Data Science Training

Viswa Online Trainings is one of the world’s leading online IT training providers. We deliver a comprehensive catalog of courses and online training for freshers and working professionals to help them achieve their career goals and experience our best services.

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Learners : 1080

Duration :   Days

About Course

Our Data Science Course can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions. In Data Science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics in them. Thus, Data Science is all about the present and future. Enroll now and get certified in it.

Data Science Training Course Syllabus

Introduction to Machine Learning

✔ What is Machine learning
✔ Who is a Data Scientist
✔ How ML is Different from Programming Languages
✔ Use cases of ML
✔ Techniques used in ML

✔ Data Collection
✔ Data Cleansing
✔ Data Visualization
✔ Web Scrapping

Introduction to Python (Data Science)

✔ Why Python for Machine Learning
✔ Installing Python
✔ Python IDE’s
✔ Writing the First Python Programme
✔ Executing of Python programs

Data Types in Python Data Science

✔ Integer
✔ Float
✔ String
✔ Bool

Operators in Python

✔ Relational Operators
✔ Arithmetic Operators
✔ Logical Operators
✔ Concatenation Operator

Python Objects

✔ Lists
✔ Tuples
✔ Sets
✔ Dictionaries
✔ Range

Control Flow and Loops

✔ If and else if statements
✔ For and while Statements
✔ Iterables and Iterators
✔ List Comprehensions and Generators
✔ Case Study

Functions in Pythons

✔ Writing user-defined functions
✔ Lambda Functions
✔ Using *args and *kwargs

Error handling in Python (Data Science)

✔ Handling Error in Python
✔ Types of Errors
✔ Using try and except
✔ Raising an Error

Python Modules (Data Science)

✔ Usage of Modules
✔ Datetime
✔ Re
✔ OS
✔ Numpy
✔ Pandas
✔ Matplotlib
✔ Seaborn
✔ scikit-learn
✔ sklearn and many mores

Working with Files

✔ File Input/output
✔ Opening of Files
✔ Reading Information from Files
✔ Writing to a File

Importing Data in Python

✔ CSV Files
✔ Excel Sheets
✔ SAS Files
✔ From Web
✔ Working with Relational Databases
✔ Using API’s

Cleaning Data in Python

✔ Exploring the data
✔ Tidying Data for Analysis
✔ Combining Data
✔ Cleaning data


✔ Working with Numpy Arrays
✔ Creating and Manipulating Numpy Arrays
✔ Mathematical and Statistical Functions
✔ Subsetting Numpy Arrays
✔ Concatenating Numpy Arrays


✔ Pandas Foundations
✔ Data Ingestion and Inspection
✔ Exploratory Data Analysis
✔ Time Series in Pandas

Manipulating Data Frames with Pandas

✔ Extracting and Transforming Data
✔ Advanced Indexing
✔ Re-arranging and Re-shaping of data
✔ Grouping Data

Merging Data Frames with Pandas

✔ Preparing Data
✔ Concatenating Data
✔ Merging Data

Introduction to Data Visualization (Data Science)

✔ Customizing plots
✔ Plotting 2D arrays
✔ Statistical Plotting using Seaborn
✔ Analyzing Time series and Images

Data Visualization using Bokeh

✔ Basic Plotting with Bokeh
✔ Layouts, Interactions, and Annotations
✔ Building Interactive Apps with Bokeh

Regular Expressions in Python

✔ Basic concepts of String Manipulations
✔ Formatting Strings
✔ Pattern Matching
✔ Advance Regular Expressions

Machine Learning Algorithms (Data Science)

✔ Supervised Learning

✔ Classification
✔ Regression

✔ Un-Supervised Learning

✔ Association Rules
✔ Clustering

✔ Time Series Analysis

Classification Using Nearest Neighbours

✔ Understanding classification using Nearest Neighbours
✔ The KNN algorithm
✔ Different Distance Metrics
✔ Choosing an appropriate k
✔ Preparing data for use with KNN
✔ Strengths And Weaknesses of KNN
✔ Collecting data
✔ Exploring and preparing the data

✔ normalizing numeric data
✔ creating training and test datasets

✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ z-score standardization
✔ Testing alternative values of k
✔ Why KNN is Lazy Learner?

