Data Science & Machine Learning Training in Dilsukhnagar, Hyderabad
Work through data preparation, statistics, machine learning, model evaluation and real-world applications.
Course overview
Work through data preparation, statistics, machine learning, model evaluation and real-world applications.
Data Science & Machine Learning learning roadmap
Use this roadmap to understand the subject and discuss your goals. Confirm the exact syllabus, tools and practical access for the batch you choose.
Frame a useful question
Define a prediction or analysis goal, the unit being measured and what a useful answer would change. Identify what data is available before choosing a model.
Prepare and explore data
Check types, duplicates, missing values and unusual records. Use summaries and visual exploration to understand distributions and relationships rather than treating cleaning as a single automatic step.
Build statistical foundations
Connect sampling, variation and uncertainty with practical questions. Recognise when a result describes an association and when the evidence is insufficient for a causal claim.
Develop a baseline model
Choose a simple reference method and separate training from evaluation data. Compare suitable regression or classification measures and explain why the chosen measure fits the problem.
Evaluate without leakage
Keep information from the evaluation set out of training decisions. Investigate class imbalance, overfitting and errors on different groups instead of reporting only one attractive score.
Communicate a reproducible result
Present the question, preparation steps, evaluation method and limitations together. Keep the code and environment organised so another learner can repeat the analysis.
How this pathway fits your goals
A useful data-science project begins with a question, not a model. The important work includes checking what a dataset represents, finding missing or inconsistent records and selecting a fair way to evaluate an answer. A simple baseline that can be explained is often a better learning starting point than a complicated model whose results are unclear.
Compare this pathway with Power BI if you want to focus on business reporting, or Azure Data Engineering if your interest is dependable data pipelines. Ask how much Python and statistics the chosen batch expects, and whether you will explain model limitations as well as report a score.
Is this the right starting point?
Python, basic statistics and comfort with tables are useful foundations. Share your mathematics and programming background.
Tell the team about your background and the work you want to practise. Ask whether the current Data Science & Machine Learning batch begins with foundations or expects prior knowledge.
Skills to work towards
- Clean and explore a dataset before modelling
- Choose and evaluate a baseline machine-learning model
- Explain findings and limitations using appropriate evidence
A practical brief to explore
Explore a sample business dataset, compare a baseline model with an alternative and report the evaluation method.
Use this example to discuss your goals. Confirm the actual assignments, tools and project scope included in your batch.
What a useful practice review can show
Use these checkpoints to assess an example exercise and identify what to practise next.
- Explain the dataset and target without jargon
- Show a baseline and an evaluation split
- Identify one leakage risk
- Describe a model error and its practical effect
Data Science & ML: a worked study example
Illustrative scenario for discussion and self-study.
A sample dataset contains 100 customers, of whom five cancel. A model predicting that nobody cancels is 95% accurate but finds none of the cancellations. Compare that baseline with a model that identifies some cancellations, and explain the trade-off between missed cases and false alarms.
What to check in your work
- Calculate accuracy and recall from the same test records.
- Keep evaluation records separate from model development.
How this pathway compares
Data analytics focuses on answering questions from data; machine learning adds predictive modelling. Python foundations help with both, while Power BI is a useful comparison when your immediate goal is reporting.
Data Science & Machine Learning: a quick self-check
Why should a model's test records stay separate from feature preparation?
Read the explanation
Learning transformations from test records can leak information into development. Fit preparation steps using training records, then apply the same steps to held-out records. Keep an untouched final evaluation set and explain the metric and baseline you compare against.
A self-study aid for exploring the subject. Confirm the actual syllabus, practical access and class arrangements with the office.
Data Science & Machine Learning questions, answered
Why is a high accuracy score not enough for a data-science project?
Class imbalance can make an unhelpful prediction look accurate. Compare a baseline, choose a metric related to the problem and examine the errors before describing a model as useful.
What should I know before starting Data Science & Machine Learning?
Python, basic statistics and comfort with tables are useful foundations. Share your mathematics and programming background.
How is data science different from data analytics?
