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- You will never miss a class at Suntrainings! You can choose either of the two options:
- 1. You can go through the recorded session of the missed class and the class presentation that are available for online viewing through the LMS.
- 2. You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the course.
- Sun Trainings is committed to provide you an awesome learning experience through world-class content and best-in-class instructors.
- We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
- We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
- All instructors at Sun Trainings are senior industry practitioners with minimum 10 - 12 years of relevant IT experience. They are subject matter experts who trained by Sun Trainings to provide impeccable learning experience to all our global users.
- You can Call us at +91 9642434362 OR Email us at contact@suntrainings.com. We shall be glad to assist you.
Datascience Course Curriculum
- Introduction about Statistics
- Different Types of Variables
- Measures of Central Tendency with examples
- Measures of Dispersion
- Probability & Distributions
- Probability Basics
- Binomial Distribution and its properties
- Poisson distribution and its properties
- Normal distribution and its properties
- INFERENTIAL STATISTICS AND TESTING OF HYPOTHESIS
- Sampling methods
- Different methods of estimation
- Testing of Hypothesis & Tests
- Analysis of Variance
- COVARIANCE & CORRELATION
- PREDICTIVE MODELING STEPS AND METHODOLOGY WITH LIVE EXAMPLE:
- Data Preparation
- Exploratory Data analysis
- Model Development
- Model Validation
- Model Implementation
- MULTIPLE LINEAR REGRESSION
- Linear Regression-Introduction-Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics(Variable significance,R-square/Adjusted R-Square,Global hypothesis etc)
- Validation of Linear Regression Models(Re running Vs.Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation,drivers etc)
- Interpretation of Results - Business Validation - Implementation on new data
- Real time case study of Manufacturing and Telecom Industry to estimate the future revenue using the models
- Logistic Regression-Introduction-Ipplications
- Linear Regression Vs.Logistic Regression Vs.Generalized Linear Models
- Building Logistic Regression Model
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve)
- Probability Cut-offs, Lift charts, Model equation, drivers etc)
- Interpretation of Results - Business Validation - Implementation on new data
- Real time case study to Predict the Churn customers in the Banking and Retail industry
- Partial Least Square Regression
- Partial Least square Regression - Introduction - Applications
- Difference between Linear Regression and Partial Least Square Regression
- Building PLS Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
- Interpretation of Results - Business Validation - Implementation on new data
- Sharing the real time example to identify the key factors which are driving the Revenue
- Variable Reduction Techniques
- Factor Analysis
- Principle Component Analysis
- Assumptions of PCA
- Working Mechanism of PCA
- Types of Rotations
- Standardization
- Positives and Negatives of PCA
- Chaid
- Cart
- Difference between chaid and cart
- Random Forest
- Decision tree vs. Random Forest
- Data Preparation
- Missing data imputation
- Outlier detection
- Handling imbalance data
- Random Record selection
- Random Forest R parameters
- Random Variable selection
- Optimal number of variables selection
- Calculating Out Of Bag (OOB) error rate
- Calculating Out of Bag Predictions
- Segmentation For Marketing Analysis
- Need for segmentation
- Criterion of segmentation
- Types of distances
- Clustering algorithms
- Hierarchical clustering
- K-means clustering
- Deciding number of clusters
- Case study
- Business rules criteria
- Real time use case to identify the most valuable revenue generating customers
- Time series components( trend, seasonality, cyclicity and level) and decomposition
- Basic techniques
- Averages,
- Smoothening etc.
- Advanced techniques
- AR Models,
- ARIMA
- UCM
- Hybrid Model
- Understanding forecasting accuracy - mape, mad, mse etc
- Couple of use cases, to forecast the future sales of products
- Gathering text data from web and other sources
- Processing raw web data
- collecting twitter data with twitter api
- naive bayes algorithm
- Assumptions and of Naïve Bayes
- Processing of Text data
- Handling Standard and Text data
- Building Naïve Bayes Model
- Understanding standard model metrics
- Validation of the Models (Re running Vs. Scoring)
- Sentiment analysis
- Goal Setting
- Text Preprocessing
- Parsing the content
- Text refinement
- Analysis and Scoring
- Live connectivity from r to tableau
- generating the reports and charts
Trainer Information
- Trainer has 15+ years of experience in IT industry.
