DataScience Online Training in Hyderabad
with
100% Placement Assistance
Data Science Training in Hyderabad
1. Introduction to Data Science
What is Data Science?
Applications of Data Science
Data Science lifecycle
Roles: Data Analyst vs Data Scientist vs Data Engineer
2. Programming for Data Science
Python for Data Science
Python basics: variables, data types, loops, functions
NumPy and Pandas for data manipulation
Data visualization with Matplotlib & Seaborn
Introduction to Jupyter Notebooks
3. Statistics & Probability
Descriptive statistics (mean, median, mode, standard deviation)
Probability theory basics
Distributions: Normal, Binomial, Poisson
Hypothesis testing
Correlation & Regression
4. Data Wrangling & Cleaning
Handling missing data
Outlier detection and treatment
Data transformation and normalization
Feature engineering
5. Exploratory Data Analysis (EDA)
Univariate & Bivariate analysis
Visualization techniques
Data patterns and trends
Dashboard tools (optional: Power BI or Tableau basics)
6. Machine Learning Fundamentals
Supervised vs Unsupervised Learning
Model training and evaluation
Algorithms:
Linear Regression
Logistic Regression
Decision Trees
Random Forest
KNN
Naïve Bayes
SVM
K-Means Clustering
7. Model Evaluation & Optimization
Confusion Matrix, Accuracy, Precision, Recall, F1 Score
ROC-AUC Curve
Cross-validation
Hyperparameter tuning (GridSearch, RandomizedSearch)
8. SQL for Data Science
Basics of SQL: SELECT, WHERE, GROUP BY
JOINS and Subqueries
Window functions
Integrating SQL with Python
9. Deep Learning Basics (optional for advanced track)
Introduction to Neural Networks
TensorFlow/Keras basics
ANN, CNN basics
10. Real-World Projects
Predictive analytics (e.g., Sales Forecasting)
Customer segmentation
Sentiment analysis
Healthcare or Finance datasets
11. Capstone Project
End-to-end project from data cleaning to model deployment
Presentation and storytelling with data
12. Resume Preparation & Interview Training
Portfolio development
Mock interviews
GitHub & LinkedIn profile optimization
Data Science Online Training
Experienced and Certified Trainers
Flexibility with Learning Modes
100% Career Support
Affordable and Value-Driven
Comprehensive Data Science Training
Industry-Relevant Curriculum
Latest Tools and Technologies
Industry projects
Data Science Online Course & Certifications
What is Datascience
Data Science is a field that combines programming, statistics, and domain knowledge to extract meaningful insights and knowledge from data. It involves collecting, cleaning, analyzing, and visualizing data to help businesses or organizations make better decisions.
At [Hope Infotech], we are passionate about transforming careers through high-quality, industry-relevant training in Data Science and related technologies. With a team of experienced trainers, real-world projects, and a learner-focused approach, we aim to bridge the gap between academic learning and practical application.
Our mission is to empower students, working professionals, and businesses with the skills and confidence to succeed in the data-driven world. Whether you’re starting your career or looking to upgrade your skill set, our courses are designed to provide hands-on experience, personalized mentorship, and career support.
Data Science Training Online
Why is Datascience so popular
1. Explosion of Data
We are generating data at an unprecedented rate—through apps, social media, sensors, e-commerce, and more. Data Science helps make sense of this massive volume of information.Data Science is so popula
2. High Demand for Data-Driven Decisions
Companies today rely on data to understand customer behavior, predict trends, optimize operations, and gain a competitive edge. Data Science provides the tools to do all of that effectively.
3. Better Career Opportunities
Data Science has become one of the highest-paying and fastest-growing fields, with roles like: Data Scientist Data Analyst Machine Learning Engineer Business Intelligence Analyst
4. Wide Range of Applications
It’s used across many industries, including: Healthcare (disease prediction) Finance (fraud detection) Marketing (customer targeting) Retail (recommendation systems) Manufacturing.
5. Advancements in AI & Machine Learning
With modern algorithms and computing power, Data Science can now solve complex problems—like language translation, image recognition, and predictive analytics—that were impossible.
