It's been said that Data Scientist is the most sought after job title of the 21st century. Why is it such a demanded position these days? The short answer is that over the last decade there's been a massive explosion in both the data generated and retained by companies, as well as you and me. Sometimes we call this "big data," and like a pile of lumber we'd like to build something with it. Data scientists are the people who make sense out of all this data and figure out just what can be done with it.
A data scientist is the adult version of the kid who can't stop asking "Why?". They're the kind of person who goes into an ice cream shop and gets five different scoops on their cone because they really need to know what each one tastes like. Similarly, even the term data scientist is a catchall title that encompasses many different flavors of work.
Data Science course is the most popular courses and therefore no wonder that many teaching institutes have mushroomed all over the country. But most of these institutes just teaches the basics but charges a bomb from the participants.
We offer training to make you learn data science is real terms. We understand there is a major differentiator between a data scientist and a statistician or an analyst or an engineer. Data Science course is taught by industry experts who have written books on these topics and are respected in the industry. They are regarded as the experts in their field.
Some of the advanced concepts will be taught by an IIT Professor <--
- Module 1: Programming concepts in R or Python as desired by the candidate
- Module 2: Advanced Data Analytics concepts using R or Python
- Module 3: Basic to Advanced Statistics concepts -using R/Python
- Module 4: Data Visualization using R or Python
- Module 5: Machine Learning
- Project
Main Content:
Detailed Structure: We have divided our course structure into 3 parts:
- Part A: Programming Concepts in R & Python
- Part B: Data Analytics
- Part C: Data Science
- R Overview and setup
- Syntax
- Data types - vector, lists, metrices, arrays, factors, data frames
- R Variables
- R Operators - arithmetic, relational, logical, assignment and miscellaneous
- Decision making & loops
- Functions in R
- Reading from files (CSV, Excel)
- Working with databases
- Overview of R Packages used for Visualization
- Basic and Advanced Plots. GGPLOT Interactive Plots: Multiple Graphs, Title, Axes, Labels, Color Palletes, vcd, Tableplot, Googlecharts, Shiny, Rcharts, D3.js
- 1. What is Data Science and Why study Data Science
- Tools and Techniques in Data Science
- Applications of Data Science
- 2. Getting to know your data, data cleansing and data transformation using R
- 3. Standard linear regression using R
- Building a linear model
- Performance tuning of linear model
- Building a polynomial model
- Ridge Regression
- Penalty based variable selection in regression models with many parameters(Lasso)
- Case study
- 4. Logistic regression using R
- Building a linear model for binary response data
- Interpretation of regression coefficients
- Classification of new cases
- Building a polynomial model for binary response data
- Performance tuning of model
- Multinomial logistic regression
- Case study
- 5. Clustering using R
- K-Means clustering
- K-Medoids clustering
- Hierarchical clustering procedures
- Density based clustering procedures
- Case study
- 6. Time series analysis
- Reading, plotting and decomposing time series data
- Forecasting using exponential smoothing
- Case study
- 7. Market basket analysis using R
- Part C: Basic to Advanced Statistics concepts
- Basic Probability
- Statistical Terminology and Basic Notations
- Importance of Data and Numbers with domain specific
- Measure of Central Tendencies & Measure of dispersion
- Variance Discussion and its importance across the business
- Legendre’s Least Square Principle
- Scatter Diagram and Data points distribution
- Trend lines and Trend Pattern Discussion
- Outlier and Missing Value Treatment Analysis
- Central Limit Theorem
- Probability Terminology and Notations
- Sample Space, Events and Experiments
- Probability Rules & Probability Types
- Bayes Theorem & Error Matrix
- Probability Scores and its importance in banking domain
- Discussion on Churn probability
- P-Value Significance in model outputs
- Understanding Distributions
- Discrete and Continuous distributions
- Binomial distribution & Poisson distribution
- Exponential distribution & t- Distribution
- Normal/Gaussian distribution
- Concepts on Confidence intervals
- Industry examples on understanding the distributions
- Advanced Statistical Concepts
- Theory of Hypothesis Testing
- Small Sample and Large Sample Tests( t & Chi square testing )
- ANOVA ( One way and Two Way )
- Explanation on F test and Z tests in summary outputs
- Theory of Association
- Bivariate and Multivariate Analysis
- Importance of Linearity
- Correlation (Positive Correlation, Negative Correlation and Types of Correlation)
- Regression Theory and Assumptions
- Exploratory Data Analysis
- Module 5: Machine Learning
- Artificial Neural Network
- Deep Learning
- Support Vector Machines and Kernel Regression
- Hidden Markov Models
- Decision Trees
- Ensemble Methods: Random Forest, Boosting: Gradient/XG, Ada, Bagging
- Regularization model
- Feature selection
Benefits:
- Advanced topics handled by IIT Professor
- Personalized training programs
- Project work covered
- Assignments to practice
Call us for Demo today at: 8008101590 / 70329 75766