Data Science Certification Training

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About Course

Dell Boomi understands the need for a quality training curriculum along with real-time implementation exposure as it forms the very essence of your future career in Boomi Certification. Our well-structured online training course in Dell Boomi Training extensively covers all the core aspects of classes with an emphasis on live scenarios. Access to expert trainers and instructor-led training sessions ensures that you can easily clear your doubts and get the exact guidance that is expected from Dell Boomi Online Training sessions.

Upcoming Live Online Classes

 
10-SEP-2022 TO 05-DEC-2022

 08:00 AM

 
10-SEP-2022 TO 05-DEC-2022

 08:00 AM

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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 more
  • 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
  • Case study
  • 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
  • Case Study
  • Extracting and Transforming Data
  • Advanced Indexing
  • Re-arranging and Re-shaping of data
  • Grouping Data
  • Case Study
  • Preparing Data
  • Concatenating Data
  • Merging Data
  • Case Study
  • 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
  • Case Study
  • 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?
  • Case Study
  • 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
  • Case Study
  • Understanding decision trees
  • Divide conquer
  • The Decision tree algorithm
    • Choosing the best split
    • Pruning the decision tree
  • Entropy
  • Information Gain.
  • Strengths And Weakness
  • 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
  • Case Study
  • 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

Course Details

  • Live Instructor Based Training
  • Real-Time Oracle Certified Trainer
  • 1 Yr. Membership to Multiple Batches
  • Live Recorded Videos Access
  • Latest Instance Access for 5 Months
  • Study Material Provided
  • Sample Resumes & Preparation
  • Interview Questions with Ans

CHOOSE YOUR OWN COMFORTABLE LEARNING EXPERIENCE

Corporate Training

  • Learn As A Full Day Schedule With Discussions, Exercises,
  • Practical Use Cases
  • Design Your Own Syllabus Based
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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.
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Live Virtual Training

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

Dami
Dami@dami
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The trainer who taught me was very good. The total journey went great. He used to teach me from the beginner level to the advanced…
Surekha
Surekha@Surekha
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The training experience and throughout the duration of course was excellent.I took SAP ABAP couse here which has covered all the topics from beginner to…
Vikram Kumar
Vikram Kumar@Vikram_Kumar
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Best training in Java Script and Angular JS. Recording classes are very good. Focused on every concept with practical approach. This institute gives best training…
Yaswanth
Yaswanth@Yaswanth
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Trainer was good in understanding the requirements and trained the topics with relevant examples for a better understanding. The training was interactive and content was…
Abey
Abey@Abey
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Nice institute with very good facilities for trainer and trainee. Highly recommend it for SAP S4 Hana Basis training. Rajendra Prasad is very helpful and…
Abdhul Mohammad
Abdhul Mohammad@Abdhul_Mohammad
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I had a good experience with VISWA Technologies My trainer was Pradeep sir, he is a good trainer. I completed python course successfully with deep…
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