Frequency Chart
Bimodal & Multimodal Histograms
Histograms
Scatter Plot
Pie Chart
Bar Graph
Line Graph
Market Basket Analysis

Applications of Market Basket Analysis
What is association Rules
Overview of Apriori algorithm
Key terminologies in MBA
Support
Confidence
Lift
Model building for MBA
Transforming sales data to suit MBA
MBA Rule selection
Ensemble modelling applications using MBA
Time Series Analysis (Forecasting)

Model building using ARIMA, ARIMAX, SARIMAX
Data De-trending & data differencing
KPSS Test
Dickey Fuller Test
Concept of stationarity
Model building using exponential smoothing
Model building using simple moving average
Time series analysis techniques
Components of time series
Prerequisites for time series analysis
Concept of Time series data
Applications of Forecasting
Decision Trees using R

Understanding the Concept
Internal decision nodes
Terminal leaves.
Tree induction: Construction of the tree
Classification Trees
Entropy
Selecting Attribute
Information Gain
Partially learned tree
Overfitting
Causes for over fitting
Overfitting Prevention (Pruning) Methods
Reduced Error Pruning
Decision trees – Advantages & Drawbacks
Ensemble Models
K Means Clustering

Parametric Methods Recap
Clustering
Direct Clustering Method
Mixture densities
Classes v/s Clusters
Hierarchical Clustering
Dendogram interpretation
Non-Hierarchical Clustering
K-Means
Distance Metrics
K-Means Algorithm
K-Means Objective
Color Quantization
Vector Quantization
Tableau Analytics

Tableau Introduction
Data connection to Tableau
Calculated fields, hierarchy, parameters, sets, groups in Tableau
Various visualizations Techniques in Tableau
Map based visualization using Tableau
Reference Lines
Adding Totals, sub totals, Captions
Advanced Formatting Options
Using Combined Field
Show Filter & Use various filter options
Data Sorting
Create Combined Field
Table Calculations
Creating Tableau Dashboard
Action Filters
Creating Story using Tableau
Analytics using Tableau

Clustering using Tableau
Time series analysis using Tableau
Simple Linear Regression using Tableau
R integration in Tableau

Integrating R code with Tableau
Creating statistical model with dynamic inputs
Visualizing R output in Tableau