Data Science refers to the process of mining, inspecting, cleansing, and modeling data in order to reveal useful insights and information that would have otherwise been unobtainable. Most organizations that make analysis an integral part of their day-to-day operations use the conclusions suggested by the data to make informed business decisions.

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.

#### Who Can Learn?

Anyone who has business perception with an interest and passion towards predicting the future using statistics. Given that the boom for this professional has started very recently, every fresh graduate can see to this profession as an easy way to get a job & pursue the most sorts after profession in the world.

#### What do I Learn?

Statistics, Regression Techniques, Linear Methodologies, Regression Analysis, R, Python, Data Mining, Mini Tab, Big Data Hadoop, SAS Base, Spark, Data Mining/Machine Learning, Text Mining/ Natural Language Processing, Forecasting, Artificial Intelligence, Data Visualization, Tableau, XL Miner

- Data types and its measures
- Random Variable, its applications and exercises
- Probability – Applications with examples
- Probability distribution with examples
- Sampling Funnel – why and how
- Measures of Central Tendency
- Mean
- Median
- Mode

- Measures of dispersion
- Variance
- Standard Deviation
- Range – its derivation

- Measures of Skewness and Kurtosis – Graphical representation and application
- Various graphical representation of data for analysis
- Bar Chart
- Histogram
- Box Plot
- Scatter Plot

- Continuous Probability distribution
- Standard Normal distribution / Z distribution
- F – distribution
- Students t distribution
- Chi square distribution

- Discrete probability distribution
- Binomial distribution
- Negative Binomial distribution
- Poisson distribution

- Computing probability from Normal Distribution
- Building Normal Q-Q plots& its interpretation
- Central Limit Theorem for sampling variations
- Confidence Interval – Computation and analysis
- Formulating a hypothesis statement
- Parametric tests
- 1 sample, 2 sample test
- 1 sample Z test
- 1 Proportion, 2 Proportion test
- Paired t test
- One way ANOVA
- Chi- Square test

- Nonparametric tests
- 1 sample sign test
- Mann - Whitney test
- Kruskal – Wallistest
- Mood’s Median test

- Measure of correlation coefficient and its analysis
- Regression model using “Ordinary Least Squares”
- Coefficient of determination as a strength of a model
- Prediction interval and Confidence interval
- Prerequisites to Regression
- Regression techniques
- Linear Regression
- Simple
- Multiple

- Logistic Regression
- Simple
- Multiple

- Advanced Regression
- Negative Binomial
- Poisson
- Zero – Inflated
- Hurdle
- LOESS
- Polynomial

- Logit and Probit analysis

- Linear Regression
- Model building using regression
- Measures of accuracy
- Model improvement techniques
- Analysis of regression output with case studies
- Imputation Techniques
- Listwise, Pairwise Deletion
- Mean / Mode Substitution
- Regression Imputation
- Hot Deck, KNN Imputation

- Survival Analysis
- Time to event data
- Prediction techniques

- Supervised vs Unsupervised
- Basic Matrix Algebra
- Data Mining Unsupervised
- Clustering – its applications and limitation
- Hierarchal
- Non-Hierarchal (K-Means)

- Network Analysis
- Measures of strength
- Introduction to Webpage ranking

- Affinity Analysis / Association Rules
- Measures of association Support, Confidence, Lift Ratio
- Sequential pattern mining

- Recommender Systems
- Methods and tricks of the trade

- Dimension Reduction Techniques
- Principle Component Analysis
- Singular Value Decomposition

- Clustering – its applications and limitation
- Data Mining –Supervised
- Black Box demystified
- Neural Networks
- Support Vector Machines

- Classification / Pattern mining
- K Nearest Neighbor
- Naïve Bayes
- Seasonality factored model
- Autoregressive model
- Random walk

- Data Driven
- Smoothing
- Exponential Smoothing
- Advanced Exponential Smoothing
- Holt’s Method
- Winter Method

- AR, MA, ARIMA models

- Analysis of errors in forecast
- Skewness of Error
- Decision Tree & Random Forest
- Decision Tree C5.0

- Ensemble Techniques
- Boosting
- Bagging

- Gradient Boosting & Extreme Gradient Boosting

- Black Box demystified
- Text extraction from webpage
- Word clouds – analysis with context
- Negative and Positive words
- NLP
- Latent Dirichlet Allocation (LDA)
- Structured Extraction
- Emotion Mining

- Strategy for Forecasting
- Analysis by Graphical Representation
- Components in a time series data
- Plots of Time series data
- Autocorrelation function / Correlogram
- Visualizations – How to preform
- Methods of Forecast
- Naïve methods
- Simple and Moving Average

- Model driven
- Regression Model –Linear,
- Exponential, Quadratic
- Econometric models
- Seasonality factored model
- Autoregressive model
- Random walk

- Data Driven
- Smoothing
- Exponential Smoothing
- Advanced Exponential Smoothing
- Holt’s Method
- Winter Method

- AR, MA, ARIMA models

- Naïve methods
- Analysis of errors in forecast
- Skewness of Error
- Types of error measure
- Mean Error (ME)
- Mean Absolute Deviation (MAD)
- Means Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Percentage Error (MPE)
- Mean Absolute Percentage Error (MAPE)

- Introduction to R
- Working with Packages
- Performing various regression and data mining techniques using R
- Studio

- Next Batch Saturdays
- Duration 80 Hrs
- Students 145
- Certificate Yes
- Price Call - 91485 67987

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