Data Science with AI Training Course in Hyderbad
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DATA
SCIENCE
Real-time insights into the fastest growing tech sector of 2026
12 LPA
Average annual salary for Data Science professionals
25k+
Career opportunities related to Data Science
36%
Annual growth rate of data science job roles
Industry-Relevant Data Science Training at DIGIT innovations
Our data science training course at DIGIT innovations® equips you with essential skills in data manipulation, analysis, and modeling. Learn to clean and wrangle data (Pandas), perform statistical analysis (SciPy), visualize insights (Matplotlib/Seaborn), query databases (SQL), and build predictive models (Scikit-learn). Deploy ML models and interpret results for business decisions.
Our professional instructors bring a wealth of real-world experience and knowledge, offering personalized guidance and mentorship throughout the course. With hands-on projects, you will not only learn but also apply your skills in practical scenarios. The program also includes job-oriented training with mock tests and mock interviews.
Data Engineering & ETL
Machine Learning Models
Your Data Science Learning Journey
Unlock your potential in data science with DIGIT innovation's professionally curated curriculum. Join us and embark on a transformative journey to mastering data analytics tools and techniques.
Python Basics
Python Introduction & Setup
History of Python, installation using Anaconda, setting up environments, Jupyter Notebook, VSCode configuration.
Basic Programming Concepts
Variables, data types, operators, type conversions, input/output, scripting vs interactive mode.
Control Flow
Decision making using if/else, nested conditions, loops, practical scenario-based exercises.
Functions & Functional Programming
Defining functions, default/keyword arguments, recursion, lambda, map/filter/reduce, decorators.
Advanced Python
List/dict comprehensions, generators, iterators, context managers.
OOP in Python
Classes, objects, constructors, polymorphism, inheritance, abstraction, encapsulation.
Modules, Packages & File Handling
Working with CSV, JSON, Excel; using OS, sys modules; reading/writing files; exception handling.
Python Basics
Python Introduction & Setup
History of Python, installation using Anaconda, setting up environments, Jupyter Notebook, VSCode configuration.
Basic Programming Concepts
Variables, data types, operators, type conversions, input/output, scripting vs interactive mode.
Control Flow
Decision making using if/else, nested conditions, loops, practical scenario-based exercises.
Functions & Functional Programming
Defining functions, default/keyword arguments, recursion, lambda, map/filter/reduce, decorators.
Advanced Python
List/dict comprehensions, generators, iterators, context managers.
OOP in Python
Classes, objects, constructors, polymorphism, inheritance, abstraction, encapsulation.
Modules, Packages & File Handling
Working with CSV, JSON, Excel; using OS, sys modules; reading/writing files; exception handling.
Statistics & Maths
Descriptive Statistics
Mean, median, mode, variance, skewness, kurtosis, exploratory numerical summaries.
Probability & Distributions
Random variables, PMF, PDF, CDF, Bernoulli, Binomial, Normal, Poisson distributions.
Inferential Statistics
Hypothesis testing, p-values, Z-test, T-test, Chi-square test, ANOVA, confidence intervals.
Linear Algebra
Vectors, matrices, dot products, eigenvalues, eigenvectors, decomposition techniques (SVD).
Calculus for ML
Derivatives, chain rule, gradients, optimization concepts, cost minimization.
Statistics & Maths
Descriptive Statistics
Mean, median, mode, variance, skewness, kurtosis, exploratory numerical summaries.
Probability & Distributions
Random variables, PMF, PDF, CDF, Bernoulli, Binomial, Normal, Poisson distributions.
Inferential Statistics
Hypothesis testing, p-values, Z-test, T-test, Chi-square test, ANOVA, confidence intervals.
Linear Algebra
Vectors, matrices, dot products, eigenvalues, eigenvectors, decomposition techniques (SVD).
Calculus for ML
Derivatives, chain rule, gradients, optimization concepts, cost minimization.
