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Tools Covered in Data Science Online Training

Sql Tableau VBA Python Excel Tensorflow

Data Science Online Training Objectives

  • To provide a learning environment that fosters scientific excellence and promote lifelong learning with understanding of professional responsibilities and obligations to clients and public.
  • Students themselves can formulate simple algorithms to solve problems, and can code them in a high-level language appropriate for data science work (e.g., Python, SQL, R, Java).
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports.
  • Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
  • Ability to develop software solutions for the requirements, based on critical analysis and research.
  • Ability to use SQL and Tableau Tool for Data Visualization and Optimization.
  • Learn Techniques and Tools for Transformation of Data.

Prerequisites to learn Data Science Online Training

  • Statistics
  • Maths (Calculus and Linear Algebra)
  • Programming (R/Python/SQL)
  • BI & ETL Tools

Top Skills Required To Become a Data Scientist in 2021

  • Python
  • SQL
  • Scala
  • Java
  • R Programming
  • MATLAB
  • MongoDB
  • Oracle
  • Microsoft Azure
  • Cloudera
  • Tableau
  • Power BI
  • SAS
  • D3.js
  • Python
  • Java
  • R libraries
  • Natural Language Processing
  • Classification
  • Clustering
  • Ensemble methods
  • Deep Learning
  • PyTorch
  • TensorFlow
  • Scikit Learn
  • Theano
  • Auto ML

Best Data Science Online Training Institute in Chennai & Bangalore,
100% Job Oriented Course

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About Data Science Online Training

Data Science Online Training will help you to become an expert in Probability and Statistics, Excel, SQL, Tableau, R and Python with 10+ Real Life Projects. This Data Science Course will give you expertise on Machine Learning and Big data analysis for Solving Complex Challenges with Hands-on Classes. Gain the Critical Skills Such as Data Visualization, Linear Algebra, Multivariable Calculus, Python libraries and more from this Online Data Science Training Courses. Develop Your Mathematical & Programming Skills to become Professional Certified Data Scientist with our Data Science Certification Courses.

Besant Technologies Data Science Online Training will make you an expert in Data Science & AI Skills Including:-

  • Programming
  • Machine Learning Techniques
  • Data Visualization and Reporting
  • Risk Analysis
  • Statistical analysis and Math
  • Data Mining
  • Cleaning and Munging
  • Big Data Platforms
  • Cloud Tools to Become Data scientist

Enroll in this Data Science online Training to jump start your career in Fortune 500 company, Such as Amazon, Microsoft, IBM, Google. This Data Science Certification course will Focued on your Carrer Growth delivered by Industry Experts from the Basic to Advanced Level.You will Start learning Data Scinece & AI Concepts with Demo Projects experience for better understanding.

The Data Science Online Training is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. Professionals who can benefit from this course include:

  • A career as a data analyst will suit you if you are highly analytical, have strong mathematical skills and are curious and inquisitive.
  • Managers from any field, as Analytics is the best tool for managers these days who love solving challenging programs.
  • Business Analysts and Data Analysts who wish to upscale their Data Analytics skills who love working with data and writing programs to analyze the data.
  • Database professionals who aspire to venture into the field of Big Data by acquiring analytics skills.
  • Fresh graduates who wish to make a career in the field of Big Data or Data Science.

Besant Technologies Provides tailor-made Data Science Online Training for working Professionals and Freshers.

  • Besant Technologies offers 1-on-1 live student-teacher sessions.
  • The technology used by Besant is called Whiteboard Audio Video Education or WAVE in short.
  • The platform enables a primary teacher, multiple teacher assistants, and many students to be live at the same time. We also hosts quizzes, but it is entirely on a live model.

Practical work is an essential component of science teaching and learning, both for the aim of developing student's scientific knowledge and that of developing student's knowledge about science.

Data Science Online Training Key Features

  • 100+ hours of Practical Learning.
  • Six industry projects with Multiple case studies.
  • Lifetime access to self-paced learning and class recordings.
  • Hands-on practice with Python, R, Machine Learning & AI.
  • 100% Placement Oriented Classes.
  • Course Delivered by Certified Data Scientist Professionals.
  • Unlimited Interviews & Internal Job Portal.

