DATA SCIENCE & Cyber Security

In ravet

CORE MODULE

Learn from scratch the key concepts of Analytics and get trained on modelling techniques, with a focus on Python/R computing, machine learning, statistical concepts, BI dashboarding tools, and their real world business applications

DUAL CERTIFICATION

IN TWO MOST IN-DEMAND AND HIGHLY PAID SKILLS

DATA SCIENCE + Cyber security

CERTIFICATION

4 MONTHS

(100 HOURS WEEKEND COURSE)

DIPLOMA

4 MONTHS

(100 HOURS WEEKEND COURSE)

MASTER DIPLOMA/POST GRADUATE MASTER DIPLOMA

10 MONTHS

(100 HOURS WEEKEND COURSE) +

(6 MONTHS ON-JOB TRAINING AS DATA SCIENTIST)

SKILL-SETS COVERED

ADVANCED EXCEL

Pivot Tables for Data Summarization
Data Analysis and Visualization
Data Linking for Comprehensive Reports
Practical Exercise: Create a Pivot Table and build advanced charts to analyze data from different angles.

PYTHON FUNDAMENTALS

Python Basics and Operators
Control Flow with Conditional Statements
Python Data Types and Structures
Practical Exercise: Write Python code to solve simple programming problems, focusing on variables and operators.

LOOPS & FUNCTIONS IN PYTHON

Iterate with Loops in Python
Create and Use Python Functions
Advanced Data Manipulation with Lambda
Practical Exercise: Implement loops and functions to perform tasks such as data processing and automation.

NUMPY FUNDAMENTALS

Work with NumPy Arrays
Efficient Indexing and Slicing
Filtering and Boolean Indexing
Practical Exercise: Work with NumPy arrays to perform basic array operations, indexing, and filtering.

DATA MANIPULATION WITH PANDAS & DATA VISUALIZATION

Master Pandas Data Structures
Explore Data and Visualize Insights
Introduction to Version Control with Git
Practical Exercise: Load and explore a dataset using Pandas, and create basic data visualizations using Matplotlib and Seaborn.

INTRODUCTION TO SQL & BASIC QUERYING

SQL for Data Retrieval
Data Modeling Fundamentals
Advanced Data Sorting and Filtering
Practical Exercise: Write SQL queries to retrieve and manipulate data from a sample database.

ADVANCED SQL CONCEPT & DATA MANIPULATION

Temporary Tables and Documentation
Aggregations and Grouping Data
Advanced SQL Operations and Joins
Practical Exercise: Perform more complex SQL operations, such as joining and aggregating data.

FUNDAMENTALS OF STATISTICS & PROBABILITY

Understand Data Types
Central Tendency and Variance
Probability and Distribution Basics
Practical Exercise: Calculate mean, median, variance, and standard deviation for a dataset.

ADVANCED STATISTICS & HYPOTHESIS TESTING

Hypothesis Testing Techniques
Interpret Data Visualizations
Correlation, Regression, and ANOVA
Practical Exercise: Perform hypothesis tests and analyze real datasets using statistical techniques.

INTRODUCTION TO MACHINE LEARNING AND REGRESSION BASICS

Dive into Machine Learning
Data Preprocessing Essentials
Linear Regression for Predictive Modeling
Practical Exercise: Implement a simple linear regression model and evaluate its performance.

MULTIPLE LINEAR REGRESSION & MODEL EVALUATION

Evaluate Models with MAE, MSE, RMSE
Multiple Linear Regression
Practical Model Evaluation with Real Data
Practical Exercise: Build and evaluate a multiple linear regression model using a real-world dataset

LOGISTIC REGRESSION AND CLASSIFICATION METRICS

Master Logistic Regression
Classification Metrics for Model Assessment
ROC Curves and Model Performance
Practical Exercise: Train and evaluate logistic regression models for binary and multiclassclassification problems.

DECISION TREES AND ENSEMBLE METHODS

Understand Decision Trees
Prevent Overfitting and Tree Pruning
Explore Random Forest and Gradient Boosting
Practical Exercise: Create decision tree models and explore the power of ensemble methods.

MODEL EVALUATION AND VALIDATION TECHNIQUES

K-Fold Cross-Validation
Hyperparameter Tuning
In-Depth Classification Metrics
Practical Exercise: Apply K-fold cross-validation and hyperparameter tuning to improve model performance.

UNSUPERVISED LEARNING

Discover K-Means Clustering
Hierarchical Clustering Techniques
Clustering for Data Insights
Practical Exercise: Implement K-Means clustering and hierarchical clustering on real data.

