LEARN DATA SCIENCE
Accelerate your career prospects with iteanz Data Science Training
Learn Data Science from myTectra the market leader!
Enhance your skill set and boost your hirability through innovative and hands on Data Science training with myTectra.
Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.
Businesses in all industries are beginning to capitalize on the vast increase in data and the new big data technologies becoming available for analyzing and gaining value from it.
This makes it a great prospect for anyone looking for a well-paid career in an exciting and cutting-edge field Data Science.
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36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each. Weekday Class : 18 sessions of 2 hours each.
You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.
Real-life Case Studies
Live project based on any of the selected use cases, involving implementation of the various Data Science services.
24 x 7 Expert Support
We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
Each class will be followed by practical assignments which can be completed before the next class.
Towards the end of the course, you will be working on a project. myTectra certifies you as an Data Science Expert based on the project.
CHAPTER 1:Getting Started With Data Science And Recommender Systems
- Data Science Overview
- Reasons to use Data Science
- Project Lifecycle
- Data Acquirement
- Evaluation of Input Data
- Transforming Data
- Statistical and analytical methods to work with data
- Machine Learning basics
- Introduction to Recommender systems
- Apache Mahout Overview
CHAPTER 2:Reasons To Use, Project Lifecycle
- What is Data Science?
- What Kind of Problems can you solve?
- Data Science Project Life Cycle
- Data Science-Basic Principles
- Data Acquisition
- Data Collection
- Understanding Data- Attributes in a Data, Different types of Variables
- Build the Variable type Hierarchy
- Two Dimensional Problem
- Co-relation b/w the Variables- explain using Paint Tool
- Outliers, Outlier Treatment
- Boxplot, How to Draw a Boxplot
CHAPTER 3:Acquiring Data
- Discussion on Boxplot- also Explain
- Example to understand variable Distributions
- What is Percentile? – Example using Rstudio tool
- How do we identify outliers?
- How do we handle outliers?
- Outlier Treatment: Using Capping/Flooring General Method
- Distribution- What is Normal Distribution
- Why Normal Distribution is so popular
- Uniform Distribution
- Skewed Distribution
CHAPTER 4:Machine Learning In Data Science
- Discussion about Box plot and Outlier
- Goal: Increase Profits of a Store
- Areas of increasing the efficiency
- Data Request
- Business Problem: To maximize shop Profits
- What are Interlinked variables
- What is Strategy
- Interaction b/w the Variables
- Univariate analysis
- Multivariate analysis
- Bivariate analysis
- Relation b/w Variables
- Standardize Variables
- What is Hypothesis?
- Interpret the Correlation
- Negative Correlation
- Machine Learning
CHAPTER 5:Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data
- Correlation b/w Nominal Variables
- Contingency Table
- What is Expected Value?
- What is Mean?
- How Expected Value is differ from Mean
- Experiment – Controlled Experiment, Uncontrolled Experiment
- Degree of Freedom
- Dependency b/w Nominal Variable & Continuous Variable
- Linear Regression
- Extrapolation and Interpolation
- Univariate Analysis for Linear Regression
- Building Model for Linear Regression
- Pattern of Data means?
- Data Processing Operation
- What is sampling?
- Sampling Distribution
- Stratified Sampling Technique
- Disproportionate Sampling Technique
- Balanced Allocation-part of Disproportionate Sampling
- Systematic Sampling
- Cluster Sampling
- 2 angels of Data Science-Statistical Learning, Machine Learning
CHAPTER 6:Testing And Assessment, Production Deployment And More
- Multi variable analysis
- linear regration
- Simple linear regration
- Hypothesis testing
- Speculation vs. claim(Query)
- Step to test your hypothesis
- performance measure
- Generate null hypothesis
- alternative hypothesis
- Testing the hypothesis
- Threshold value
- Hypothesis testing explanation by example
- Null Hypothesis
- Alternative Hypothesis
- Histogram of mean value
- Revisit CHI-SQUARE independence test
- Correlation between Nominal Variable
CHAPTER 7:Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment
- Machine Learning
- Importance of Algorithms
- Supervised and Unsupervised Learning
- Various Algorithms on Business
- Simple approaches to Prediction
- Predict Algorithms
- Population data
- Disproportionate Sampling
- Steps in Model Building
- Sample the data
- What is K?
- Training Data
- Test Data
- Validation data
- Model Building
- Find the accuracy
- Deploy the model
- Linear regression
CHAPTER 8:Getting Started With Segmentation Of Prediction And Analysis
- Cluster and Clustering with Example
- Data Points, Grouping Data Points
- Manual Profiling
- Horizontal & Vertical Slicing
- Clustering Algorithm
- Criteria for take into Consideration before doing Clustering
- Graphical Example
- Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy
- Simple Approaches to Prediction
- Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
- Clustering Algorithm in Mahout
- Probabilistic Clustering
- Pattern Learning
- Nearest Neighbor Prediction
- Nearest Neighbor Analysis
CHAPTER 9:Integration Of R And Hadoop
- R introduction
- How R is typically used
- Features of R
- Introduction to Big data
- Ways to connect with R and Hadoop
- Case Study
- Steps for Installing RIMPALA
- How to create IMPALA packages