Analytics often focuses on understanding and communicating existing data. Data science can also involve statistical modelling and prediction. Both require careful data preparation and a clear business question.
How do I get the Data Science & Machine Learning syllabus and batch details?
Use the course enquiry link to ask for the current syllabus, timings, duration, fees, trainer details and practice access. Confirm classroom or online availability before enrolling.
Where is the classroom training centre?
SV Infotech is at #7-20, 4th Floor, beside Konark Theater, Dilsukhnagar, Hyderabad, Telangana 500060. Ask the team for directions and the learning format available for your selected batch.
Further reading for Data Science & Machine Learning
Explore these official or publisher-maintained resources alongside your learning. Resource links do not imply affiliation, certification or a confirmed course syllabus.
Previously published syllabus and historical references
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SV Infotech Offers Data Science Training in Dilsukhnagar, Hyderabad.
Data Scientist Course training institute in Dilsukhnagar Hyderabad Telangana
Module 1: Introduction to Data Science
1.1 Definition and Scope
Understanding the role of data science in solving real-world problems
Historical context and evolution of data science
1.2 Key Concepts
Data, information, and knowledge
The data science lifecycle
1.3 Data Science Roles and Responsibilities
Data Scientist, Data Engineer, Machine Learning Engineer, etc.
Collaborative aspects and team structures
Module 2: Data Collection and Preprocessing
2.1 Data Collection
Sources of data: structured, unstructured, and semi-structured data
Web scraping,
APIs
, surveys, and more
2.2 Data Cleaning and Preprocessing
Handling missing data
Dealing with outliers
Standardization and normalization
2.3 Exploratory Data Analysis (EDA)
Descriptive statistics
Data visualization using tools like Matplotlib and Seaborn
Module 3: Data Storage and Retrieval
3.1 Databases
SQL and NoSQL databases
Relational databases (e.g., MySQL, PostgreSQL) vs. non-relational databases (e.g., MongoDB)
3.2 Big Data Technologies
Hadoop and MapReduce
Spark for distributed computing
3.3 Data Warehousing
Concepts of data warehousing
Data lakes vs. data warehouses
Module 4: Feature Engineering
4.1 Importance of Feature Engineering
Feature selection vs. feature extraction
Techniques for handling categorical data
4.2 Dimensionality Reduction
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Module 5: Machine Learning Basics
5.1 Introduction to Machine Learning
Supervised vs. unsupervised learning
Classification, regression, clustering
5.2 Model Training and Evaluation
Splitting data into training and testing sets
Cross-validation and model evaluation metrics
5.3 Model Selection
Decision trees, random forests, support vector machines, k-nearest neighbors, etc.
Module 6: Advanced Machine Learning
6.1 Deep Learning
Neural networks and deep neural networks
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
6.2 Model Optimization
Hyperparameter tuning
Regularization techniques
6.3 Model Deployment
Deploying models in production
Model monitoring and maintenance
Module 7: Data Ethics and Privacy
7.1 Ethical Considerations
Bias in data and models
Fairness and accountability
7.2 Privacy Concerns
GDPR and other regulations
Anonymization and encryption techniques
Module 8: Real-world Projects and Case Studies
8.1 Capstone Project
Applying data science concepts to solve a real-world problem
Presenting findings and insights
8.2 Industry Applications
Case studies from various industries (healthcare, finance, marketing, etc.)
Guest lectures from industry professionals
Module 9: Emerging Trends in Data Science
9.1 Artificial Intelligence and Machine Learning
Reinforcement learning
Generative Adversarial Networks (GANs)
9.2 Big Data and Cloud Computing
Cloud platforms (AWS, Azure, Google Cloud)
Serverless computing
Module 10: Continuous Learning and Resources
10.1 Stay Updated
Keeping up with the latest developments
Online courses, blogs, and conferences
10.2 Networking and Community
Joining data science communities
Collaborating with peers and professionals
This course outline provides a structured approach to cover fundamental and advanced concepts in data science. Adjustments can be made based on the audience’s background and the intended duration of the course.
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