Data Science Certification Training
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Learners : 1080
Duration : 3 Months
About Course
Our Data Science Course may be characterized as a fusion of mathematics, business acumen, tools, algorithms, and machine learning approaches, all of which assist us in identifying hidden patterns or insights from unstructured data that can be crucial in the development of important business decisions. Data science makes use of both organized and unstructured data. The algorithms also use predictive analytics. Data science is therefore concerned with the present and the future. Enrol today to earn your certification.
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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Instructor-led training, Real-time projects
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Certification Guidance.
Self-Paced Learning
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Complete set of live-online training sessions recorded videos.
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Corporate Training
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Learn As A Full Day Schedule With Discussions, Exercises,
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Design Your Own Syllabus Based
Data Science Training FAQ'S
The type of training data that supervised and unsupervised learning systems receive varies. In contrast to unsupervised learning, which uses unlabeled data to help the algorithm identify trends, supervised learning requires labelled training data.
Predictive analysis is a type of logistic regression. By using a logistic regression equation, it is possible to determine the associations between a dependent binary variable and one or more independent variables.
Get ahead in your career by learning Data Science through VISWA Online Trainings
Decision trees are a method for categorising data and figuring out whether certain outcomes are possible in a system. The root node is the term for the tree’s base. Based on the many choices available at each level, the root node divides into decision nodes. Lead nodes, which indicate each choice’s outcome, are formed by the flow of decision nodes.
Pruning a decision tree is the process of removing non-critical subtrees to prevent overfitting of the data under consideration. Pre-pruning involves trimming the tree as it grows, using measurements like the Gini index or information gain metrics. After a tree has been built, it must be pruned from the ground up, which is known as post-pruning.
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.