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.

4627 Reviews 4.9
4.7/5

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

✔ 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

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

✔ Integer
✔ Float
✔ String
✔ Bool

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

✔ Lists
✔ Tuples
✔ Sets
✔ Dictionaries
✔ Range

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

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

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

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

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

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

✔ 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

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

✔ Preparing Data
✔ Concatenating Data
✔ Merging Data

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

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

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

✔ Supervised Learning

✔ Classification
✔ Regression

✔ Un-Supervised Learning

✔ Association Rules
✔ Clustering

✔ Time Series Analysis

✔ 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?

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

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

✔ 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

7th NOV 2022

8 AM IST

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5th NOV 2022

8 AM IST

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Live Virtual Training

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  • Instructor-led training, Real-time projects
  • Certification Guidance.
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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
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Data Science Training FAQ'S

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.

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

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.

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.

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. 

Reviews

SHAIK SAJID
SHAIK SAJID
2022-11-23
Excellent institute for Online WebMethods Course and I recommend VISWA Technologies thank you For arranging such a great faculty
Madhu
Madhu
2022-11-23
Today is the last class for online RPA course i am thankful to Chaitanya sir for such a great training and I thank to admin team
Bodicherla Jagadeeswar
Bodicherla Jagadeeswar
2022-11-23
Recently completed online Microsoft Intune training course By "Rajendra Prasad" and I recommend VISWA Online Trainings for this course and also thank you admin team
Penchalakumar Ganji
Penchalakumar Ganji
2022-11-23
Best institute for Linux, AWS online course by Musab sir. I thank Chaitanya sir for arranging our classes
Konduru Akhil
Konduru Akhil
2022-11-23
Hi, Overall training classes are very good.All the instructions are kind and patient.I look forward to take more classes from Viswa online trainings.
Eswar Tech
Eswar Tech
2022-11-18
I enroll for aws solution architecture course. It was great learning with viswa technologies. Thanks team.
Manikanta Goud
Manikanta Goud
2022-11-18
I am really satisfied. Best training institute. Thanks Chaitu
S Rambabu 6228
S Rambabu 6228
2022-11-18
Best online training institute. I have more experience with java srinath trainer.
gaddam Manojkumar
gaddam Manojkumar
2022-11-12
They have a professional approach by their trainer and support team. Personally, I liked the instructor's way of teaching and also helping us if we have any doubts after the class.
Ramesh Metta
Ramesh Metta
2022-11-11
They have teachers with strong expertise. The AWS course i have taken was good and had full support from team.

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