Data Science

• Shortcut keys in Excel
• Character functions in Excel
• Date and Time Functions in Excel
• Mathematical Functions in Excel
• Pivot Tables
• Pivot Charts
• Visualization in Excel
• VLOOKUP
• HLOOKUP

• Download & Installation.
• Introduction and History of R programming.
• Basic Syntax in R.
• Variables in R.
• Operators in R.
• Arithmetic Operators.
• Assignment Operators.
• Relational Operators.
• Logical Operators.
• Miscellaneous Operators.
• Data Types in R.
• Data type Conversions in R.
• Lists in R.
• Factors in R.
• Matrices in R.
• Array Functions.
• Data Frames.
• Packages in R.
• Decision making in R. (Conditional statements)
• If statement.
• If-else statement.
• Switch statement.
• Loops in R.
• For loop.
• While loop.
• Repeat loop.
• Break statement.
• Next statement.
• Functions in R.
• Built-in Functions.
• User-defined Functions.
• File Readings in R.
• Charts & Graphs in R.
• Data Operations (Merging data, Aggregating data, Reshaping data, Sub setting data, Sorting data).
• Statistics in R.
• ML Algorithms in R.

• Download and Installation.
• Introduction and History.
• Basic Syntax.
• Variables in Python.
• Operators in Python.
• Arithmetic Operators.
• Assignment Operators.
• Comparison Operators.
• Logical Operators.
• Identity Operators.
• Membership Operators.
• Bitwise Operators.
• Data Types in Python.
• Data types / Data structures and their Operation.
• Decision Making in Python.
• If statement.
• If else statement.
• elif statement.
• Break statement.Continue statement.
• Pass statement.
• Loops in Python.
• For loops.
• While Loops.
• File Input/output Operations in Python.

• Numpy
• Pandas
• MatPlotlib
• Seaborn
• SKLearn

MySQL DB.

• Creating Database and Tables.
• Importing Data into Database.
• Establishing connection between DB and Python IDL.
• Performing SQL commands in Python IDL.
• Converting Data into data Frame.

 MongoDB/NoSQL DB

• Creating Database and Collection.
• Importing Data into Database.
• Establishing Connection between DB and Python IDL.
• Converting Data into Data Frame.

• Data Types in SQL.
• Operators in SQL.
• Arithmetic Operators.
• Comparison Operators.
• Logical Operators.
• Expressions in SQL.
• Boolean Expressions.
• Numeric Expressions.
• Date Expressions.
• Data Base Operation in SQL.
• Create/Alter and Delete Database.
• Create/Alter and Delete Table.
• Basic SQL Queries.
• Select Query.
• Insert Query.
• Update Query.
• Delete Query.
• Where Clause.
• Like Clause.
• Order By.
• Group By.
• Distinct Etc.
• Advanced SQL.
• Null Values.
• Data Functions.
• Etc.

• Scaling
• Shape and Type of data.
• Percentiles and Quantiles.
• Identification of Missing Values.
• Identification of Anomalies/Outliers.
• Describing data using statistics.
• Data Visualization.
• Data Summarization.

• Why statistics for Data science?
• Linear Algebra.
• Population and Sampling.
• Univariate statistics.
• Measures of Dispersion.
• Measures of Central Tendency.
• Other Measures.
• Multivariate statistics.
• Testing of Hypothesis.
• Probability Distributions.

• Why statistics for Data science?
• Linear Algebra.
• Population and Sampling.
• Univariate statistics.
• Measures of Dispersion.
• Measures of Central Tendency.
• Other Measures.
• Multivariate statistics.
• Testing of Hypothesis.
• Probability Distributions.

• Feature selection.
• Missing values treatment.
• Outliers Treatment.

1. Supervised Learning.
2. Regression:

• Linear algorithms
• Simple Linear Regression.
• Multiple Linear Regression.
• Non-linear algorithms.
• Log Regression.
• Log-Log Regression.
• Square Root Regression.
• Cubic Regression.
• Quadratic Regression.
• Polynomial Regression.
• Forward Regression.
• Backward Regression.
• Step-wise Regression.
• Binomial Regression
• Bernoulli Regression.
• Poisson Regression.
• Quantile Regression.
• Robust Regression.

1. Classification

• Linear Classification.
• Binary Logistic Regression.
• Multiple Logistic Regression.
• Ordinal Regression.
• Linear Discriminate Analysis.
• Non- linear classification.
• Mixture Discriminate analysis.
• Quadratic Discriminate analysis.
• Regularized Discriminate analysis.
• Flexible Discriminate analysis.
• Support Vector Machine.
• K-Nearest Neighbour.
• Naïve Bayes.

• Non linear classification with Decision Trees.
• Decision Trees.
• Random Forest.
• Gradient Boosted Machine.
• Ada Boost.

2. 1. Unsupervised Learning.
   2. Clustering  (1)Linear
   • K-Means Clustering.
   • Non- Linear.
   • Hierarchical Clustering

3. Reinforcement Learning.

• Lasso Regression.
• Ridge Regression.
• Elastic-Net Regression.

4. Ensembling Methods.

• Bagging
• Boosting

5. Principal component analysis.

• Lemmatization
• Stemming
• Tokenization
• POS Tagging.
• Sentiment Analysis.
• Chat Bot.