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Data Science Training Course

Advertiser: FuturePoint Technologies Location: Chennai Posted: Jun 19, 2017 8:09:43 AM Hits: 101

Data Science Training Course

Ref: BT18194
Data Science involves Data Mining / Visualization / Statistics / Mathematics / Forecasting / Predictive.

Prerequisites : ( All will be covered in Course )
Any graduate BE/ BTech/ BSC/ BA/ MBA/ MCA/ ME/ MTech
Good at Statistics and mathematics
Business Analysts/Analysts /Managers in any Organisation IT/ Non IT
R Language Basics
Managers in Operations/ Planning/Strategy/ R & D
Developers (problem solving) in product/service

Able to produce models for Business/ Organization ( Model is technique/ Algorithm/ process to follow to solve issue).
Problems / cases where Algorithm will be applied in Real Time
Quick solution building to Problem ( Rapid Prototype development)
R code is open source can be accessed through Java( rJava) / other platforms.
Quick appreciation in Job as participant turn around time is good.
Solutions can be as global as possible ( Global products / Services) used this techniques.
Able to optimize resources in Labor / programming.
produce analyses and algorithms that help businesses make better decisions impacts critical business KPI's.
Exposure to at least 4 verticals in Retail/ E-Commerce/ Finance/ Cloud computing/ Machine Learning/ Business Process he gets improved skills and confidence.
Course Content

1. Data Exploration with Statistics
1.1 Types of Data
1.2 Data Summarization
1.3 Frequency tables and Distributions
1.4 Histograms
1.5 Measures of central tendency
1.5.1 Mean, Mode, and Median
1.5.2 Skewness and Kurtosis
1.6 Probability
1.7 Conditional Probability
2. Sampling and Hypothesis Testing
2.1 Normal Distribution
2.1.1 Significance Level
2.1.2 p-Value
2.2 Sampling and Estimation
2.3 Central Limit Theorem
2.4 Point and Interval Estimates
2.5 Null and Alternate Hypothesis
2.6 Types of Errors
3. Predictive Analytics : Linear Regression
3.1 Covariance and Correlation
3.2 Simple Linear Regression
3.4 Significance Tests
3.5 Multiple Linear Regression
3.6 Interpretation of Regression Coefficients
3.7 Categorical/ Dummy Variables
3.8 Assumptions of Linear Regression and implications
3.8.1 heteroscedasticity
3.8.2 Multicollinearity
3.8.3 Serial Correlation
3.9 Outliers
3.10 Regression Models Building
4. Predictive Analytics: Logistic Regression
4.1 When to use logistic regression
4.2 Assumptions
4.3 Logistic Function
4.4 Model Fit
4.4.1 Chi-Square test
4.4.2 -2 Log likelihood
4.4.3 Classification table
4.5 Interpreting Coefficients
4.6 Inferential Statistics
4.7 Dependent Variable Prediction
5. Predictive Analytics: Forecasting
5.1 Principles of Forecasting
5.1.1 Time Series
5.1.2 Casual Models
5.2 Forecasting Methods and Characteristics
5.2.1 Moving Average
5.2.2 Exponential Smoothing
5.3 Forecast Data Patterns Types
5.3.1 Level
5.3.2 Trend
5.3.3 Seasonality
5.3.4 Cyclical
5.4 Compute Forecast Accuracy
5.5 Selection of Forecasting Models
6. Market Basket Analysis : Unsupervised Learning
6.1 Concepts
6.2 Frequent Item set Methods
6.2.1 Apriori Algorithm, coding and Examples
6.2.2 FP-Growth Algorithm, coding and Examples
6.2.3 Pattern Evaluation Methods
6.3 Lift
6.4 Chi-Square
7. Classification
7.1 Concepts
7.2 Decision Trees coding examples
7.3 Bayes Classification method
7.4 Model Evaluation and Selection
7.5 Techniques to improve classification Accuracy
8. Clustering
8.1 Concepts
8.2 Partitioning methods
8.3 Hierarchical methods
8.4 Density based methods
8.5 Grid based methods
8.6 Evaluation of Clustering
9. Web Analytics and Mobile Analytics
9.1 Text Analytics
9.2 Sentiment Analytics
9.3 Click Analytics
10. Tools
Introduction to MS-Excel
10.1.1 Sumifs, countIfs, AvergaeIfs
10.1.2 Pivot Tables and Charts
10.1.3 Filters, advanced Filters
10.1.5 Case Study
11. Hands on coding to Rapid Prototyping Framework RStudio
11.1 Reading and Writing to R
11.2 Vectors
11.3 Frames and Subsets
11.4 5 Examples Regression
11.5 5 Examples Logistic Regression
11.6 5 Examples Machine Learning

12. Hands on coding to Rapid Prototyping Framework SAS
12.1 Managing and manipulating data
12.2 Creating charts
12.3 Linear Regression
12.4 Multiple Linear Regression
12.5 Data Mining in SAS

13. Big Data Applications
13.1 Coding in SQL based Hive examples
13.2 Coding in Non-SQL based PIG examples
13.3 Hadoop Java MapReduce Programs: 10 examples
13.4 2 Spark Machine Learning Examples
13.5 2 Storm and Kafka Examples

14. Projects Real World Cases
14.1 Churn Prediction in Telecom
14.2 Churn Prediction in HR
14.3 Prediction of Sales
14.4 Prediction of ATM Cash Deposits / Day
14.5 Recommendation Systems Algorithms used in Amazon/Linkedin
14.6 Text Analytics Classification using Linear SVM


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