Classification Using Naive Bayes

✔ Understanding naive Bayes

✔ Basic concepts of Bayesian methods
✔ Probability
✔ Joint probability
✔ Conditional probability with Bayes theorem

✔ The Naive Bayes algorithm

✔ The naive Bayes classification
✔ The Laplace estimator
✔ Using numeric features with naive Bayes
✔ Strengths and Weakness

✔ Spam Filtering Case Study

✔ Collecting data
✔ Exploring and preparing the data
✔ processing text data for analysis
✔ creating training and test datasets
✔ Visualizing text data-word clouds
✔ creating indicator features for frequent words

✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance

Classification Using Decision Trees

✔ Understanding decision trees
✔ Divide conquer
✔ The Decision tree algorithm

✔ Choosing the best split
✔ Pruning the decision tree

✔ Entropy
✔ Information Gain.
✔ Strengths And Weaknesses
✔ Identifying risky bank loans using Decision trees

✔ Collect data
✔ Exploring and preparing the data
✔ Data preparation-creating random training and test datasets
✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ Boosting the accuracy of decision trees
✔ Making some mistakes more costly than others

Forecasting Numeric Data –Regression

✔ Understanding Regression
✔ Simple linear Regression
✔ Least-squares Estimation

✔ Correlations
✔ Multiple linear regression
✔ Predicting medical expenses using Linear Regression
✔ Collecting data
✔ Exploring and preparing data
✔ Exploring relationships among features- the correlation matrix
✔ Visualizing relationships among features –the scatterplot matrix
✔ Training a model on the data
✔ Evaluating model performance

Logistic Regression

✔ ODD’s Ratio
✔ Applying Logistic Regression
✔ Training a model on the data
✔ Evaluating the Model Performance
✔ Improving of Model Performance
✔ Other Types of Regressions

✔ Polynomial Regression
✔ Ridge Regression
✔ Lasso Regression
✔ Quantile Regression

Market Basket Analysis using Association Rules

✔ Understanding association rules
✔ The Apriori algorithm for association rule learning
✔ Measuring rule interest –support and confidence
✔ Building a set of rules with the Apriori
✔ Identifying frequently purchased groceries with association rules
✔ creating a sparse matrix for transaction data
✔ Visualizing item support –item frequency plots
✔ Visualizing transaction data- plotting the sparse matrix
✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ Sorting the set of association rules
✔ Taking subsets of association rules
✔ Saving association rules to a file or data frame

Finding Groups of Data- Clustering with K- Means

✔ Understanding clustering
✔ Clustering as a machine learning task
✔ The K-means algorithm for clustering
✔ Using distance to assign and update cluster
✔ Choosing the appropriate number of cluster
✔ Finding Teen market segments using K-means clustering
✔ Collecting data
✔ Exploring and preparing the data
✔ Dummy coding missing values
✔ Imputing missing values
✔ Training a model on the data

Evaluating model performance

✔ Improving model performance
✔ Evaluating Model Performance
✔ Working with classification prediction data
✔ Using confusion matrices to measure performance
✔ Beyond accuracy – another measure of performance
✔ The kappa statistic
✔ Sensitivity and specificity
✔ Precision and recall
✔ The F – measure
✔ Visualizing performance ROC curves
✔ Precision and Recall Curves
✔ Estimating future performance

✔ The holdout method
✔ Cross-validation
✔ Bootstrap sampling

Improving Model Performance

✔ Tuning models for better performance
✔ Hyper Parameters
✔ Creating a simple tuned model
✔ Customizing the tuning process
✔ Improving model performance with meta-learning

✔ Understanding ensembles
✔ Bagging
✔ Boosting
✔ Random forests
✔ Training random forests
✔ Evaluating random forest performance