ML & DL
( Machine Learning & Deep Learning )
ML Foundations
1 learning points
Regression Models
1 learning pointsClassification Models
1 learning pointsUnsupervised Learning
1 learning pointsFeature Engineering & Model Tuning
1 learning pointsNeural Network Basics
1 learning pointsANN using TensorFlow/Keras
1 learning points
CNN & Transfer Learning
1 learning pointsML & DL
ML Foundations
1 learning pointsRegression Models
1 learning pointsClassification Models
1 learning pointsUnsupervised Learning
1 learning pointsFeature Engineering & Model Tuning
1 learning pointsNeural Network Basics
1 learning pointsANN using TensorFlow/Keras
1 learning pointsCNN & Transfer Learning
1 learning pointsNLP
( Natural Language Processing )Text Preprocessing
Tokenization, stemming, lemmatization, stopword removal, normalization, POS tagging.
Text Vectorization
TF-IDF, Bag-of-Words, n-grams, Word2Vec, GloVe embeddings.
NLP Applications
Sentiment analysis, spam classification, topic modeling using LDA.
NLP
Text Preprocessing
Tokenization, stemming, lemmatization, stopword removal, normalization, POS tagging.
Text Vectorization
TF-IDF, Bag-of-Words, n-grams, Word2Vec, GloVe embeddings.
NLP Applications
Sentiment analysis, spam classification, topic modeling using LDA.
Big Data & Spark
PySpark Basics
1 TopicsRDDs, DataFrames, Spark SQL, transformations and actions.
MLlib
1 TopicsBuilding scalable ML models, pipeline creation, working with large datasets.
Big Data & Spark
PySpark Basics
1 TopicsRDDs, DataFrames, Spark SQL, transformations and actions.
MLlib
1 TopicsBuilding scalable ML models, pipeline creation, working with large datasets.
More Concepts
( Data Science )
NumPy
Arrays, slicing, indexing, broadcasting, vectorization, random sampling, matrix operations.
Pandas
DataFrames, merging, joining, grouping, aggregation, pivot tables, time-series data, window functions.
Data Cleaning & Wrangling
Handling missing values, duplicates, outliers, encoding techniques, datatype conversions.
Matplotlib
Plotting basics, customizing charts, multi-plot layouts, styling plots.
Seaborn
Distribution plots, categorical plots, statistical visualizations, heatmaps, pairplots.
Plotly
Interactive graphs, dashboards, map plots, animations, real-time visualization.
Project 1: End-to-End ML
Data cleaning, EDA, ML model building, optimization, saving and loading model.
Project 2: NLP
Text classification pipeline using TF-IDF or embeddings.
Project 3: Big Data
Customer segmentation using PySpark and MLlib.
More Concepts
NumPy
Arrays, slicing, indexing, broadcasting, vectorization, random sampling, matrix operations.
Pandas
DataFrames, merging, joining, grouping, aggregation, pivot tables, time-series data, window functions.
Data Cleaning & Wrangling
Handling missing values, duplicates, outliers, encoding techniques, datatype conversions.
Matplotlib
Plotting basics, customizing charts, multi-plot layouts, styling plots.
Seaborn
Distribution plots, categorical plots, statistical visualizations, heatmaps, pairplots.
Plotly
Interactive graphs, dashboards, map plots, animations, real-time visualization.
Project 1: End-to-End ML
Data cleaning, EDA, ML model building, optimization, saving and loading model.
Project 2: NLP
Text classification pipeline using TF-IDF or embeddings.
Project 3: Big Data
Customer segmentation using PySpark and MLlib.
Power BI
Introduction, Case Studies & Power BI
Case Studies and Discussion & Power BI
Reviewing case studies of Python usage in data analysis
Q&A and discussions on best practices
Introduction to Power BI
Understanding the Power BI interface
Importing data from different sources
Transforming and shaping data within Power BI
Power BI
Data Modeling and Relationships in Power BI
Creating a data model in Power BI
Understanding relationships between tables
Implementing calculated columns and measures
Using DAX (Data Analysis Expressions) for advanced calculations
Visualizations and Interactivity
Creating common visualizations (bar charts, line charts, etc.)