6. Open Tools & Community
Popular tools like Python, R, and open-source libraries (e.g., Pandas, Scikit-learn, TensorFlow) make it accessible to learn and experiment with. improve efficiency, and create new business opportunities.
Data Science Online Course for beginners
Data Science Tutorial
What is Datascience Used for?
Data Science is used to analyze and interpret large volumes of data to solve real-world problems and support decision-making across various industries. It turns raw data into meaningful insights, predictions, and automation. Here’s how it’s used in different domains:
1. Business Intelligence
Understanding customer behavior
Market trend analysis
Sales forecasting
Inventory optimization
2. Healthcare
Predicting disease outbreaks
Diagnosing illnesses using medical data
Personalizing treatment plans
Drug discovery and genomics
3. Finance
Fraud detection
Credit scoring and risk assessment
Stock market prediction
Algorithmic trading
4. Marketing
Targeted advertising
Customer segmentation
Sentiment analysis (social media, reviews)
Campaign performance tracking
5. Manufacturing
Predictive maintenance of machines
Quality control and defect detection
Process automation
Supply chain optimization
6. Retail and E-commerce
Product recommendation systems
Price optimization
Customer churn prediction
Inventory demand forecasting
7. Transportation & Logistics
Route optimization
Traffic pattern analysis
Self-driving vehicle algorithms
Delivery time prediction
8. Sports & Entertainment
Player performance analysis
Fan engagement insights
Content recommendation (Netflix, YouTube)
Ticket sales prediction
Data Science Course fees
Data Science Training in Hyderabad
| Feature | Data Science | Data Analytics | Machine Learning | Artificial Intelligence (AI) |
|---|---|---|---|---|
| Focus | Extract insights from data | Analyze historical data | Enable systems to learn from data | Mimic human intelligence |
| Goal | Decision support and automation | Business reporting and trend analysis | Predictions and pattern recognition | Smart behavior and automation |
| Techniques Used | ML, stats, visualization, data wrangling | SQL, Excel, BI tools | Supervised & unsupervised algorithms | ML, deep learning, NLP, robotics |
| Tools/Languages | Python, R, SQL, Jupyter | Excel, Power BI, Tableau | Python, TensorFlow, Scikit-learn | Python, Keras, PyTorch, OpenAI |
| Output | Actionable insights, models | Dashboards, reports | Predictive models | Intelligent agents/systems |
| Data Type | Structured + Unstructured | Mostly structured | Structured and semi-structured | All data types, including multimedia |
| Scope | Broad (includes ML, AI, analytics) | Narrower (part of Data Science) | Subset of Data Science and AI | Broader, includes ML, logic, perception |
Data Science Certifications
🧠 1. Foundational Knowledge
💻 3. Programming and Data Handling
📈 5. Exploratory Data Analysis (EDA)
⚙️ 7. Model Deployment and Tools
📚 9. Ethics and Responsibility
🧮 2. Mathematics and Statistics
. 📊 4. Data Wrangling and Cleaning
🤖 6. Machine Learning
📢 8. Communication and Storytelling
🔁 10. Continuous Learning
Data Science Online Training for beginners
Prerequisites of Data Science Course Here
Basic Knowledge of Mathematics
Understanding of high school-level algebra and statistics Familiarity with concepts like mean, median, probability, and standard deviation
Analytical Thinking
Ability to solve problems logically Curiosity to ask questions and interpret data-driven insights, making logical decisions on evidence and reasoning.
Basic Computer Skills
Comfortable using a computer, installing software, and navigating file systems Experience with Excel or Google Sheets is helpful
Programming Fundamentals
Prior exposure to any programming language (like Python or C) is a plus If not, the course should include an introductory module in Python
Interest in Data and Technology
Willingness to work with numbers, datasets, and analytical tools With digital transformation accelerating globally, the demand for skilled professionals
Recommended
Exposure to Excel or SQL Basic understanding of business processes or domain-specific knowledge (e.g., finance, marketing)
data science using python for beginners
1. Aspiring Data Scientists
Individuals who want to build a career analyzing data, developing models, and extracting insights.
Ideal for those with a background in:
Math, statistics, computer science, engineering, economics, or physics.
