Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Our Course Content
Introduction
 Introduction
 Why Should I Learn Data Science
Analytics in MS Excel
 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
R Programming
 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.
 Ifelse statement.
 Switch statement.
 Loops in R.
 For loop.
 While loop.
 Repeat loop.
 Break statement.
 Next statement.
 Functions in R.
 Builtin Functions.
 Userdefined 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.
Core Python Programming for Data Science
 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.
Scientific Python (basic packages/libraries) for Data Science.
 Numpy
 Pandas
 MatPlotlib
 Seaborn
 SKLearn
Databases
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.
SQL
 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.
Exploratory data analysis:
 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.
Statistics and Maths:
 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.
Statistics and Maths:
 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.
Data Preprocessing:
 Feature selection.
 Missing values treatment.
 Outliers Treatment.
Machine Learning
 Supervised Learning.
 Regression:
 Linear algorithms
 Simple Linear Regression.
 Multiple Linear Regression.
 Nonlinear algorithms.
 Log Regression.
 LogLog Regression.
 Square Root Regression.
 Cubic Regression.
 Quadratic Regression.
 Polynomial Regression.
 Forward Regression.
 Backward Regression.
 Stepwise Regression.
 Binomial Regression
 Bernoulli Regression.
 Poisson Regression.
 Quantile Regression.
 Robust Regression.
 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.
 KNearest Neighbour.
 Naïve Bayes.
 Non linear classification with Decision Trees.
 Decision Trees.
 Random Forest.
 Gradient Boosted Machine.
 Ada Boost.

 Unsupervised Learning.
 Clustering (1)Linear
 KMeans Clustering.
 Non Linear.
 Hierarchical Clustering
 Reinforcement Learning.
 Lasso Regression.
 Ridge Regression.
 ElasticNet Regression.
 Ensembling Methods.
 Bagging
 Boosting
 Principal component analysis.
NLP ( Natural Leaning Programming )
 Lemmatization
 Stemming
 Tokenization
 POS Tagging.
 Sentiment Analysis.
 Chat Bot.