Customizing visualizations for better insights
Adding interactivity to reports and dashboards
Implementing drill-through actions for detailed analysis
The Art of Storytelling with Data
Principles of Effective Data Storytelling
Importance of narrative in data presentations
Building a cohesive narrative in Power BI
Using bookmarks and storytelling features
Power BI for Real-Time Analytics and Advanced Features
Real-Time Dashboards
Setting up real-time data streaming in Power BI
Creating dashboards for live data monitoring
Advanced Features and Custom Visuals
Exploring custom visuals and visuals from the marketplace
Leveraging advanced features like forecasting and clustering
Case Studies and Discussion
Reviewing case studies of effective Power BI usage
Q&A and discussions on best practices in storytelling with data
Power BI
Introduction, Case Studies & Power BI
Case Studies and Discussion & Power BI
Reviewing case studies of Python usage in data analysis
Q&A and discussions on best practices
Introduction to Power BI
Understanding the Power BI interface
Importing data from different sources
Transforming and shaping data within Power BI
Power BI
Data Modeling and Relationships in Power BI
Creating a data model in Power BI
Understanding relationships between tables
Implementing calculated columns and measures
Using DAX (Data Analysis Expressions) for advanced calculations
Visualizations and Interactivity
Creating common visualizations (bar charts, line charts, etc.)
Customizing visualizations for better insights
Adding interactivity to reports and dashboards
Implementing drill-through actions for detailed analysis
The Art of Storytelling with Data
Principles of Effective Data Storytelling
Importance of narrative in data presentations
Building a cohesive narrative in Power BI
Using bookmarks and storytelling features
Power BI for Real-Time Analytics and Advanced Features
Real-Time Dashboards
Setting up real-time data streaming in Power BI
Creating dashboards for live data monitoring
Advanced Features and Custom Visuals
Exploring custom visuals and visuals from the marketplace
Leveraging advanced features like forecasting and clustering
Case Studies and Discussion
Reviewing case studies of effective Power BI usage
Q&A and discussions on best practices in storytelling with data
AI & GenAI
AI & Generative AI Fundamentals
What is AI, Machine Learning, Deep Learning, and Generative AI?
Simple analogies with real-world examples
Understanding where AI is used in daily life
Why Generative AI matters in modern data science workflows
How Large Language Models Work
High-level understanding of LLM training
Tokens, context window, and parameters
How models generate responses
Why LLM outputs can sometimes be inaccurate
Popular AI Models in 2026
GPT series and when to use it
Gemini strengths and use cases
Claude for reasoning and writing
Grok and Llama overview
Prompt Engineering Basics
Zero-shot and Few-shot prompting
Chain-of-thought prompting basics
Role-playing and structured prompts
Output formatting with JSON
Temperature, top-p, and system prompts
Limitations, Ethics & Responsible AI
Hallucinations and bias in AI
Context loss and response limitations
Ethical concerns in production and analytics use cases
Cost awareness and token usage
AI APIs vs Open Source Models
Difference between AI APIs and open-source models
When to choose hosted APIs
When open-source models make sense
Basic introduction to AI app architecture
Python, SQL & Data Analysis with AI
Using AI to assist Python coding in notebooks
Writing and optimizing SQL queries with AI support
Using AI for data cleaning, wrangling, and feature creation
Generating quick insights from structured datasets
Data Preparation, RAG & Data Storage
Saving datasets, model outputs, and analysis notes
Introduction to RAG and why it is useful in data science
Storing documents and retrieving relevant parts
Basic input validation and prompt injection protection
AI for EDA, Visualization & Model Workflows
Calling AI tools from Python data workflows
Building AI-assisted exploratory data analysis summaries
Markdown rendering and loading states in notebook-driven apps
Supporting model evaluation, reporting, and business insights
Sharing results through reports, notebooks, or cloud platforms
AI & GenAI
AI & Generative AI Fundamentals
What is AI, Machine Learning, Deep Learning, and Generative AI?
