PY201:

Python Fundamentals

• Introduction to Python and Data Science
• Data Structures in Python
• Control Structures and Functions
• Introduction to Object-Oriented
• Programming in Python
• File Handling in Python
• Regular Expressions
• Advanced-Data Structures in Python
• Functional Programming in Python

EXL301:

Data Analysis with Excel

• Unleashing the Power of Excel
• Mastering Data Entry and Formatting
• Cleaning and Manipulating Data
• Mastering Excel Functions
• Creating Stunning Visualizations
• Advanced-Data Analysis Techniques
• Pivot Tables and Pivot Charts
• Descriptive Statistics and Time Series Analysis
• Interactive Dashboards and Adv. Data Visualization
• Excel Macros

SQL103:

Data Analysis with SQL

• Getting Started with My SQL
• SQL Commands Fundamentals
• Data Types, Keywords, and Clauses in My SQL
• Functions in My SQL
• Aggregate Functions in My SQL
• My SQL Constraints
• My    SQL     Joins
• My SQL Subqueries
• Views and Stored Procedures in My SQL
• Window Functions in My SQL

SP101:

Statistics and Probability

• Introduction to Statistics
• Probability Theory
• Statistical Inference I
• Statistical Inference I I
• Regression Analysis I
• Regression Analysis I I

Semester Overview

In  this  Semester,  we  will  cover  three major  modules:  Python  for  Data Science  &  Visiualization  Data Analysis  and  Visualization,  Data Cleaning  and  preparation,  and Introduction  to  Machine  learning.

The Students will understand the basics of  Statistics  and  get  into data  analysis  and  visualization concepts.  Each  module  will  end  with a  guided  project,  and  the  semester will end with an introduction to machine  learning.

Objective

• Learners will understand the theoretical part and working of essential  Machine  Learning algorithms  used  in  real  world scenarios.
• Learners will be able to process different types of Datasets for performing Machine Learning Operations.
• Learners will be able to perform Regression  Analysis,  Classification Analysis  &  Clustering  Analysis  using Machine  Learning  Algorithms.
• Learners  will  be  able  to  create  full fledged  ML  solutions  catering  to wide  variety  of  solutions  with  well defined  data  pipelines.
• Learners will understand the process of model evaluation using various  machine  learning  metrices.

Training Materials

• Learners  will  get  access  to  special project  sessions  designed  for beginners to work on end-to-end Machine  Learning  problems.
• Learners  will  work  on  case  studies, articles,  journals,  research  papers specific to Machine Learning algorithms  and  applications.

Software Requirement

Operating  System:  Any,  Preferred: Mac OS,  Windows,  Linux.

Libraries:  Tensorflow,  Pytorch, Scikit-Learn

Processor:  intel  i 3  5 th  Gen  or  above.

RAM:  4  GB  or  above.

PY202:

Python for Data Science Visualization

• Num PY for Data Science
• Basics of Series and Data Frame Filtering using Pandas
• Extracting and Aggregating Pandas Matplotlib and Seaborn
• Plotly and Cuffinks

PY203:

Data Cleaning and Preparation

• Introduction to Data Cleaning and Data Types
• Exploring and Visualization the missing values
• Advanced-Data Cleaning Concepts
• Introduction to Feature Engineering
• Feature Extraction  and  Transformation
• Feature Selection and Dimensionality Reduction

PY204:

Machine Learning (scikit)

• Introduction to Machine Learning
• Understanding the Linear regression
• Different types of Regularization Methods
• Logistic // Binary Regression Modelling
• KNN, SVM and Naive Bayes
• Cluster Analysis with K-Means Algorithm
• Advanced Clustering Techniques

Semester Overview

In  this  semester,  we  are  going  to cover  four  modules,  namely,  Tree- Based  and  Boosting  Models,  Time Series  Analysis  and  Forecasting, Introduction to NLP and Introduction to  Deep  Learning.  The  Students  will go through each  topic  which  are based  on  core  Machine  learning concepts,  with  two  projects  in  each module.

Objective

• Learners can understand the fundamental  theories  related  to Deep  Learning  technology  &  Neural Networks.
• Learners  can  design  simple  to complex Neural Networks  using various frameworks presenting Convolutional  Neural  Networks  using Tensorflow.
• Learners will be introduced to the latest  technology  of  Transfer Learning and implement the concept of  Transfer Learning for  complex NLP  &  Vision  problems.
• Learners will be able to fetch Time Series Data using different methods
• Learners will understand auto regressive  models

Training Materials

Special  focused  training  and  course material  for  mastering  the  Tensorflow Library.

Case  study  sessions  for  learners where they will be able to learn –  How to  analyse  and  extract  meaningful  and useful  insights  using  Deep  Learning techniques  from  a  large  datasets.

Software Requirement

Operating  System:  Any,  Preferred: Mac OS,  Windows,  Linux.

Libraries:  Tensorflow,  Pytorch,  NLTK, Spacy.

Tools:  Airflow,  Google  Vertex. Processor: intel i 3 5 th Generation or above

RAM: 4 GB or above

PY205:

Tree Based and Boosting Models

• Decision Trees Random Forest
• Introduction to Boosting
• Imbalanced Machine Learning Models
• Factor of Imbalanced Machine Learning Models Techniques to Handle Imbalanced Data Introduction to Recommendation Engines

PY206:

Time Series Analysis and Forecasting

• Introduction to Time Series Analysis
• Preprocessing and Visualization of Time Series Data Time Series Forecasting using ARIMA
• Exponential Smoothing Models for Time Series Forecasting
• Machine Learning Models for Time Series Forecasting
• Time Series Forecasting using Prophet

PY207:

Natural Language Processing

• Introduction to NLP & Text Preprocessing Feature Extraction for NLP
• NLP Classification Deep Learning Models RNN, LSTM, Resnet, GRU
• Advance NLP Models Transformer and GPT-3

PY208:

Deep Learning Fundamentals

• Introduction of Deep learning CNN Models
• Transfer Learning Models
• NLP Deep Learning Models