2. Software Developers & Engineers
Those looking to transition into machine learning, AI, or data-heavy roles.
Gain the ability to integrate predictive models into applications or optimize systems based on data.
3. Data Analysts
Analysts who want to move beyond Excel or BI tools into more advanced data modeling and automation.
Learn tools like Python, SQL, and machine learning to expand their skill set.
4. Scientists & Researchers
Academic or industry researchers using data to draw conclusions or publish results.
Improve reproducibility, analysis depth, and automation in research
5. Business & Marketing Professionals
Learn to make data-driven decisions, run experiments (like A/B testing), and interpret dashboards.
Understand customer behavior, segmentation, and campaign performance.
6. Managers & Decision Makers
Gain foundational data literacy to make informed decisions and work more effectively with data teams.
Understand KPIs, predictive models, and data ethics.
7. Students & Graduates
Especially those in STEM or business programs.
Build a competitive edge and open doors to internships and tech/data careers.
8. Career Changers
Anyone looking to move into a future-proof, high-demand field.
Data science roles span industries: healthcare, finance, tech, e-commerce, manufacturing, and government.
data science career path after graduation
data science roadmap 2025
Data Science Training in Hyderabad
What You’ll Learn
Fundamentals of data science and data analysis
Python and R for data manipulation
Machine Learning (Supervised & Unsupervised)
Deep Learning and Neural Networks
Data Visualization with tools like Matplotlib, Seaborn, and Power BI
SQL and NoSQL databases
Data wrangling and preprocessing techniques
Real-world project deployment using cloud platforms (AWS, GCP, Azure)
⏱️ Course Duration
Total Duration: 5 to 6 Months
Recommended Weekly Commitment: 6 to 10 hours/week
Includes:
Live sessions/recorded video lessons
Assignments & practice exercises
Real-world projects
Capstone project
Career support & mock interviews
🛠️ Tools & Technologies Covered
Languages: Python, R, SQL
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras
Tools: Jupyter Notebook, VS Code, GitHub, Tableau, Power BI
Databases: MySQL, MongoDB
Cloud: AWS S3, EC2, GCP BigQuery, Azure ML Studio
📊 Who Should Enroll
Aspiring Data Scientists & Developers
Software Engineers seeking data roles
Analysts looking to upskill in AI/ML
Students and freshers interested in data careers
📦 Capstone Projects
Work on industry-specific use cases in:
Healthcare: Disease prediction
Retail: Customer segmentation
Finance: Credit scoring model
E-commerce: Recommendation system
Data Science Training Courses
Data Science Training in Hyderabad
| Feature / Mode | Classroom Training | Live Online Training | Self-Paced Learning | Blended Learning | Corporate Training |
|---|---|---|---|---|---|
| Interaction with Trainer | 👨🏫 High | 👨💻 High | ❌ None | ⚖️ Medium | 👔 High |
| Flexibility | ❌ Fixed Schedule | ✅ Moderate | ✅ High | ✅ High | ✅ Customized |
| Access to Recordings | ❌ No | ✅ Yes | ✅ Yes | ✅ Yes | ✅ On request |
| Hands-on Projects | ✅ Yes | ✅ Yes | ✅ Limited | ✅ Yes | ✅ Yes |
| Peer Networking | ✅ Strong | ✅ Moderate | ❌ Minimal | ✅ Moderate | ✅ Team-Based |
| Ideal For | Students, Job Seekers | Remote Learners, Working Professionals | Busy Professionals | Working Professionals | Company Teams |
| Support & Mentorship | ✅ Immediate | ✅ Scheduled | ❌ Limited | ✅ Balanced | ✅ Dedicated |
| Certifications Provided | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
Data Science Training Institute
🔍 Top Career Roles in Data Science
| Job Title | Description |