Simple analogies with real-world examples
Understanding where AI is used in daily life
Why Generative AI matters in modern data science workflows
How Large Language Models Work
High-level understanding of LLM training
Tokens, context window, and parameters
How models generate responses
Why LLM outputs can sometimes be inaccurate
Popular AI Models in 2026
GPT series and when to use it
Gemini strengths and use cases
Claude for reasoning and writing
Grok and Llama overview
Prompt Engineering Basics
Zero-shot and Few-shot prompting
Chain-of-thought prompting basics
Role-playing and structured prompts
Output formatting with JSON
Temperature, top-p, and system prompts
Limitations, Ethics & Responsible AI
Hallucinations and bias in AI
Context loss and response limitations
Ethical concerns in production and analytics use cases
Cost awareness and token usage
AI APIs vs Open Source Models
Difference between AI APIs and open-source models
When to choose hosted APIs
When open-source models make sense
Basic introduction to AI app architecture
Python, SQL & Data Analysis with AI
Using AI to assist Python coding in notebooks
Writing and optimizing SQL queries with AI support
Using AI for data cleaning, wrangling, and feature creation
Generating quick insights from structured datasets
Data Preparation, RAG & Data Storage
Saving datasets, model outputs, and analysis notes
Introduction to RAG and why it is useful in data science
Storing documents and retrieving relevant parts
Basic input validation and prompt injection protection
AI for EDA, Visualization & Model Workflows
Calling AI tools from Python data workflows
Building AI-assisted exploratory data analysis summaries
Markdown rendering and loading states in notebook-driven apps
Supporting model evaluation, reporting, and business insights
Sharing results through reports, notebooks, or cloud platforms
Track Your Progress Toward Enrollment
Follow these four milestones to transform your career. From registration to your first class, we've made it seamless.
Registration
Register online, provide details, help admissions understand your career goals.
Test
Submit application, then an assessment ensures eligibility and program aptitude.
Offer Applicable
After review, eligible candidates get scholarship offers and confirmation email.
Fee Payment
Review your admission offer, promptly pay fee to confirm enrollment.
Mentor Community
Our mentor community at DIGIT Innovations believes in the power of sharing. We partner with experienced professionals from leading companies to guide you through real projects, career advice, and hiring-ready skills.
Advance Your Career with Our Data Science Certification
Global Industry Recognition
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Practical Skill Validation
Our certifications validate your skills through practical application, equipping you for the workforce and demonstrating your expertise to employers.
Career Advancement
Achieve new heights in your professional journey with our certifications.
Shared Experiences from Our Students
Companies Hiring
DIGIT innovations Alumni
Our alumni work with leading companies worldwide, delivering real-world impact.
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FAQ
Frequently Asked Questions
Everything you need to know about the course. Find answers to common queries regarding enrollment, curriculum, and career paths.
Talk to usNo prior experience is required, although familiarity with basic programming and math concepts can be beneficial. Our course caters to beginners as well as those looking to enhance their existing skills.
Absolutely! We provide career support services including resume building, mock tests, mock interviews, and job placement assistance to help you kickstart your career in data science.
Yes, our course includes hands-on projects and practical assignments that simulate real-world scenarios. You'll have the opportunity to apply your skills in various data environments.
Our course is designed and delivered by industry professionals with extensive experience in data science. We offer a comprehensive curriculum, personalized instruction, and a supportive learning environment to ensure your success.
Enrolling is easy! Simply visit our website or contact our admissions team for more information on course schedules, fees, and enrollment procedures.