Data Science Architect Program

• Machine Learning
• Natural Language Processing
• Deep Learning

• Creating the “Hello World” code
• Demonstrating Conditional Statements
• Variables
• Demonstrating Loops

• Statistics
• Python Essentials for Data Science

• Descriptive Statistics and Inferential Statistics
• Sample and Population
• Variables and Data types
• Percentiles
• Measures of Central Tendency
• Measures of Spread
• Skewness, Kurtosis
• Degrees of freedom
• Variance, Covariance, Correlation
• Descriptive statistics and Inferential Statistics in Python
• Test of Hypothesis
• Confidence Interval
• Sampling Distribution
• Standard Probability Distribution Functions
• Bernoulli, Binomial-Distributions
• Normal Distributions

• Data Transformations
• Outlier Detection and Management
• Charts and Graphs
• One Dimensional Chart
• Box plots
• Bar graph
• Histogram
• Scatter plots
• Multi-Dimensional Charts

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

• Creating the “Hello World” code
• Demonstrating Conditional Statements
• Variables
• Demonstrating Loops

• Fundamentals of Python Programming

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

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

• Error and Exception Management in Python
• Working with Functions in Python

Learning Objectives: Through this module, you will understand in-detail about Data Manipulation

Topics:

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

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

• Python in Data Manipulation

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.

Topics:

• Python Revision (Numpy, Pandas, scikit learn, matplotlib)
• Machine Learning Use-Cases
• Machine Learning Categories
• Gradient Descent
• What is Machine Learning?
• Machine Learning Process Flow
• Linear Regression

• Linear Regression – Boston Dataset

• Machine Learning Concepts
• Linear Regression Implementation
• Machine Learning Types

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

• Implementation of Logistic Regression
• Random Forest
• Decision Tree

• Supervised Learning Concepts
• Evaluating Model Output
• Implementing different types of Supervised Learning Algorithms

• Introduction to Dimensionality
• PCA
• Scaling Dimensional Model
• Why Dimensionality Reduction
• Factor Analysis
• LDA

• PCA
• Scaling

• Implementing Dimensionality Reduction Technique

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

• Implementation of Naïve Bayes, SVM

• Supervised Learning Concepts
• Evaluating Model Output
• Implementing different types of Supervised Learning Algorithms

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyse the data.

Topics:

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

• Implementing K-means Clustering
• Implementing Hierarchical Clustering

• Unsupervised Learning
• Implementation of Clustering – Various Types

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with the Apriori Algorithm.

Topics:

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

• Apriori Algorithm
• Market Basket Analysis

• Data Mining using Python
• Recommender Systems using Python

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyse the data.

Topics:

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

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

• Implement Reinforcement Learning using Python
• Developing Q – Learning model in Python

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

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

• TSA in Python

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

• Cross-Validation
• AdaBoost

• Model Selection
• Boosting algorithm using Python

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

• Tuple – properties, Related Operations, compared with a list
• Dictionary – properties, related operations
• List – properties, related operations
• Set – properties, Related Operations

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

• NumPy – Arrays
• Indexing Slicing and iterating
• Pandas – Data Structures & Index Operations
• Matplotlib Library
• Markers, Colours, Fonts and Styling
• Contour Plots
• Operations on Arrays
• Reading and Writing Arrays on Files
• Reading and Writing Data from Excel/CSV formats into Pandas
• Grids, Axes, Plots
• Types of Plots – Bar Graphs, Pie Charts, Histograms

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

• Probability Distributions in Python
• Python for Data Visualisation

• Case Study 1: Maple Leaves Ltd is a start-up company that makes herbs from different types of plants and leaves. Currently, the system they use to classify the trees that they import in a batch is quite manual. A labourer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process.
• Case Study 2: BookRent is the largest online and offline book rental chain in India. The company charges a fixed fee per month plus rental per book. So, the company makes more money when the user rents more books. You are an ML expert and must model a recommendation engine so that the user gets a recommendation of books based on the behaviour of similar users. This will ensure that users are renting books based on their individual tastes. The company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User-Based vs Item Based. You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference.
• Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with a random forest classifier. Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy.
• Case Study 4: Principal component analysis using scikit learn. Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy. Using scikit learn to perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label.
• Case Study 5: Handling GIS data and working with maps. Creating, cleaning, collating and visualizing maps of India at different levels – state, district, taluka, and villages. Using Geo Pandas, Mapviz, and Leaflet in Python to perform spatial analytics and visualizing statistics with geographical context. Using public data of government expenditure, identify the areas and districts with the highest expenditure per capita in different states and all over India.

• Problem Statement: You, as ML expert, have to do analysis and modeling to predict the number of shares of an article given the input parameters.
• Actions to be performed: Load the corresponding dataset. Perform data wrangling, visualisation of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimise your model to the fullest.

• Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.
• Actions to be performed: You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across the years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components that explain the max variance.