Data Science Online Training Options

Group Training

Group Training

If you have Three or more people in your training we will be delighted to offer you a group discount.

One-One Training

One-One Training

Customized and exclusive training based on your requirement.

Online Training

Online Training

Online Course will make you become a certified Expert in Just One Month.

Preferred
Team/Corporate Training

Team/Corporate Training

If you want to give the Trending technology experience to your esteemed employees, we are here to help you!

Why Besant Technologies?

When believing young talents make revolutions and our story started in late 2005. We(Besant Technologies) are focusing exclusively in the area of Software Training and Placement Division in IT Sector. Each year there might be around 550,000 engineering graduates are out and looking for career in different fields and most of them are looking for an Information Technology Sector for their career.

24x7 Servicesand Support

100k StudentsEnrolled

100% CourseCompletion rate

20+ Branchesin India

Unlimited InterviewSupport

Low Cost Comparedwith others

Certified IndustryExperts

Online & ClassroomTrainings

Tie-up with5k Companies

13+ Years ofIndustry Experts

Internal JobPortal Access

100% Placement Certification* Support

Data Science Online Training Schedule

  • 01-11-2021 Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 28-10-2021 Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 30-10-2021Sat (Sat - Sun)Weekend Batch11:00 AM (IST) (Class 3Hrs) / Per SessionGet Fees
Can’t find a batch you were looking for? Request a Batch

Data Science Online Training Course Content

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & its types
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  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview

Hands-on-Exercise:

  • Installing Python idle for windows,Linux and
  • Creating “Hello World” code
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  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels

Hands-on-Exercise-Constructing Operators

  • Practice and Quickly learn Python necessary skills by solving simple questions and problems.
  • how Python uses indentation to structure a program, and how to avoid some common indentation errors.
  • You executed to make simple numerical lists, as well as a few operations you can perform on numerical lists, tuple, dictionary and set
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  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library

Hands-on-Exercise:

  • Installing jupyter notebook for windows, Linux and
  • Installing numpy, pandas and matplotlib
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  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to CSV file

Hands-on-Exercise:

  • To generate data sets and create visualizations of that data. You learned to create simple plots with matplotlib, and you saw how to use a scatter plot to explore random
  • You learned to create a histogram with Pygal and how to use a histogram to explore the results of rolling dice of different
  • Generating your own data sets with code is an interesting and powerful way to model and explore a wide variety of real-world
  • As you continue to work through the data visualization projects that follow, keep an eye out for situations you might be able to model with
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  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

Hands-on-Exercise:

  • As you gain experience with CSV and JSON files, you’ll be able to process almost any data you want to analyze.
  • Most online data sets can be downloaded in either or both of these From working with these formats, you’ll be able to learn other data formats as well.
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  • Central Tendency
    • Mean
    • Median
    • Mode
    • Skewness
    • Normal Distribution
  • Probability Basics
    • What does it mean by probability?
    • Types of Probability
    • ODDS Ratio?
  • Standard Deviation
    • Data deviation & distribution
    • Variance
  • Bias variance Tradeoff
    • Underfitting
    • Overfitting
  • Distance metrics
    • Euclidean Distance
    • Manhattan Distance
  • Outlier analysis
    • What is an Outlier?
    • Inter Quartile Range
    • Box & whisker plot
    • Upper Whisker
    • Lower Whisker
    • Scatter plot
    • Cook’s Distance
  • Missing Value treatment
    • What is NA?
    • Central Imputation
    • KNN imputation
    • Dummification
  • Correlation
    • Pearson correlation
    • positive & Negative correlation

Hands-on-Exercise:

  • Compute probability in a situation where there are equally-likely outcomes
  • Apply concepts to cards and dice
  • Compute the probability of two independent events both occurring
  • Compute the probability of either of two independent events occurring
  • Do problems that involve conditional probabilities
  • Calculate the probability of two independent events occurring
  • List all permutations and combinations
  • Apply formulas for permutations and combinations
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  • Classification
    • Confusion Matrix
    • Precision
    • Recall
    • Specificity
    • F1 Score
  • Regression
    • MSE
    • RMSE
    • MAPE

