Curriculum Designed by Experts

Introduction to Python

Topics:

• Overview of Python
• The Companies using Python
• Different Applications where Python is Used
• Discuss Python Scripts on UNIX/Windows
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the Screen

Sequences and File Operations

Topics:

• Python files I/O Functions
• Numbers
• Strings and related operations
• Tuples and related operations
• Lists and related operations
• Dictionaries and related operations
• Sets and related operations

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Topics:

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object Oriented Concepts
• Standard Libraries
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways
• Errors and Exception Handling
• Handling Multiple Exceptions

Topics:

• Data Analysis
• NumPy - arrays
• Operations on arrays
• Indexing slicing and iterating
• Reading and writing arrays on files
• Pandas - data structures & index operations
• Reading and Writing data from Excel/CSV formats into Pandas
• Metadata for imported Datasets
• Matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots - bar graphs, pie charts, histograms
• Contour plots

Data Manipulation

Topics:

• Basic Functionalities of a data object
• Merging of Data objects
• Concatenation of data objects
• Types of Joins on data objects
• Exploring a Dataset
• Analysing a dataset

Introduction to Machine Learning with Python

Topics:

• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression

Supervised Learning - I

Topics:

• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?

Dimensionality Reduction

Topics:

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA

Topics:

• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is a Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter Optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification

Unsupervised Learning

Topics:

• What is Clustering & its Use Cases?
• What is K-means Clustering?
• How K-means algorithm works?
• How to do optimal clustering?
• What is C-means Clustering?
• What is Hierarchical Clustering?
• How Hierarchical Clustering works?

Association Rules Mining and Recommendation Systems

Topics:

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

Reinforcement Learning

Topics:

• What is Reinforcement Learning?
• Why Reinforcement Learning?
• Elements of Reinforcement Learning
• Exploration vs. Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• Values

Topics:

• Data Visualization
• Business Intelligence tools
• VizQL Technology
• Connect to data from File
• Connect to data from Database
• Basic Charts
• Chart Operations
• Combining Data
• Calculations

Time Series Analysis

Topics:

• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

Model Selection and Boosting

Topics:

• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

Topics:

• What is Exploratory Data Analysis?
• EDA Techniques
• EDA Classification
• Univariate Non-graphical EDA
• Univariate Graphical EDA
• Multivariate Non-graphical EDA
• Multivariate Graphical EDA
• Heat Maps

Data Connection and Visualization in Tableau (Self-paced)

Topics:

• Data Visualization
• Business Intelligence tools
• VizQL Technology
• Connect to data from File
• Connect to data from Database
• Basic Charts
• Chart Operations
• Combining Data
• Calculations

Advanced Visualizations (Self-paced)

Topics:

• Trend lines
• Reference lines
• Forecasting
• Clustering
• Geographic Maps
• Using charts effectively
• Dashboards
• Story Points
• Visual best practices
• Publish to Tableau Online