• 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,
  • Learn about R-bind.
  • R Functions, code compilation and data in well-defined format called R-Packages,
  • Learn about R-Package structure,
  • Package metadata and testing,
  • 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,
  • Advanced regression in R,
  • Prediction linear regression.
  • Logistic Regression Mean,
  • Logistic Regression in R.
  • Advanced logistic regression,
  • 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,
  • MC Fadden’s pseudo R-squared,
  • 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,
  • Solving business problems,
  • 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,
  • Methods of business prediction,
  • 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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