Microsoft Excel Overview, Formatting Excel Shortcuts Basic Formulas, Sorting Data, Filtering Data, Column Chart Pie Chart, Pivot Tables, Vlookup, Match Function, Lookup Function, VBA

Introduction To Python, What and Why Python, Variables, Databases and various Operation, Datatypes and various Operations, Operators, Block Structure, Data Structures, Functions, Modules, Class, Numpy, Pandas, Matplotlib, Seaborn

Introduction To Statistics, Descriptive Statis [], Population And Sample, Types Of Data, Percentile, Quartile, IQR, , Measure Of Central Tendency, Asymmetry And Variability , Skewness, Kurtosis, Central Limit Theorem, Confidence Interval, Probability, Probability distribution, Discrete Probability Distribution [Probability Mass Function, Binomial Distribution, Poisson Distribution, Discrete Uniform Distribution],Continuous Probability Distribution [ Probability Density Function, Normal Distribution, Continuous Uniform, Exponential Distribution], Hypothesis Testing [Z Distribution, Student’s T distribution, Chi Square , Chi-Square goodness of fit, Chi square test of independence, Bayes Theorem, Correlation[ Pearson Corelation, Linear Corelation], Covariance

otIntroduction To Machine Learning, Types of Machine Learning, Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, Cross Validation [K-fold cross-validation, Hold-out cross-validation, Stratified k-fold cross-validation], Hyperparameter Tuning, Ensemble Learning [ Lasso Regression, Ridge Regression, Adaboost, Adagrad, PCA, K-Means,

otIntroduction To Neural Network, Foreword Propagation, Activation Function, Optimizers[SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, Adam], Activation Function [ Linear, Sigmoid, Tanh, ReLu, Leaky ReLu, ELU, Softmax, Swish], Loss Classification(BinaryCrossentropy, CategoricalCrossentropy, SparseCategoricalCrossentropy, Poisson, binary_crossentrop, categorical_crossentropy function, sparse_categorical_crossentropy function, Regression (MeanSquaredError class, MeanAbsoluteError class, MeanAbsolutePercentageError class, MeanSquaredLogarithmicError class, CosineSimilarity class)], Weight Initialization [ Xavier Weight Initialization, Normalized Xavier Weight Initialization, He weight Initialization], Bias Initialization, Callbacks [ReduceLROnPlateau, ModelCheckpoint, Earlystopping], TensorBoard, Keras Tuner, Padding, Pooling, Convolution, , Time Series Analysis ,Component of TSA, Methods to check stationarity [ADF , KPSS], AR, Moving Average Methods [Simple Moving Average , Cumulative Moving Average, Exponential Moving Average ] ACF, PACF, ARMA, LSTM, GRU

What Is R Programming, Variables And Data Type In R, Logical Operators,Vectors,List,Matrix,Data Frame,Flow Control, Functions In R, Data Manipulation In R- Dplyr, Data Manipulation In R- Tidyr, Data Visualization In R

BASIC OF DATABASE, TYPES OF DATA BASE, DATA TYPES, SQL OPERATORS, EXPRESSION (BOOLEAN, DATE, NUMERIC), CREATE, INSERT, DROP, TRUNCATE, DELETE, ALTER, UPDATE, SELECT, RANGE OPERATER, IN, WILDCARD, LIKE CLAUSE, CONSTRAINT, AGGREGATION FUNCTION, GROUP BY, ORDER BY, HAVING, JOINS, CASE, COMPLEX QUERIES, Trigger, Stored Procedures, Common Table Expression, Index, Except, Exists, Grouping set, Pivot, Rollup, Cube, Constraints, Partition

TABLEAU PRODUCTS, INSTALLATION OF TABLEAU DESKTOP/PUBLIC, CONNECTING TO DATABASES, REPLACING DATASOURCE, TABLEAU CANVAS INTERFACE, DATA TYPES, DRILL DOWN, HIREARCHIES, MEASURES, DIMESIONS, SORTING, GROUPING, PARAMETER, SETS, COMBINE, DATA BLENDING, FILTER, MARKS CARD, DUAL AXIS, CALCULATED FIELD, VISUALZATION, CANVAS FORMATING, DASHBOARD CREATION, Pareto Analysis, Table calculation, Multiple data source blending, Advanced charting techniques, Tableau prep tool

POWER BI PLATFORM, PROCESS FLOW, FEATURES, DATASET, BINS, PIVOTING, QUERY GROUP, DAX FUNCTION, FORMULA, CHARTS, REPORTS, DASHBOARDS, Multiple data source blending, Basic of SSIS for ETL