• Introduction of analytics
• Introduction to Business Intelligence – what, where
and how?
• Need for Business Intelligence / Analytics
• BI/DW Lifecycle
• Real life-based exercises to understand BI phases
• Requirements gathering, Data Modeling
(Dimensional)
• Analytics and Approach for Solution Design
• Dimensional Modeling Process and Scenario based
model designing
• Career Opportunities discussion and guidance
• Introduction to Excel
• Excel features (Lookup, count blank, forecast)
• Formatting along with conditional formatting
• Pivoting (Reports and charts with pivot tables)
• Data Protections
• Data Population and error handling
• Statistical analysis
• Excel Function (useful for aggregation of data)
• Data Analysis using Excel
• SQL Basic
• Introduction SQL (Structured Query Language)
• Fundamentals of SQL
• SQL Tables, Joins, Variables
• SQL Advance
• SQL Functions, Subqueries, Views, Rules
• Condition based queries using various operator
• Data Modeling
• Different type of joins (Basic, Advance)
• Aggregate Function
• Windows Functions
• Use of CTE (Common table expression) with
practice
• Hands on exercise on live projects
• Setting Python in Windows
• Introduction to print ()
• Variable and Data Type
• Enumerators, operators, building basic expressions
• More print (), input ()
• String data type indexing and slicing
• Program flow controls
• Loops
• Data structures – List, Dictionary, Tuple, Set
• Introduction to functions
• Exception Handling
• Object Oriented Programming concepts
• Reading from text files and writing to text files, .csv
files
• URL Libraries
• NumPy
• Data frames using panda’s library
• Datasets
• Introduction to Machine Learning
• Classification, Dimensionality
• Types of Supervised Learning
• Probability, Decision Tree, ID3 Algorithm
• Bayes Theorem, Naïve Bayes Classifier
• Managing Data Label Encoder, Data Scaling –
Standard Scalar
• Feature Selection – Select K Best
• Sampling and Evaluation – Confusion Matrix, Cross
Evaluations
• Linear Regression, Logistic Regression
• Support Vector Machines
• Clustering – KMeans
• Ensemble Learning, Reinforcement Learning, PAC
Learning
• Artificial Neural Network –
• Single Layer
• Multilayer Perceptron
Algorithm
• Introduction to Data Analysis and Visualization
• Data –
• Sources of Data
• Cleaning the data
• Central tendencies for
cleaning
• Hypothesis testing
• Shape of the Data – Univariate, Bivariate,
Multivariate with their plottings
• Univariate –
• Bar graphs
• Histograms
• Bivariate –
• Covariance
• Scatter Plot
• Linear regression
• Multivariate Data –
• Correlation matrix
• Multivariate regression
• EDA
• Plotting –
Special plotting’s bubble plot, animated bubble plot
etc.
using
• Seaborne
• ggplot
• Altair
• Matplotlib
• Bokeh
• Case Studies
• Introduction of analytics
• Introduction to Business Intelligence – what, where and how?
• Need for Business Intelligence / Analytics
• BI/DW Lifecycle
• Real life-based exercises to understand BI phases
• Requirements gathering, Data Modeling (Dimensional)
• Analytics and Approach for Solution Design
• Dimensional Modeling Process and Scenario based model designing
• Career Opportunities discussion and guidance
• Introduction to Excel
• Excel features (Lookup, count blank, forecast)
• Formatting along with conditional formatting
• Pivoting (Reports and charts with pivot tables)
• Data Protections
• Data Population and error handling
• Statistical analysis
• Excel Function (useful for aggregation of data)
• Data Analysis using Excel
• SQL Basic
• Introduction SQL (Structured Query Language)
• Fundamentals of SQL
• SQL Tables, Joins, Variables
• SQL Advance
• SQL Functions, Subqueries, Views, Rules
• Condition based queries using various operator
• Data Modeling
• Different type of joins (Basic, Advance)
• Aggregate Function
• Windows Functions
• Use of CTE (Common table expression) with practice
• Hands on exercise on live projects
• Setting Python in Windows
• Introduction to print ()
• Variable and Data Type
• Enumerators, operators, building basic expressions
• More print (), input ()
• String data type indexing and slicing
• Program flow controls
• Loops
• Data structures – List, Dictionary, Tuple, Set
• Introduction to functions
• Exception Handling
• Object Oriented Programming concepts
• Reading from text files and writing to text files, .csv files
• URL Libraries
• NumPy
• Data frames using panda’s library
• Datasets
• Introduction to Machine Learning
• Classification, Dimensionality
• Types of Supervised Learning
• Probability, Decision Tree, ID3 Algorithm
• Bayes Theorem, Naïve Bayes Classifier
• Managing Data Label Encoder, Data Scaling – Standard Scalar
• Feature Selection – Select K Best
• Sampling and Evaluation – Confusion Matrix, Cross Evaluations
• Linear Regression, Logistic Regression
• Support Vector Machines
• Clustering – KMeans
• Ensemble Learning, Reinforcement Learning, PAC Learning
• Artificial Neural Network –
• Single Layer
• Multilayer Perceptron
Algorithm
• Introduction to Data Analysis and Visualization
• Data –
• Sources of Data
• Cleaning the data
• Central tendencies for
cleaning
• Hypothesis testing
• Shape of the Data – Univariate, Bivariate, Multivariate with their plottings
• Univariate –
• Bar graphs
• Histograms
• Bivariate –
• Covariance
• Scatter Plot
• Linear regression
• Multivariate Data –
• Correlation matrix
• Multivariate regression
• EDA
• Plotting –
Special plotting’s bubble plot, animated bubble plot etc.
using
• Seaborne
• ggplot
• Altair
• Matplotlib
• Bokeh
• Case Studies
From gathering business requirements to applying the technology, we focus on solving business questions.