|---|---|
| Data Scientist | Builds models, interprets data, and provides strategic business insights. |
| Data Analyst | Focuses on data visualization, dashboards, and statistical analysis. |
| Machine Learning Engineer | Designs and deploys machine learning models into production systems. |
| Data Engineer | Develops data pipelines and architecture for managing big data. |
| Business Intelligence Analyst | Translates data into actionable insights for business growth. |
| AI/Deep Learning Engineer | Specializes in neural networks and deep learning algorithms. |
| Research Scientist (AI/ML) | Conducts experiments and innovations in AI/ML fields. |
| Big Data Developer | Works with Hadoop, Spark, and distributed computing frameworks. |
Data Science Training Material
Data Science Tools Covered in the Course
Programming & Scripting Languages
Python—Core language for data science and machine learning
R—Statistical computing and data visualization
SQL—Querying structured data from relational databases
Libraries & Frameworks
-
NumPy—Numerical operations and array processing
-
Pandas—Data manipulation and analysis
-
Matplotlib/Seaborn—Data visualization
-
Scikit-learn—Machine learning models and evaluation
-
TensorFlow/Keras/PyTorch—Deep learning frameworks
Data Visualization & BI Tools
-
Tableau—Interactive data dashboards and storytelling
-
Power BI—Business intelligence reporting and insights
-
Matplotlib/Plotly—Custom graphs and charts in Python
Databases
-
MySQL/PostgreSQL—Relational database systems
-
MongoDB—NoSQL database for handling unstructured data
-
SQLite—Lightweight embedded database
☁️ Cloud Platforms
AWS (S3, EC2, SageMaker)—Cloud storage, computing & ML
Google Cloud Platform (BigQuery, AutoML)
Azure (ML Studio)—Visual ML development and deployment
⚙️ Development & Collaboration Tools
Jupyter Notebook—Interactive coding and analysis
Google Colab—Cloud-based notebook for Python
Git & GitHub—Version control and project collaboration
VS Code—Code editor for Python, SQL, and notebooks
🔐 Optional Tools (Advanced Track)
Apache Spark—Distributed big data processing
Hadoop—Data storage and batch processing
Airflow—Workflow orchestration for pipelines
Docker—Containerizing and deploying ML applications
Data Science Training in Hyderabad
1. Statistical & Analytical Thinking
Descriptive and inferential statistics
Probability distributions and hypothesis testing
Correlation and regression analysis
2. Programming Proficiency
Writing efficient Python and R code
Data manipulation using Pandas and NumPy
SQL for querying structured databases
Automation using scripting
3. Data Wrangling & Cleaning
Handling missing, inconsistent, and noisy data
Transforming raw datasets into structured formats
Feature engineering for ML readiness
4. Data Visualization
Building dashboards and charts using Matplotlib, Seaborn, Tableau, and Power BI
Telling stories with data using visual insights
Creating interactive and real-time visualizations
5. Machine Learning & Predictive Modeling
Supervised & Unsupervised Learning (e.g., Linear Regression, Decision Trees, Clustering)
Model training, validation, and hyperparameter tuning
Evaluation using precision, recall, F1-score, ROC-AUC
6. Deep Learning (Advanced Track)
Neural networks using TensorFlow and Keras
CNNs for image data, RNNs for sequence data
Model deployment using cloud-based services
7. Data Engineering Foundations
Building and managing data pipelines
Working with large datasets using Spark
Basics of cloud data warehousing (BigQuery, Redshift)
8. Deployment & Real-world Applications
Deploying models using Flask and FastAPI
Cloud deployment on AWS/GCP
Real-time prediction systems
.