Hands-on-Exercise:

  • State why the z’ transformation is necessary
  • Compute the standard error of z
  • Compute a confidence interval on ρ The computation of a confidence interval
  • Estimate the population proportion from sample proportions
  • Apply the correction for continuity
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  • Linear Regression
    • Linear Equation
    • Slope
    • Intercept
    • R square value
  • Logistic regression
    • ODDS ratio
    • Probability of success
    • Probability of failure Bias Variance Tradeoff
    • ROC curve
    • Bias Variance Tradeoff

Hands-on-Exercise:

  • we've reviewed the main ways to approach the problem of modeling data using simple and definite
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  • K-Means
  • K-Means ++
  • Hierarchical Clustering
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  • Support Vectors
  • Hyperplanes
  • 2-D Case
  • Linear Hyperplane
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  • Linear
  • Radial
  • polynomial
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  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest

Hands-on-Exercise:

  • We have covered the simplest but still very practical machine learning models in an eminently practical way to get us started on the complexity
  • where we will cover several regression techniques, it will be time to go and solve a new type of problem that we have not worked on, even if it's possible to solve the problem with clustering methods (regression), using new mathematical tools for approximating unknown values.
  • In it, we will model past data using mathematical functions, and try to model new output based on those modeling
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  • Perceptron
  • Multi-Layer perceptron
  • Markov Decision Process
  • Logical Agent & First Order Logic
  • AL Applications
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  • CNN – Convolutional Neural Network
  • RNN – Recurrent Neural Network
  • ANN – Artificial Neural Network

Hands-on-Exercise:

  • We took a very important step towards solving complex problems together by means of implementing our first neural
  • Now, the following architectures will have familiar elements, and we will be able to extrapolate the knowledge acquired on this chapter, to novel
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  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization
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  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s
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  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching

Hands-on-Exercise:

  • provided, you will even be able to create new customized
  • As our models won't be enough to solve very complex problems, in the following chapter, our scope will expand even more, adding the important dimension of time to the set of elements included in our generalization.
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  • Start Page
  • Show Me
  • Connecting to Excel Files
  • Connecting to Text Files
  • Connect to Microsoft SQL Server
  • Connecting to Microsoft Analysis Services
  • Creating and Removing Hierarchies
  • Bins
  • Joining Tables
  • Data Blending
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  • arameters
  • Grouping Example 1
  • Grouping Example 2
  • Edit Groups
  • Set
  • Combined Sets
  • Creating a First Report
  • Data Labels
  • Create Folders
  • Sorting Data
  • Add Totals, Subtotals and Grand Totals to Report

Hands-on-Exercise:

  • Install Tableau Desktop
  • Connect Tableau to various Datasets: Excel and CSV files
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  • Area Chart
  • Bar Chart
  • Box Plot
  • Bubble Chart
  • Bump Chart
  • Bullet Graph
  • Circle Views
  • Dual Combination Chart
  • Dual Lines Chart
  • Funnel Chart
  • Traditional Funnel Charts
  • Gantt Chart
  • Grouped Bar or Side by Side Bars Chart
  • Heatmap
  • Highlight Table
  • Histogram
  • Cumulative Histogram
  • Line Chart
  • Lollipop Chart
  • Pareto Chart
  • Pie Chart
  • Scatter Plot
  • Stacked Bar Chart
  • Text Label
  • Tree Map
  • Word Cloud
  • Waterfall Chart

Hands-on-Exercise:

  • Create and use Static Sets
  • Create and use Dynamic Sets
  • Combine Sets into more Sets
  • Use Sets as filters
  • Create Sets via Formulas
  • Control Sets with Parameters
  • Control Reference Lines with Parameters
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  • Dual Axis Reports
  • Blended Axis
  • Individual Axis
  • Add Reference Lines
  • Reference Bands
  • Reference Distributions
  • Basic Maps
  • Symbol Map
  • Use Google Maps
  • Mapbox Maps as a Background Map
  • WMS Server Map as a Background Map

