DigiLifeStyle

DATA SCIENCE & AI

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

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.

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.

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)

DUAL CERTIFICATION

IN TWO MOST IN-DEMAND AND HIGHLY PAID SKILLS

DATA SCIENCE + Cyber security

SKILL-SETS COVERED

DATA ANALYTICS/ BUSINESS ANALYTICS

DATA VISUALIZATION

MACHINE LEARNING ALGORITHMS

STATISTICS

ENSEMBLE TECHNIQUES

DATA ANALYTICS/ BUSINESS ANALYTICS

FORECASTING ANALYTICS

GENERATIVE AI

TOOLS & TECHNOLOGIES

OUR STUDENTS COME FROM ALL EDUCATION BACKGROUNDS

MODE OF LEARNING

CLASSROOM
data science and ai at ravet pune
ONLINE

Data Science has a CLASSROOM + ONLINE training pattern where students have the flexibility to attend the sessions IN CLASSROOM as well as ONLINE.  Trainers conduct the training sessions live from Classrooms. All  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.