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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.

Reviews 4.9 (4.6k+)

Rated 4.7 out of 5

Learners : 1080

Duration : 25 Days

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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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.

Reviews

    
 
 
         
   Chandu Gutlapalli 
 
 
 
 Trustindex verifies that the original source of the review is Google. I have attended the Big data Hadoop online course In VISWA Technologies. Course content is excellent. I would recommend this course anyone wants to make the career in the analyst. instructor (Rajesh sir) describes you course in detail. All Presentations are very good. The Course Material is truly great, and Good thing is that you will get Project Explanation with Case Studies.
  
 
        
   Pudi Nithish 
 
 
 
 Trustindex verifies that the original source of the review is Google. I had a great experience with Jaya Vardhan. Very good faculty the teaching style is clear and practical and the course material is well structured and up to date. Very good lab faculty is too supportive. Overall it's very good place learn Datadog from expert trainer.
  
 
        
   Rahul Dharmapuri 
 
 
 
 Trustindex verifies that the original source of the review is Google. Good Institute
  
 
        
   Sai kiran 
 
 
 
 Trustindex verifies that the original source of the review is Google. Great institute, chaithanya sir is friendly, highly recommend for both beginner and working professionals
  
 
        
   Tharak 
 
 
 
 Trustindex verifies that the original source of the review is Google. Excellent Experience. I would Recommend others to Join this course Oracle OTM & GTM.
  
 
        
   S I V A ROCK 
 
 
 
 Trustindex verifies that the original source of the review is Google. I recently completed the Oracle 19c DBA course at VISWA Online Trainings, and it was an excellent learning experience. The trainer explained every topic clearly with real-time examples and practical sessions. The hands-on labs helped me gain confidence in database administration. I highly recommend this course to anyone who wants to build a career as an Oracle DBA.
  
 
        
   Nikil 
 
 
 
 Trustindex verifies that the original source of the review is Google. The course was very well structured. I have taken Weblogic Admin Training. My learning journey was excellent at VISWA Online Trainings!
  
 
        
   Monica Benny 
 
 
 
 Trustindex verifies that the original source of the review is Google. I recently completed the Oracle Analytics Cloud (OAC) and Fusion Data Intelligence (FDI) training program with Viswa Training, and it has been an exceptional learning experience from start to finish.

The entire journey was well-supported. Chaithanya was extremely responsive, ensuring seamless coordination and assistance throughout the learning process.

The instructor brought a high level of expertise and depth to the classroom. Their extensive knowledge of the subject matter made complex analytical concepts easy to grasp and highly applicable to real-world scenarios.

Thanks to this structured curriculum and expert instruction, I now have a robust, comprehensive understanding of Oracle Analytics Cloud technology. Highly recommended for anyone looking to upskill in enterprise analytics!

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