Hands-on-Exercise:

  • Create Barcharts
  • Create Area Charts
  • Create Maps
  • Create Interactive Dashboards
  • Create Storylines
  • Understand Types of Joins and how they work
  • Work with Data Blending in Tableau
  • Create Table Calculations
  • Work with Parameters
  • Create Dual Axis Charts
  • Create Calculated Fields
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  • Calculated Fields
  • Basic Approach to Calculate Rank
  • Advanced Approach to Calculate Ra
  • Calculating Running Total
  • Filters Introduction
  • Quick Filters
  • Filters on Dimensions
  • Conditional Filters
  • Top and Bottom Filters
  • Filters on Measures
  • Context Filters
  • Slicing Fliters
  • Data Source Filters
  • Extract Filters

Hands-on-Exercise:

  • Creating Data Extracts in Tableau
  • Understand Aggregation, Granularity, and Level of Detail
  • Adding Filters and Quick Filters
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  • Create a Dashboard
  • Format Dashboard Layout
  • Create a Device Preview of a Dashboard
  • Create Filters on Dashboard
  • Dashboard Objects
  • Create a Story
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  • Tableau online.
  • Overview of Tableau
  • Publishing Tableau objects and scheduling/subscription.

Hands-on-Exercise:

  • Create Data Hierarchies
  • Adding Actions to Dashboards (filters & highlighting)
  • Assigning Geographical Roles to Data Elements
  • Advanced Data Preparation
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  • List the features of Oracle Database 11g
  • Discuss the basic design, theoretical, and physical aspects of a relational database
  • Categorize the different types of SQL statements
  • Describe the data set used by the course
  • Log on to the database using SQL Developer environment
  • Save queries to files and use script files in SQL Developer

Hands-on-Exercise:

  • Prepare your environment
  • Work with Oracle database tools
  • Understand and work with language features
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  • List the capabilities of SQL SELECT statements
  • Generate a report of data from the output of a basic SELECT statement
  • Select All Columns
  • Select Specific Columns
  • Use Column Heading Defaults
  • Use Arithmetic Operators
  • Understand Operator Precedence
  • Learn the DESCRIBE command to display the table structure

Hands-on-Exercise

  • Individual statements in SQL scripts are commonly terminated by a line break (or carriage return) and a forward slash on the next line, instead of a semicolon.
  • You can create a SELECT statement, terminate it with a line break, include a forward slash to execute the statement, and save it in a script file.
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  • Write queries that contain a WHERE clause to limit the output retrieved
  • List the comparison operators and logical operators that are used in a WHERE clause
  • Describe the rules of precedence for comparison and logical operators
  • Use character string literals in the WHERE clause
  • Write queries that contain an ORDER BY clause to sort the output of a SELECT statement
  • Sort output in descending and ascending order

Hands-on-Exercise:

  • Creating the queries in a compound query must return the same number of columns.
  • Create corresponding columns in each query must be of compatible data types.
  • ORDER BY; it is, however, permissible to place a single ORDER BY clause at the end of the compound query
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  • Describe the differences between single row and multiple row functions
  • Manipulate strings with character function in the SELECT and WHERE clauses
  • Manipulate numbers with the ROUND, TRUNC, and MOD functions
  • Perform arithmetic with date data
  • Manipulate dates with the DATE functions

Hands-on-Exercise:

  • Create the distinction is made between single- row functions, which execute once for each
  • row in a dataset, and multiple-row functions, which execute once for all the rows in a data- set.
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  • Describe implicit and explicit data type conversion
  • Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
  • Nest multiple functions
  • Apply the NVL, NULLIF, and COALESCE functions to data
  • Use conditional IF THEN ELSE logic in a SELECT

Hands-on-Exercise:

  • we create and discuss the NVL function, which provides a mechanism to convert null values into more arithmetic-friendly data values.
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  • Use the aggregation functions in SELECT statements to produce meaningful reports
  • Divide the data into groups by using the GROUP BY clause
  • Exclude groups of date by using the HAVING clause

