Data Science online training:
Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, operations research information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Methods that scale to big data are of particular interest in data science, although the discipline is not generally considered to be restricted to such big data, and big data technologies are often focused on organizing and reprocessing the data instead of analysis. The development of machine learning has enhanced the growth and importance of data science.
Data Science provides a full range of capabilities that allow you to:
- Data
- Big Data
- Data Science Deep Dive
- Intro to R Programming
- Data Manipulation in R
- Data Import Techniques in R
- Exploratory Data Analysis (EDA) using R
- Data Visualization in R
- Big Data and Hadoop Introduction
- Understand Hadoop Cluster Architecture
- Map Reduce Concepts
- Advanced Map Reduce Concepts
- Hadoop 2.0 & YARN
- PIG
- Module 7
- HIVE
- Module-9
- Module-11
- SQOOP
- Project in Healthcare Domain
- Project in Finance/Banking Domain
- Spark
- Statistics + Machine Learning
Data science Online Training Content
Duration : 45 Hours
DATASCIENCE CONTENT:
COVARIANCE & CORRELATION
SUPERVISED TECHNIQUES:
VARIABLE REDUCTION TECHNIQUES
SUPERVISED TECHNIQUES CLASSIFICATION:
COUPLE OF REAL TIME USE CASES WHICH ARE RELATED TO TELECOM AND RETAIL INDUSTRY. IDENTIFICATION OF THE CHURN.
UNSUPERVISED TECHNIQUES:
BUSINESS RULES CRITERIA
REAL TIME USE CASE TO IDENTIFY THE MOST VALUABLE REVENUE GENERATING CUSTOMERS.
TIME SERIES ANALYSIS:
TIME SERIES COMPONENTS( TREND, SEASONALITY, CYCLICITY AND LEVEL) AND DECOMPOSITION
TEXT ANALYTICS:
GATHERING TEXT DATA FROM WEB AND OTHER SOURCES
PROCESSING RAW WEB DATA
COLLECTING TWITTER DATA WITH TWITTER API
- 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
- Sampling methods
- Different methods of estimation
- Testing of Hypothesis & Tests
- Analysis of Variance
- Data Preparation
- Exploratory Data analysis
- Model Development
- Model Validation
- Model Implementation
- 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
- 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 – 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
- Assumptions of PCA
- Working Mechanism of PCA
- Types of Rotations
- Standardization
- Positives and Negatives of PCA
- 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
UNSUPERVISED TECHNIQUES:
- Need for segmentation
- Criterion of segmentation
- Types of distances
- Clustering algorithms
- Hierarchical clustering
- K-means clustering
- Deciding number of clusters
- Case study
REAL TIME USE CASE TO IDENTIFY THE MOST VALUABLE REVENUE GENERATING CUSTOMERS.
TIME SERIES ANALYSIS:
TIME SERIES COMPONENTS( TREND, SEASONALITY, CYCLICITY AND LEVEL) AND DECOMPOSITION
- Averages,
- Smoothening etc
- 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
- 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)
- Goal Setting
- Text Preprocessing
- Parsing the content
- Text refinement
- Analysis and Scoring
Highlights of Data Science Online Training:
- DATA SCIENCE Real Time Project Support and Documentation:- DATA SCIENCE Real-time project support related design and migration and function setup documentation also teach under training
- Interview Skills based on DATA SCIENCE:- Mark interview conducted on every week and provide more than 500+ interview questions by end of course
- DATA SCIENCE Certification Support:-Providing DATA SCIENCE certified training, 50+ learner certified through RamJS Tech in DATA SCIENCE technology.
- DATA SCIENCE Resume Preparation: – Based on learner experience we helps him to show his confidence through resume. We will help you to prepare to shortlist resumes.
- Small Training Batch:-One to one session will arrange by RamJS Tech for learner comfortability.
- DATA SCIENCE Practical Guidance:- Everyday tasks and practical exercises as homework
Data Science along with Project plan Implementation Approaches.
- 12+ Years of Real-time Data Science Experience in IT Industry
- Worked on certified professional with various Great MNC’s
- Real-time project oriented training with step-by-step Scenarios.
- Technical and functional and migrations and security design Documentation approaches
- Covering the Pre-requisites for learning Data Science.
- Completed 50+ coaching Assignment around 80 students
The Data Science training contains all real time industry standards.