9. Problem Solving & Business Insight
Translating business problems into analytical solutions
Communicating insights to non-technical stakeholders
Making data-driven strategic decisions
Data Science Training Videos
Data Science Training in Hyderabad
Data Science tutorial for beginners
Hiring Industries
| Company | Industry | Hiring Focus |
|---|---|---|
| Amazon | E-commerce & Cloud | Platforms, logistics, recommendations |
| Google (Alphabet) | Tech & AI | Search, ML, autonomous systems |
| Microsoft | Software & Cloud | Azure, BI, cybersecurity |
| IBM | AI & Enterprise | Watson, NLP, quantum research |
| Meta | Social Media & Ads | User analytics, content optimization |
| Netflix | Streaming | Content recommendations & engagement |
| J.P. Morgan & Finance Firms | Finance | Risk, fraud detection, trading analytics |
| TCS, Wipro, Capgemini | IT Services (India) | Consulting & analytics |
Data Science Software Training
Job roles and responsibilities
1. Data Scientist
Responsibilities:
Collect, process, and analyze large datasets to extract actionable insights
Build predictive models and machine learning algorithms
Perform statistical analysis and develop data-driven strategies
Communicate results using data visualizations and storytelling
Collaborate with product, engineering, and business teams
Skills Needed:
Python, R, SQL
Scikit-learn, TensorFlow, Keras
Strong grasp of statistics and machine learning
2. Data Analyst
Responsibilities:
Analyze data to identify patterns, trends, and correlations
Create dashboards, charts, and reports using visualization tools
Provide insights to support decision-making processes
Conduct A/B testing and performance analysis
Skills Needed:
Excel, SQL, Power BI, Tableau
Descriptive statistics
Basic Python or R knowledge
3. Machine Learning Engineer
Responsibilities:
Design and deploy scalable machine learning models
Select appropriate ML algorithms based on the problem
Optimize model performance for production use
Work with software engineers to integrate models into applications
Skills Needed:
Deep knowledge of ML/DL algorithms
Experience with TensorFlow, PyTorch, and cloud ML tools
Strong programming and software engineering skills
4. Data Engineer
Responsibilities:
Build and maintain data pipelines and ETL processes
Manage large volumes of structured and unstructured data
Design and implement scalable data architectures
Ensure data quality, security, and accessibility
Skills Needed:
Python, Java, Scala
Hadoop, Spark, Kafka
SQL, NoSQL, data warehousing
5. Business Intelligence (BI) Analyst
Responsibilities:
Use business data to generate performance insights
Design and maintain dashboards and visualization reports
Support stakeholders with regular and ad-hoc reporting
Translate business needs into technical data solutions
Skills Needed:
Tableau, Power BI
SQL, Excel
Data storytelling and presentation skills
6. AI/Deep Learning Engineer
Responsibilities:
Develop train deep learning models for image, audio, text data
Implement neural networks such as CNNs, RNNs, transformers
Work on cutting-edge AI applications chatbots image recognition
Optimize training for speed and performance
Skills Needed:
TensorFlow, Keras, PyTorch
NLP libraries (Hugging Face, SpaCy)
GPU computing, cloud deployment
7. Data Architect
Responsibilities:
Design and manage the data infrastructure of an organization
Define data storage, integration, and retrieval methods
Ensure compliance with data governance and privacy policies
Guide engineers on best practices for data modeling
Skills Needed:
Data modeling and architecture
SQL, Spark, Redshift, Snowflake
Cloud platforms like AWS, Azure
8. Statistician
Responsibilities:
Use statistical methods to collect, analyze, and interpret data
Design surveys, experiments, and research studies
Develop models for prediction and testing hypotheses
Skills Needed:
R, SAS, Python
Probability, hypothesis testing
Report writing and analysis
9. NLP Engineer
Responsibilities:
Develop systems to understand, interpret, generate human language
Build chatbots, voice assistants, and language translation tools
Work with large-scale text data and pre-trained language models
Skills Needed:
NLTK, SpaCy, Hugging Face Transformers
BERT, GPT, LLMs
Text classification, summarization, sentiment analysis
10. Data Science Consultant
Responsibilities:
Analyze business problems and recommend data-driven solutions
Work across domains to implement predictive analytics AI tools
Provide strategic advice to stakeholders based on insights from data
Skills Needed:
Communication and client-facing skills
Knowledge of data science tools and business domains
Project management and solution design
Data Science Interview Questions
🔍 1. Surging Demand & Market Growth
The global data science and predictive analytics industry is expanding rapidly—from ~$104 billion in 2023 to a projected ~$777 billion by 2032, a CAGR near 25%.
Job growth is strong, with data scientist roles expected to increase ~42% from 2023–2033—far outpacing average growth.
📡 2. Real-Time Analytics, Edge Computing & IoT
Edge computing enables processing data closer to devices, enabling low-latency insights—75% of enterprise data may be processed at the edge by 2025 (GeeksforGeeks+1Mygreatlearning+1).
IoT proliferation (~27 billion connected devices by 2025) fuels demand for real-time streaming analytics in manufacturing, smart cities, and healthcare.
