Python Certification Training for Data Science

• 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

• Tuple - properties, related operations, compared with a list
• List - properties, related operations
• Dictionary - properties, related operations
• Set - properties, related operations

Skills:

• File Operations using Python
• Working with data types of Python

• 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

• Functions - Syntax, Arguments, Keyword Arguments, Return Values
• Lambda - Features, Syntax, Options, Compared with the Functions
• Sorting - Sequences, Dictionaries, Limitations of Sorting
• Errors and Exceptions - Types of Issues, Remediation
• Packages and Module - Modules, Import Options, sys Path

• Error and Exception management in Python
• Working with functions in Python

• 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
• matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots - bar graphs, pie charts, histograms
• Contour plots

• NumPy library- Creating NumPy array, operations performed on NumPy array
• Pandas library- Creating series and dataframes, Importing and exporting data
• Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

• 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

• Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
• GroupBy operations
• Aggregation
• Concatenation
• Merging
• Joining

• Python in Data Manipulation

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

• Linear Regression – Boston Dataset

• Machine Learning concepts
• Machine Learning types
• Linear Regression Implementation

• What are 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?

• Implementation of Logistic regression
• Decision tree
• Random forest

• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output

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

• PCA
• Scaling
• Skills:
• Implementing Dimensionality Reduction Technique

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

• Implementation of Naïve Bayes, SVM
• Skills:
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output

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

• Implementing K-means Clustering
• Implementing Hierarchical Clustering

• Unsupervised Learning
• Implementation of Clustering – various types

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How does Recommendation Engines work?
• Collaborative Filtering
• Content-Based Filtering
• Hands-On/Demo:
• Apriori Algorithm
• Market Basket Analysis

• Data Mining using python
• Recommender Systems using python

• 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
• α values

• Calculating Reward
• Discounted Reward
• Calculating Optimal quantities
• Implementing Q Learning
• Setting up an Optimal Action

• Implement Reinforcement Learning using python
• Developing Q Learning model in python

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

• Checking Stationarity
• Converting a non-stationary data to stationary
• Implementing Dickey-Fuller Test
• Plot ACF and PACF
• Generating the ARIMA plot
• TSA Forecasting

• TSA in Python

• What is Model Selection?
• The need for Model Selection
• Cross-Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

• Cross-Validation
• AdaBoost

• Model Selection
• Boosting algorithm using python