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Data Science Certification Training
One of the top providers of online IT training worldwide is VISWA Online Trainings. To assist beginners and working professionals in achieving their career objectives and taking advantage of our best services, We provide a wide range of courses and online training.
About Course
🧠 Course Overview
The Data Science Online Training Course is designed to help learners master the essential skills required to analyze data, build predictive models, and drive data-driven decisions. This course covers the complete data science lifecycle — from data collection and cleaning to visualization, machine learning, and deployment.
Learners will gain expertise in tools such as Python, R, SQL, TensorFlow, Power BI, and Tableau, and understand the application of statistical analysis, machine learning, and data visualization techniques to solve real-world business problems.
Whether you are a beginner or a professional aiming to upskill, this course provides a hands-on learning experience with real-time projects, industry case studies, and certification support to prepare you for roles like Data Scientist, Data Analyst, or Machine Learning Engineer.
🌟 Key Features
✔️ Comprehensive Curriculum: Covers Python, statistics, data wrangling, visualization, machine learning, and AI basics.
✔️ Hands-On Projects: Work on real-world datasets and industry use cases.
✔️ Expert-Led Sessions: Learn from experienced data scientists and industry mentors.
✔️ Machine Learning Algorithms: Master regression, classification, clustering, and deep learning techniques.
✔️ Visualization Tools: Learn Power BI, Tableau, and Matplotlib for creating dashboards.
✔️ Data Handling Skills: Gain expertise in SQL, Pandas, and NumPy.
✔️ Capstone Project: Apply end-to-end data science workflow in a final project.
✔️ Flexible Learning: Online live sessions with lifetime access to recorded content.
🎯 Course Outcomes
After completing this Data Science Online Training, you will be able to:
🔹 Understand the data science process, including data collection, cleaning, and transformation.
🔹 Apply statistical and Data analytics techniques to derive insights from data.
🔹 Build and evaluate machine learning models using Python and R.
🔹 Perform data visualization to communicate findings effectively.
🔹 Work with big data frameworks like Hadoop or Spark (depending on specialization).
🔹 Integrate data science solutions into business and real-world applications.
🔹 Prepare for job roles such as Data Scientist, Data Analyst, or ML Engineer.
Data Science Training Course Syllabus
Introduction to Data Science
- What is Data Science?
- Importance and applications of Data Science
- Data Science vs Business Intelligence vs AI
- Data Science lifecycle
- Roles and responsibilities of a Data Scientist
Python for Data Science
- Introduction to Python programming
- Data types, variables, and operators
- Control structures (if, loops, functions)
- Working with libraries: NumPy, Pandas, Matplotlib, Seaborn
- Data cleaning and manipulation using Pandas
- Data visualization using Matplotlib & Seaborn
- Exploratory Data Analysis (EDA)
Statistics and Probability
- Descriptive statistics: mean, median, mode, variance, standard deviation
- Probability theory and distributions (Normal, Binomial, Poisson)
- Hypothesis testing and confidence intervals
- Correlation and covariance
- ANOVA and Chi-square tests
- Inferential statistics for data analysis
Data Visualization
- Importance of data visualization
- Visualization with Matplotlib, Seaborn, Tableau, and Power BI
- Dashboard creation and storytelling with data
- Visualizing trends, comparisons, and patterns
Machine Learning Fundamentals
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
SQL for Data Science
- Introduction to databases and SQL
- CRUD operations (SELECT, INSERT, UPDATE, DELETE)
- Joins, subqueries, and aggregations
- Data filtering, sorting, and grouping
- Working with large datasets
Deep Learning and Neural Networks
- Introduction to Deep Learning
- Understanding neural networks
- Activation functions, forward & backward propagation
- Using TensorFlow and Keras frameworks
- Building simple ANN, CNN, and RNN models
Big Data & Cloud Integration
- Introduction to Hadoop, Spark, and PySpark
- Working with large datasets in distributed environments
- Introduction to AWS, Azure, or Google Cloud for data science deployment
Data Science Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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CHOOSE YOUR OWN COMFORTABLE LEARNING EXPERIENCE
Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
- Certification Guidance
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Self-Paced Learning
- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
Corporate Training
- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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Data Science Online Training FAQ'S
What is Data Science, and why is it important?
Data Science is the field of study that combines statistics, programming, and domain expertise to extract meaningful insights from data.
It is important because it helps organizations:
- Make data-driven decisions.
- Predict future trends using machine learning models.
- Optimize processes and improve business performance through data insights.
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What is the difference between supervised and unsupervised learning?
- Supervised Learning:
Uses labeled data (input-output pairs).
Example algorithms: Linear Regression, Decision Trees, Random Forest, SVM.
Example use case: Predicting house prices based on features. - Unsupervised Learning:
Uses unlabeled data (no predefined output).
Example algorithms: K-Means Clustering, PCA, Hierarchical Clustering.
Example use case: Customer segmentation.
What is overfitting in machine learning, and how can it be avoided?
Overfitting occurs when a model performs well on training data but poorly on unseen (test) data because it has learned noise instead of patterns.
To avoid overfitting:
- Use Cross-validation.
- Apply Regularization techniques (L1, L2).
- Use Dropout in neural networks.
- Simplify the model or gather more data.
What is the difference between classification and regression?
- Classification: Predicts categorical outcomes (discrete values).
Example: Spam or Not Spam, Disease or No Disease.
Algorithms: Logistic Regression, Decision Trees, Random Forest. - Regression: Predicts continuous outcomes (numeric values).
Example: Predicting sales amount or temperature.
Algorithms: Linear Regression, Lasso, Ridge Regression.
What is the role of feature selection in data science?
Feature selection is the process of choosing the most relevant variables (features) for building a model.
It improves model accuracy and reduces complexity.
Common methods:
- Filter methods: Correlation, Chi-square test.
- Wrapper methods: Recursive Feature Elimination (RFE).
- Embedded methods: Lasso Regression, Decision Tree importance.
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