Hands-on-Exercise:

  • Group functions operate on aggregated data and return a single result per group.
  • These groups usually consist of zero or more rows of data.
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  • Write SELECT statements to access data from more than one table
  • View data that generally does not meet a join condition by using outer joins
  • Join a table by using a self-join
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  • Describe the types of problem that subqueries can solve
  • Define sub-queries
  • List the types of sub-queries

Hands-on-Exercise:

  • Write a query that uses subqueries in the column projection list.
  • Write single-row and multiple-row subqueries
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  • Describe the SET operators
  • Use a SET operator to combine multiple queries into a single query
  • Control the order of rows returned

Hands-on-exercise:

  • Create The queries in the compound query must return the same number of columns.
  • creating The corresponding columns must be of compatible data type.
  • creating The set operators have equal precedence and will be applied in the order they are specified.
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  • Describe each DML statement
  • Insert rows into a table
  • Change rows in a table by the UPDATE statement
  • Delete rows from a table with the DELETE statement
  • Save and discard changes with the COMMIT and ROLLBACK statements
  • Explain read consistency

Hands-on-exercise:

  • Expressions and create expose a vista of data manipulation possibilities through the interaction of arithmetic and character operators with column or literal data, or a combination of the two.
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  • Categorize the main database objects
  • Review the table structure
  • List the data types available for columns
  • Create a simple table
  • Decipher how constraints can be created at table creation
  • Describe how schema objects work
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  • Create a simple and complex view
  • Retrieve data from views
  • Create, maintain, and use sequences
  • Create and maintain indexes
  • Create private and public synonyms
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  • Differentiate system privileges from object privileges
  • Create Users
  • Grant System Privileges
  • Create and Grant Privileges to a Role
  • Change Your Password
  • Grant Object Privileges
  • How to pass on privileges?
  • Revoke Object Privileges

Hands-on-exercise:

  • create users and execute the
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  • Add, Modify and Drop a Column
  • Add, Drop and Defer a Constraint
  • How to enable and Disable a Constraint?
  • Create and Remove Indexes
  • Create a Function-Based Index
  • Perform Flashback Operations
  • Create an External Table by Using ORACLE_LOADER and by Using ORACLE_DATAPUMP
  • Query External Tables

Hands-on-exercise:

  • Create the function based index and types.
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  • Explain the data dictionary
  • Use the Dictionary Views
  • USER_OBJECTS and ALL_OBJECTS Views
  • Table and Column Information
  • Query the dictionary views for constraint information
  • Query the dictionary views for view, sequence, index, and synonym information
  • Add a comment to a table
  • Query the dictionary views for comment information
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  • Use Subqueries to Manipulate Data
  • Retrieve Data Using a Subquery as Source
  • Insert Using a Subquery as a Target
  • Usage of the WITH CHECK OPTION Keyword on DML Statements
  • List the types of Multitable INSERT Statements
  • Use Multitable INSERT Statements
  • Merge rows in a table
  • Track Changes in Data over a period of time
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  • Time Zones
  • CURRENT_DATE, CURRENT_TIMESTAMP, and LOCALTIMESTAMP
  • Compare Date and Time in a Session’s Time Zone
  • DBTIMEZONE and SESSIONTIMEZONE
  • Difference between DATE and TIMESTAMP
  • INTERVAL Data Types
  • Use EXTRACT, TZ_OFFSET, and FROM_TZ
  • Invoke TO_TIMESTAMP, TO_YMINTERVAL and TO_DSINTERVAL
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  • Multiple-Column Subqueries
  • Pairwise and Non Pairwise Comparison
  • Scalar Subquery Expressions
  • Solve problems with Correlated Subqueries
  • Update and Delete Rows Using Correlated Subqueries
  • The EXISTS and NOT EXISTS operators
  • Invoke the WITH clause
  • The Recursive WITH clause
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  • Use the Regular Expressions Functions and Conditions in SQL
  • Use Meta Characters with Regular Expressions
  • Perform a Basic Search using the REGEXP_LIKE function
  • Find patterns using the REGEXP_INSTR function
  • Extract Substrings using the REGEXP_SUBSTR function
  • Replace Patterns Using the REGEXP_REPLACE function
  • Usage of Sub-Expressions with Regular Expression Support
  • Implement the REGEXP_COUNT function

