 • Introduction to Data Science,
• Importance of Data Science,
• Statistical and analytical methods,
• Deploying Data Science for Business Intelligence,
• Transforming data,
• Machine learning
• Introduction to Recommender systems
• How Data Science solves real world problems,
• Data Science
• Project Life Cycle,
• Principles of Data Science,
• Introduction to various BI
• Analytical tools,
• Data collection,
• Introduction to statistical packages,
• Data visualization tools,
• R Programming,
• Predictive modelling,
• Machine learning,
• Artificial intelligence
• Statistical analysis.
• Converting data into useful information,
• Collecting the data,
• Understand the data,
• Finding useful information in the data,
• Interpreting the data,
• Visualizing the data
• Descriptive statistics,
• Let us understand some terms in statistics,
• Variable
• Dot Plots,
• Histogram,
• Stemplots,
• Box and whisker plots,
• Outlier detection from box plots
• Box and whisker plots
• What is probability?,
• Set & rules of probability,
• Bayes Theorem
• Probability Distributions,
• Few Examples,
• Student T- Distribution,
• Sampling Distribution,
• Student t- Distribution,
• Poison distribution
• Stratified Sampling,
• Proportionate Sampling,
• Systematic Sampling,
• P – Value,
• Stratified Sampling
• Cross Tables,
• Bivariate Analysis,
• Multi variate Analysis,
• Dependence and Independence tests ( Chi-Square ),
• Analysis of Variance,
• Correlation between Nominal variable
• Boxplot in R programming,
• Understanding distribution and percentile,
• Identifying outliers,
• Rstudio Tool,
• Various types of distribution like Normal,
• Uniform and Skewed.

## R Programming

• R language for statistical programming, the various features of R,
• Introduction to R Studio,
• The statistical packages, familiarity with different data types and functions,
• Learning to deploy them in various scenarios,
• Use SQL to apply ‘join’ function,
• Components of R Studio like code editor,
• Visualization and debugging tools,
• R Functions, code compilation and data in well-defined format called R-Packages,
• CRAN (Comprehensive R Archive Network),
• Vector creation and variables values assignment.
• R functionality,
• Rep Function,
• Generating Repeats,
• Sorting and generating Factor Levels,
• Transpose and Stack Function.
• Introduction to matrix and vector in R,
• Understanding the various functions like Merge,
• Strsplit,
• Matrix manipulation, rowSums,
• RowMeans,
• ColMeans,
• ColSums,
• Sequencing,
• Repetition,
• Indexing and other functions.
• Understanding subscripts in plots in R,
• how to obtain parts of vectors,
• Using subscripts with arrays,
• As logical variables, with lists,
• Understanding how to read data from external files.
• Generate plot in R,
• Graphs,
• Bar Plots,
• Line Plots,
• Histogram,
• Components of Pie Chart.
• Understanding Analysis of Variance (ANOVA)
• Statistical technique,
• Working with Pie Charts, Histograms,
• Deploying ANOVA with R,
• One way ANOVA, two way ANOVA.
• K-Means Clustering for Cluster & Affinity Analysis,
• Cluster Algorithm,
• Cohesive subset of items,
• Solving clustering issues,
• Working with large datasets,
• Association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.
• Introduction to Association Rule Mining,
• The various concepts of Association Rule Mining,
• Various methods to predict relations between variables in large datasets,
• The algorithm and rules of Association Rule Mining, understanding single cardinality.
• Understanding what is Simple Linear Regression,
• The various equations of Line,
• Slope,
• Y-Intercept Regression Line,
• Deploying analysis using Regression,
• The least square criterion,
• Interpreting the results, standard error to estimate and measure of variation.
• Scatter Plots,
• Two variable Relationship,
• Simple Linear Regression analysis,
• Line of best fit
• Deep understanding of the measure of variation,
• The concept of co-efficient of determination,
• F-Test,
• The test statistic with an F-distribution,
• Prediction linear regression.
• Logistic Regression Mean,
• Logistic Regression in R.
• Understanding how to do prediction using logistic regression,
• Ensuring the model is accurate,
• Understanding sensitivity and specificity, confusion matrix,
• What is ROC, a graphical plot illustrating binary classifier system,
• ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.
• Detailed understanding of ROC,
• Area under ROC Curve,
• Converting the variable, data set partitioning,
• Understanding how to check for multicollinearlity,
• How two or more variables are highly correlated,
• Building of model, advanced data set partitioning,
• Interpreting of the output,
• Predicting the output, detailed confusion matrix,
• Deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.
• Data analysis with R,
• Understanding the WALD test,
• The significance of the area under ROC Curve,
• Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.
• Connecting to various databases from the R environment,
• Deploying the ODBC tables for reading the data,
• Visualization of the performance of the algorithm using Confusion Matrix.
• Creating an integrated environment for deploying R on Hadoop platform,
• Working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment
•  R programming for MapReduce jobs and Hadoop execution.

## Python Programming

• Hello, World!,
• Variables and Types,
• Lists,
• Basic Operators,
• String Formatting,
• Basic String Operations,
• Conditions,
• Loops,
• Functions,
• Classes and Objects,
• Dictionaries,
• Modules and Packages
• Numpy Arrays,
• Pandas Basics
• Generators,
• List Comprehensions,
• Multiple Function
• Arguments, Regular Expressions,
• Exception Handling, Sets,
• Serialization,
• Partial functions,
• Code Introspection, Closures, Decorators
• Deploying machine learning for data analysis,
• Using algorithms for searching patterns in data,
• Relationship between variables,
• Multivariate analysis, interpreting correlation,
• Negative correlation.
• Data Transformation key phases Data Mapping and Code Generation,
• Data Processing operation,
• Data patterns, data sampling,
• Sampling distribution, normal and continuous variable,
• Data extrapolation, regression,
• Linear regression model.
• Data analysis,
• Hypothesis testing,
• Simple linear regression,
• Chi-square for assessing compatibility between theoretical and observed data,
• Implementing data testing on data warehouse,
• Validating data,
• Checking for accuracy,
• Data operational monitoring capabilities.
• Various techniques of data modelling and generating algorithms,
• Prediction approaches, data sampling,
• Disproportionate sampling,
• Data modelling rules, data iteration,
• Deploying data for mission-critical applications
• Working with large data sets in data warehouses,
• Data clustering, grouping,
• Horizontal & vertical slicing,
• Data sharding in partitioning,
• Clustering algorithms,
• K-means Clustering for analysis and data mining,
• Exclusive clustering,
• Hierarchy clustering,
• Mahout Clustering algorithm and Probabilistic Clustering,
• Nearest neighbour search,
• Pattern recognition, and statistical classification.

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