Sentimental Analysis

✔ Extracting the Data from Twitter/Facebook using API
✔ Cleaning the Data
✔ Performing Text Analysis
✔ Performing Sentimental Analysis

Real Time Scenarios

✔ Deployment of Models
✔ Practical Issues
✔ Project Implementation
✔ Case Studies
✔ Cloud Based APIs

Live Instructor Based Training With Software
Lifetime access and 24×7 support
Certification Oriented content
Hands-On complete Real-time training
Get a certificate on course completion
Flexible Schedules
Live Recorded Videos Access
Study Material Provided

Data Science Training - Upcoming Batches

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Live Virtual Training

  • Schedule your sessions at your comfortable timings.
  • Instructor-led training, Real-time projects
  • Certification Guidance.

Self-Paced Learning

  • Complete set of live-online training sessions 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
For Business

Data Science Training FAQ'S

Supervised Learning vs. Unsupervised Learning: What’s the Difference?

Supervised and unsupervised learning systems differ in the nature of the training data that they’re given. Supervised learning requires labeled training data, whereas, in unsupervised learning, the system is provided with unlabeled data and discovers the trends that are present.

What Is Logistic Regression?

Logistic regression is a form of predictive analysis. It is used to find the relationships that exist between a dependent binary variable and one or more independent variables by employing a logistic regression equation.

Get ahead in your career by learning Data Science through VISWA Online Trainings

What Is a Decision Tree?

Decision trees are a tool used to classify data and determine the possibility of defined outcomes in a system. The base of the tree is known as the root node. The root node branches out into decision nodes based on the various decisions that can be made at each stage. Decision nodes flow into lead nodes, which represent the consequence of each decision.

What Is Pruning in a Decision Tree Algorithm?

Pruning a decision tree is the process of eliminating non-critical subtrees so that the data under consideration is not overfitted. In pre-pruning, the tree is pruned as it is being constructed, following criteria like the Gini index or information gain metrics. Post-pruning entails pruning a tree from the bottom up after it has been constructed.

What Is Entropy in a Decision Tree Algorithm?

Entropy is a measure of the level of uncertainty or impurity that’s present in a dataset. For a dataset with N classes, the entropy is described by the following formula. 


vishal meda
vishal meda
They give trainings properly and trainers are well versed with them where i recommend to all viswa trainings are good!!
Ntr fan
Ntr fan
I just finished sap bods training in Hyderabad. Excellent course and curriculum 100% doubt clarification sessions. Thanks Chaitanya
Shiva Krishna
Shiva Krishna
I recently completed informatica online training with Chaitanya. Course was built by excellent trainer. And process of learning was streamlined. Thanks
Mohammad ali syed
Mohammad ali syed
It was great and smooth understandable training. You can learn lots.
Govinda Bhatia
Govinda Bhatia
Not recommended as there will be no server access working to do practical after training. Also there will be no fix for the same. So it's wastage of money. If server access not at all working then no meaning to provide server access. Also it not working for single day properly. Need to followup daily but in response you told will fix that sir at home once he will back will fix. After he came back again it's not working and not able to fix for single day also Every time new excuse it's wastage of money.
M Leela mohan
M Leela mohan
I took SQL Server and MSBI Online training with Murali Krishna. I must say the course content was highly qualitative and the trainer covered all concepts. Overall it was a good experience with VISWA Online Trainings.
Attended live Virtual training for IoT Trainer was very good. He had excellent knowledge of IoT and was very good at explaining concepts in detail.…
Lakshmi Lakshmi
Lakshmi Lakshmi
Best sap commerce cloud and Spartacus training institute in india. He provides a great mix of listening, speaking, and practical learning activities and a very safe, supportive learning environment. He maintains a friendly relationship with the students during class. He not only teaches but also monitors our practice status on daily basis.
Ch Chandranath
Ch Chandranath
I have undergone Oracle Tuning training. I can proudly say that this is one of the best training institutes available in the market. The way Mr. Kumar teaches the concepts and makes them understandable is very commendable and unique. Even a novice can clearly understand the concepts clearly after attending his classes.

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