Hands-on-exercise:

  • Expressions and create the regular columns may be aliased using the AS keyword or by leaving a space between the column or expression and the alias. In this way, both wildcard symbols can be used as either specialized or regular characters in different segments of the same character string.
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Data Science Online Training & Certification Courses

Projects in Data Science Online Training

Board Game Review Prediction
Project 1

Board Game Review Prediction

Increase adoption and usability by making dashboards more dynamic and focused.

Credit Card Fraud Detection
Project 2

Credit Card Fraud Detection

Add business logic to improve the user experience.

Stock Market Clustering
Project 3

Stock Market Clustering

Create records, processes, and flows with Process Builder and Cloud Flow Designer.

Getting Started with Natural Language Processing
Project 4

Getting Started with Natural Language Processing

Create records, processes, and flows with Process Builder and Cloud Flow Designer.

 Obtaining Near State-of-the-Art Performance on Object Recognition
Project 5

Obtaining Near State-of-the-Art Performance on Object Recognition

Create records, processes, and flows with Process Builder and Cloud Flow Designer.

Image Super Resolution with the SRCNN
Project 6

Image Super Resolution with the SRCNN

Create records, processes, and flows with Process Builder and Cloud Flow Designer.

Career Success

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Data Science Online Training Course Certification Benefits

Data Science Training Course Certificate

Validate your mastery with the certification:-

We have the most in-depth and exhaustive Data Science Online Training. And the certificate you earn validates your achievement in this domain.

Set yourself apart from the crowd with our certification:-

Your experience with live projects, case studies and simulations after your Data Science Online Training Course certification with Besant will offer you an edge over other job seekers in the IT job market.

Let the world know about your mastery in Data Science:-

Share your certificate with your friends and professional circle through social media or just get it framed for display— show off your skills!

What's in this Data Science Online Course for you?

Skills Enhancements

Data science is one of todays top careers. Get the Data Science Online training with #1 Ranked Institute to Enchance your Skills such as data analysis, mining, SQL, Tableau, visualization, and big data, using tools like Excel, R, Hadoop, and Python. This Data Science Master Program will help you to Become Expert in machine-learning algorithms, data can offer insights, guide efficiency efforts, and inform predictions can be valuable skills in your career.

Career Progression

Transform your resume with Data Science Master's Program. For four years in a row, data scientist has been named the number one job in the India by Glassdoor. What’s more, the U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists. Explore Data Science Online Courses that you can complete at your pace from a Best Training Institute to Become Certified Data scientist

Average Salary

Data science is a field that combines domain knowledge, programming abilities, and mathematics and statistics knowledge to extract useful insights from data. According to the Job Sites Data Science Certified Professionals are more paid in India. Also the Salary also Depends on Employeers and Cities in India. We Recently Placed Our Candidates in Top Companies Like HCL, Genpact, Wipro, GAnalytics , IBM around 10Lacs. We List Data scientist Salary details City wise in India.
Mumbai: Rs.788,839 per annum | Chennai: Rs.784,403 per annum | Bangalore: Rs.948,488 per annum | Hyderabad: Rs.756,023 per annum | Pune: Rs.705,146 per annum | Kolkata: Rs. 502,978 per annum | Delhi: Rs. 511,978 per annum

Our Alumni Works At

Data Science Trainer Profile

Data Science Training Instructor

Instructor:Bala

Experience:7+

Specialist in: Data Science

Languages:English | Hindi | Tamil

Data Science Trainer

Bala has worked for 7 years with Besant Technologies as a Data Science Trainer, collaborating with many professionals from different backgrounds.

He is Not only the IBM Certified Trainer, but also a Developer and Technical Lead, who has been building life Applications for Small Business Using Machine Learning, Deep Learning, AI and NLP.

Trained and coached teams in data science and embracing a more agile mindset.

