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Data Science

data science online training

Data science is an inter-disciplinary 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.
  • 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.
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 Pre-processing:
  • Feature selection.
  • Missing values treatment.
  • Outliers Treatment.
Machine Learning
  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.

 

    1. Unsupervised Learning.
    2. Clustering  (1)Linear
    • K-Means Clustering.
    • Non- Linear.
    • Hierarchical Clustering
  1. Reinforcement Learning.
  • Lasso Regression.
  • Ridge Regression.
  • Elastic-Net Regression.
  1. Ensembling Methods.
  • Bagging
  • Boosting
  1. Principal component analysis.
NLP ( Natural Leaning Programming )
  • Lemmatization
  • Stemming
  • Tokenization
  • POS Tagging.
  • Sentiment Analysis.
  • Chat Bot.

Contact Us

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+91-798 958 7185

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