🤖 3. AI Automation: AutoML, Augmented Analytics & GenAI
AI-powered tools are automating data prep, feature engineering, modeling, and report generation—AutoML and augmented analytics are getting mainstream.
Generative AI and foundation models (like GPT and Gemini) are being embedded into analysis workflows for code writing, visualization, data storytelling, and agentic workflows.
🔐 4. Synthetic Data & Privacy
Synthetic data is widely used to fill gaps in training data, preserve privacy, and bypass data scarcity.
Privacy-critical techniques (differential privacy, federated learning) are gaining traction under tightening regulations like GDPR/CCPA
🧭 5. Explainability, Ethics & Responsible AI
Explainable AI (XAI) and fairness tools are essential as AI models influence critical decisions.
Governance frameworks and DataOps practices (applying DevOps to analytics pipelines) help ensure reliable, compliant, and collaborative data operations.
🧠 6. Graph Analytics & MLOps
Graph-based analysis for networks (e.g., fraud, recommendations) is entering the mainstream.
Full-stack MLOps and end-to-end AI platforms (like Dataiku) facilitate deployment, monitoring, and maintenance. A market trend refers to the direction in which a market is moving over a specific period. It helps businesses.
🌐 7. Convergence: Edge, Quantum & Hybrid Infrastructure
Quantum computing research is making headway—with potential game-changing impacts in optimization, finance, and drug discovery.
Hybrid setups combining cloud, edge, and on-prem systems address performance, sovereignty, cost, and environmental concerns.
- It helps businesses, investors, and professionals identify potential opportunities
📈 8. Talent & Recruitment Dynamics
Data scientists with skills in AI/ML command premium salaries—tech firms offer signing bonuses up to ~$200K.
There’s a growing need for cross-functional expertise—data engineers, MLOps specialists, and hybrid roles bridging development and science
The data science market is experiencing rapid growth due to increased demand for AI, real-time analytics, and data-driven decision-making across industries. Trends include automation (AutoML), synthetic data, MLOps, and ethical AI practices.
Industries such as
Healthcare
Finance & Insurance
Retail & E-commerce
Manufacturing (IoT-based)
IT & Software Services
are leading in hiring data scientists globally.
Python, SQL, R
Machine Learning & Deep Learning
Data Visualization (Power BI, Tableau)
Cloud platforms (AWS, GCP, Azure)
MLOps & AutoML
Big Data tools (Spark, Hadoop)
Data science continues to be a top career path. High salary, job flexibility, and demand across multiple sectors make it highly attractive. AI integration has also expanded roles and opportunities.
Massive data generation from digital platforms
AI & ML integration in business operations
Need for real-time analytics and forecasting
Regulatory compliance and data governance
Salaries vary by location and experience:
Entry-level: ₹6–10 LPA (India), $90,000+ (US)
Mid-level: ₹12–20 LPA, $120,000+
Senior roles: ₹25–50 LPA+, $150,000+
AI experience boosts pay significantly.
Jupyter, Colab (Python IDEs)
PyTorch, TensorFlow (ML/DL)
Snowflake, Databricks (data platforms)
MLflow, Kubeflow (MLOps)
Hugging Face, Open AI tools (Gen AI)
Automation enhances productivity but cannot fully replace human reasoning, domain expertise, or ethical decision-making—data scientists now focus more on high-level tasks, storytelling, and strategy.
IBM Data Science Professional Certificate
Microsoft Azure Data Scientist
Google Cloud Data Engineer
AWS Machine Learning Specialty
DASCA Certifications
Follow sites like Analytics India Magazine, Kaggle, Towards Data Science
Attend webinars, workshops, and conferences
Read market reports (Gartner, McKinsey)
Join LinkedIn groups and tech communities
Hopeinfotech is the best software training institute in Hyderabad, India. It deals with all the ways to make a professional.
Useful Links
Contact Information
Flat No. 403, 4th Floor, Naga Sai Nivas, Prime Hospital Lane, Ameerpet, Hyderabad, Telangana 500016.
Phone: +919951609609
WhatsApp: +919951609609
Email: hopeinfotech@gmail.com