DIMENSIONALITY REDUCTION AND FEATURE SELECTION

Reduce Dimensionality Effectively
Principal Component Analysis (PCA)
Feature Engineering for Improved Models
Practical Exercise: Apply PCA for dimensionality reduction and feature engineering to enhance model performance.

SUPPORT VECTOR MACHINES (SVM) AND K-NEAREST NEIGHBORS (KNN)

Classification with SVM
K-Nearest Neighbors for Predictions
Choose K and Distances
Practical Exercise: Build and evaluate SVM and KNN models for classifIcation problems.

ADVANCED ENSEMBLE LEARNING

Bagging, Stacking, and Blending
Explore Advanced Ensemble Algorithms
Harness the Power of XGBoost and LightGBM
Practical Exercise: Implement bagging, stacking, and advanced ensemble algorithms like XGBoost and LightGBM on a dataset

TIME SERIES MODELING WITH ARIMA AND SARIMA

Understand Time Series Data
Build ARIMA and SARIMA Models
Practical Forecasting and Model Evaluation
Practical Exercise: Analyze and forecast time series data using ARIMA and SARIMA models.

INTRODUCTION TO DEEP LEARNING

Overview of Artificial Neural Networks
Basic Deep Learning Concepts
Build and Train Simple Neural Networks
Practical Exercise: Build and train a simple neural network on a dataset using popular deeplearning frameworks.

DEEP LEARNING ARCHITECTURES AND TRAINING

Dive into CNNs and RNNs
Train Deep Learning Models
Avoid Overfitting with Regularization
Practical Exercise: Create and train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for various tasks.

NATURAL LANGUAGE PROCESSING (NLP)

Master NLP Essentials
Preprocess Text Data
Create Text Classification Models
Practical Exercise: Perform text preprocessing and build a text classification model using NLP techniques.

MODEL DEPLOYMENT

Understand Model Deployment
Set Up Deployment Environment
Secure, Monitor, and Optimize Deployed Models
Practical Exercise: Deploy a machine learning model as a web API and monitor its performance.

INTRODUCTION TO GENERATIVE AI

Types of Generative Models
Understanding Generative Adversarial Networks (GANs)
Understanding Variational Autoencoders (VAE)
Practical Exercise: Setting-up Python Environment and Deep Learning Libraries.

TEXT GENERATION WITH RECURRENT NEURAL NETWORKS (RNNS)

Introduction to Text Generation
Best Practices to Review Creative Text generation
Common Issues in Training RNNs
Practical Exercise: Building a text generator using RNNs.

INTRODUCTION TO TRANSFORMERS

RNN Vs Transformer Models
Overview of GPT-2 and BERT
NLP Applications and Text Generation with Transformers
Practical Exercise: Build a Language Model using GPT-2.

POWER BI

Introduction to Power BI
Data Transformation and Modeling
Create Interactive Dashboards
Practical Exercise: Transform data and create interactive dashboards in Power BI using real-world datasets.

TABLEAU

Explore Tableau Prep and Desktop
Visual Analytics and Calculations
Design Engaging Dashboards
Practical Exercise: Develop visualizations and dashboards in Tableau based on provided data.

INTRODUCTION TO R

Get Started with R
Work with Variables and Data Types
Handle Data Frames and Apply Functions
Practical Exercise: Perform data manipulation and analysis in R, including creating custom functions.

ADVANCED R PROGRAMMING

Data Frames and Custom Functions
Master Apply Functions
Work with Dates and Times in R
Practical Exercise: Utilize apply functions, handle dates and times, and work with data frames in R.

INDUCTION

Explore Data Science Opportunities
Gain Career-Ready Skills
Software Installation
Practical Exercise: Set up the required software tools and environment (Anaconda, Jupyter, etc.) on your computer.

FUNDAMENTALS OF EXCEL

Master Data Cleaning Techniques
Visualize Data with Excel Charts
Efficient Subtotaling and Analysis
Practical Exercise: Clean and analyze a provided dataset using Excel’s basic functions and charts.

TOOLS & TECHNOLOGIES

OUR STUDENTS COME FROM ALL EDUCATION BACKGROUNDS

MODE OF LEARNING

BIA^® has a CLASSROOM + ONLINE training pattern where students have the flexibility to attend the sessions IN CLASSROOM as well as ONLINE. BIA^® Trainers conduct the training sessions live from BIA^® Classrooms. All BIA^® sessions are live streamed for students from that batch, thus enabling students to attend the same sessions ONLINE and interact with the Trainer as well as other students.