Implemented a common approach to discovering data products with a more empirical approach.

Provided ongoing coaching for the Data Science Teams, Agile Coaches and Product Owners.

He has delivered Data Science & AI Courses all around India, helping Organizations to improve their Productivity. Bala has worked for various companies including CTS, HP, Verizon, IBM and TCS.

Data Science Instructor Experience

  • Strong Experience in Decision Trees, Ensemble Models (Bagged Trees, Random Forest, Gradient Boosting a.k.a. GBM, Extra Gradient Boosting a.k.a. XGB, Stacking, Voting)
  • Strong Experience Object based learning and optimization techniques such as SVM, Neural Network and Logistic Regression and Linear Regression
  • Unsupervised learning techniques dimensional reduction (TSNE, PCA), outlier/anomaly detection(One-Class SVM, Isolation Forest), clustering(K-Means) and association.
  • Experience in data science projects implementation using deep learning technologies such as Keras, Tensor flow, Theano and Py-Torch.
  • Experience in data science projects development with Artificial Neural Networks(ANN) for Bank churn predictions.
  • Experience in data science projects development with Convolutional Neural Networks(CNN) for image recognition.
  • Experience in using Autoencoders for language embedding(NLP)Experience in using Boltzman Machine, Self Organizing Maps.
  • Developed model for identifying features in Face images using Open CV with Viola Jones and Haar-Cascading features.
  • Developed Computer Vison model for recognize images using OpenCV Single Shot Detection(SSD) algorithm.

Student Feedback for Data Science Online Training Course

The Data Science syllabus of Besant Technologies is quite immersive and covers every requisite topic. Also, the trainers who conducted the Online Data Science course sessions were well-rounded and presented each topic with real-time examples and usage. I am happy to say that I made the right choice by choosing Besant Technologies.

Diya

Diya

IT Professional

Earlier, I was working as a marketing manager. Although I had sufficient knowledge of R and Hadoop, I was reluctant to make a career transition. Later, when I decided to finally learn Data Science, I faced many issues with the online course providers. However, Besant Technologies is quite different from the rest as it not only covers the topics in detail but provides after-hour support as well. Interestingly, the best thing about them was the query resolution sessions, which I found engaging. I wish for the best of this exemplary Data Science institute!

Sharik

Sharik

IT Professional

Besant Technologies provides an excellent data scientist training program. After completing its course, I started my career as a junior Data Scientist in India’s leading company. Loads of thanks to their trainers.

Anitha

Anitha

IT Professional

Watch Our Placed Students who Completed Data Science Course Recently

Data Science Online Training Course FAQ's

The top companies to work for as a Data Science expert are:

  • Oracle
  • Amazon
  • JP Morgan Chase
  • Teradata
  • Accenture

Other good companies to work for are Wipro, Siemens Healthiness India, HDFC Ltd, Aditya Birla GDNA Cell, Larsen & Toubro Infotech etc. All of these companies offer the right platforms and initiatives to help their employees grow.

Besant Technologies offers 250+ IT training courses in more than 20+ branches all over India with 10+ years of Experienced Expert level Trainers.

  • Fully hands-on training
  • 30+ hours course duration
  • Industry expert faculties
  • Completed 1500+ batches
  • 100% job oriented training
  • Certification guidance
  • Own course materials
  • Resume editing
  • Interview preparation
  • Affordable fees structure

Earlier, statisticians had to manually create their predictive models and then adjust them repeatedly. This became difficult due to the increasing amount of data and more complex business problems. Data Science helped immensely in this regard.

Data Science is rapidly growing and involving automation. There is simply no sign of its slowing down. So, Data Science will be around for a long time considering the current data trends.

No, Besant charges no separate Data Science certification cost. The course fee that you pay has this cost included in it.

There are certain certifications that require you to appear for their respective exams in person. All others can be taken online. Hence, if you want to take a certification test remotely, there are numerous options that you can choose from.

No.Please do visit our website to know about Refunds Policy.

We accept all major kinds of payment options. Cash, Card(Master, Visa, and Maestro, etc), Net Banking and etc.

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